Property:Post-meeting notes
From Complex Time
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# Presentation highlights: (i) The role of constraints in phenotypic evolution as means of generating irreversible evolutionary endpoints and set upper limits to evolutionary trajectories. (ii) Role of constraints in species' ability to adapt to changing environments. (iii) Species come up against hard limits to phenotypic plasticity under climate warming. (iv) In order for thermal reaction norms to evolve in the face of climate warming, there has to be genetic variation. Unclear that reaction norms under strong biochemical control (e.g., development) have sufficient amounts of variation for the upper thermal limit to evolve in response to warming.
2. Open questions:
2.1 Connection between Darwinian adaptationist evolution and the idea of increase in disorder (as in the second law of thermodynamics)
2.2 What exactly are irreversible evolutionary endpoints? Can we come up with a specific definition of irreversibility?
2.3 Selection and constraints are not the same thing. This needs to be clarified.
3. How my perspective has changed: I want to think more carefully and deeply about the connection between Darwinian evolution and the second law of thermodynamics.
4. Reflections on other presentations
4.1 Stephen Proulx - I very much liked this presentation about the population genetics of low-probability transitions. I was particularly interested in stochastic selection due to lottery competition that leads to alternative stable states making it possible for mutations of large effect to cause transitions between states in a directional manner. I also liked the models of stochastic tunneling or valley crossing, that provide possible avenues for transitions between states. The case of multiple independent mutations enabling valley crossing is equally fascinating. I particularly liked how the examples shown related to the central theme of irreversibility and transitions.
4.2 Dervis Can Vural - An elegant presentation of the evolution of cooperation against the backdrop of fluid dynamics. I would like the theory to be generalized to perturbations other than shear so that it can also apply to pathogenic microbes within a host and other situations that do not involve fluid as a medium. I think you also should take the plunge and try to connect this theory to Hamilton's theory of kin selection. It is hard, and perhaps not analytically tractable, but it would be worth doing.
4.3. Samraat Pawar - I like the connection between metabolic constraints on species interactions and carbon fluxes.
4.4 Fernanda Valdovinos - The idea that adaptive foraging by mutualists (e.g., pollinators) allowing the persistence of nested mutualistic networks is a novel and exciting finding that pushes the field forward.
'''Annette Ostling:'''
Suppose that more similar species compete more directly - a very reasonable assumption given the reality of the fuzziness of defining species. Then an LV equation can have species parameterized by "traits." Different "niches" arise naturally in this model. A study of traits in a Panamanian forest suggests that species cluster in trait niches. ''Question'': the niche peaks tend to "repel" each other - is it worthwhile to think of individual peaks as species "quasiparticles" that interact with nearby quasiparticles through some generalized interaction, thereby giving rise to large scale, slow time dynamics?
'''Otto Cordero:'''
By creating nutrient beads and immersing them in seawater, colonization by bacteria can be studied in a controlled way. Genomic signatures can be mapped to particular strategies - degraders, cross-feeders, and cheaters. ''Question'': Does the geometry of the beads have an effect on the time behavior of populations? In other words, if instead of spheres, the bacteria were left to colonize tori or sheets, would there be any noticeable differences?
'''Priyanga Amaraserkare'''
Interested in when species are able to adapt to new environments. One take away is that species die much more quickly when introduced to high temperatures as compared to low temperatures, and that rate controlled processes have a different functional dependence than regulatory (as a function of temperature). Another very interesting point: it is very common for species from the tropics to invade more temperate climates, but it is much more unlikely for a temperate species from higher altitudes to invade a tropical environment. +
'''Darwin's arrow of time versus the 2nd law'''
Life on earth is subject to constant energy input from the sun. The 2nd law of thermodynamics, that entropy should increase, is for a closed system. So it seems it is not even really relevant for thinking about life on earth.
'''Definitions of irreversibility'''
One key definition of irreversibility we discussed is that if you reversed time the process would look strange—abiological. Can we make that definition more quantitative? Perhaps we mean simply that it would be going against changes predicted by the selective regime or expected population dynamics? Can we make that definition accommodate degrees of irreversibility, e.g. looking backwards involves changes less likely to happen? This fits in with what Priyanga talked about with adaptation to colder being easier than adaptation to warmer due to the shape of the relationship between the performance and temperature.
''BUT is this definition too broad?'' Any system with an equilibrium point is irreversible in this sense, because if you reversed a time series of it approaching its equilibrium it would not make physical/biological sense?
So do we really need to add something more to that definition, perhaps to include the idea that some environmental variable is being changed in time and we are watching the response to it, and asking if the system would go back if we changed the environment back? In that case our definition of irreversibility is the presence of hysteresis?
Is another definition of irreversibility that the system changes in a way that impacts its future potential changes or response to change in the environment? Or maybe this is just something often associated with irreversibility, as it is not the same as asking about a reversal of time, but instead whether there is path dependency in the system? Is this question of path dependency related to Gould’s question about whether replaying the tape of life would lead to the same outcome?
An example of this idea of the change in the system impacting potential response to future change is the case of competitive cluster formation (see below). If one assembles the community under one environmental filter, and then the environmental filter changes, community biomass may go down and never achieve what it was before or could have been under new environmental regime if assembled that way in the first place. The idea is that the change in environmental filter may not have be strong enough to overcome competitive footholds species have in the community.
'''Drift and selection, and is drift reversible?'''
Two key processes in ecology and evolution are drift and selection (among species or among alleles). Is drift in a sense a force creating more disorder? If so, we would think it would increase entropy in a sense and lead to irreversible changes? (Note there has been one paper by Sella and Hirsh in 2005 in PNAS trying to think about drift as something increasing entropy and more broadly a "free fitness" function like a "free energy" function, summarizing the role of selection and drift in the state of the population.) But we discussed it yesterday as reversible. Can we be more quantitative about why we think about it as reversible?
''Further, is drift really reversible?'' Drift can prevent a system from reaching another fitness peak, by causing loss of advantageous, neutral, or disadvantageous alleles when they are rare. (Recall the stochastic tunnelling examples Stephen Proulx talked about for how evolution may overcome this however.) So it can change the future possibilities for the system. It can also be involved in the somewhat irreversible process of competitive cluster formation (see below). A particular species may gain high abundance by chance (drift) and then have a stronger influence on the the competitive landscape for other species and become abundant in its cluster. Actually if there are no edges and no environmental filter, drift one of the two key ingredients in cluster formation (the other being initial conditions).
'''Key idea related to irreversibility and questions I raised in my talk'''
In my talk I highlighted that the formation of species clusters on trait axes under competition has some degree of irreversibility, in the sense that under strong competitive sorting, once a species dominates a particular cluster it is unlikely to loose its foothold. It would take a strong perturbation in species' abundances, or a change in which species are favored by the environment, to change which species would dominate in each cluster. Further, once certain species have gained a foothold in each cluster, this influence any subsequent assembly or evolution (selection, speciation, extinction) in the community.
The questions I posed about this particular phenomenon of irreversibility are:
1) How is the rate of competitive sorting, i.e. the strength of cluster formation, and hence degree of irreversibility, shaped by the mechanisms of competition? Do clusters emerge for all realistic competition mechanisms?
2) How will cluster formation depend on spatial scale, and how will this be influenced by the strength and scale of dispersal, relative to the scale of any heterogeneity involved in niche differentiation mechanisms?
3) Is the irreversibility of community pattern formation a particular concern for communities that may become isolated? These communities will experience extinction debt, and afterwards their resilience to environmental change may be low (the species that may be favored by the new environment may be gone).
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'''Day 1'''
<u>DPromislow:</u> Evolution shapes function and failure. Three dimensional space: Failure, Function and Evolution.
Main question: why do different agents age at different rates (faster, slower?)
<u>MHochberg:</u> Function criticality, aging and resilience. Coupling mechanisms of adaptation and aging. Wait, what are we referring to here for adaptation?
<u>James DeGregori</u>
Is it evolutionary Explain this! Oncogenic mutations in young healthy stem cell
populations typically reduce cellular fitness.
Peto's paradox: large and long-lived animals do not develop more
cancers.
Cancers requiring different numbers of driver mutations and
originating from vastly differently organized stem cell pools
demonstrate very similar age-dependent incidence.
Adaptive oncogenesis: we evolved stem cells that are well adapted to
the tissue niche. Stabilised selection for the evolved type. This is
more powerful than avoiding mutations. Stem cells would be adapting to
new environment.
q: does it mean that changing the tissue environment you would lead
stem cells (cancer) to evolved towards optimality in the "healthy"
environment?
q2: how about metastasis? do they evolve new-niche specific
variations? or physiological adaptations?
<u>Rozalyne Anderson</u>
Conserved pathways responsive to caloric restriction, across tissues.
Chronic low-level mitochondrial activation in cells. Mito regulator
PGC 1A (expressed to 1.5X, similarly to CR tissues). Super cool!
<u>Tulja:</u>
#Frailty and disability: transitions
#Post-reproduction lifespan
u(a, H) < u(a, S)
u: death rate
a: age
H: health
S: Sick
Recovery becomes harder as we age.
Decline in homeostasis with aging.
Potential well model (similar to fitness lanscapes)
Escape from potential well. Fluctuations depend on well depth, curvature of well and size of fluctuation. It's a multidimensional space.
Prob (H --> Ha)
Prob(S --> Sa)
As we age, the profile of the H and S well changes, making it harder for the ball to escape from the S well.
Longitudinal data on health, frailty, morbidity. HMM drive transition states. (Aging studies, Framingham, HRS, ...).
borrowing Hamilton's fitness (grandmother effect, old men, learning, transfers).
<u>Sabrina Spencer</u>
Causes and consequences of non-genetic heterogeneity.
How do cells switch back and forth between quiescence and
proliferation. Shift from G1 towards G0 depends on CDK2 (off). She develops awesome trackers for CDK2. Wow!
Bifurcation in CDK2 activity appears in many cell types.
p21 inhibits CDK2. p21 -\- remove the bifurcation, leading to proliferation
only, no quiescence. Many quiescent cells have DNA lesions. Mothers of
quiescent daughters have a longer cell cycle. Daughter quiescence is
decided in the mother's G2 phase. Mother cells pass DNA damage to
their offspring cells (i.e. do not retain the damage).
CDK2low state fortifies cell lineage against stress.
<u>Barbara Natterson-Horowitz</u>
Cardiovascular disease in humans, non-human primates, lions sympathetic and parasympathetic system.
Sudden cardiac death (SCD), "Tahatsubo" cardiomyopathy.
High adrenergic events. Sympathetic responses are flight and fight. Parasympathetic responses are opposite: faint, shit yourself, etc.
Fainting: vasovagal syncope (VVS), due to underprofusion of the brain.
VVS: paradoxical bradycardia has developmental characteristics: depends by the age of first fainting.
Alarm bradycardia is preferentially a juvenile phenotype.
Early life events: shaping autonomic nervous system for later events.
'''Day 2'''
**Intro by Michael Hochberg
- Basins of attraction - Failure, gradual, rapid, can we predict it - What is failure, underperformance or misperformance - Tradeoffs and pleiotropy - Networks - not discrete comparts - Complexity vs. simplicity - Henry Ford Aging and senescence, time and disfunction. SSpencer: Immortality, regeneration and rejuvenation. DPromislow: Michael Rose's concept of age-depedent decline in fitness components.
**Morgan Levine
Geroscience. Hallmarks of aging that accumulate over time. Systems biology of aging, changes ion the moleucular level propagate through the networks. Preservation of function (paper with Luigi Ferrucci in Circulation research). Aging heterogeneity, chronological age is an imperfect proxy of the latent concept, biological aging. Epigenetic clocks. Chronological age has been shown correspond with distinct chagnes in DNA metylation at specific CpG sites. Horvath's and Hannum's clocks. These clocks are done using supervised machine learning. She wants to capture the true residuals, rather than minimizing the residuals. Understand whether those residuals tell you anything about the underlying biology. Getting more biology than just chronological age from the clocks. Phenotypic age, predictor of aging-related mortality based on clinical measures. The used markers are albumin, creatinine, glucose, c-reactive proteins, lymphocyte percent, mean cell volume, red cell ditribution width, alkaline phosphatase, white blood cell count, age. Consensus co-methylation networks - data from 6 different tissues. 14 modules, showing age correlation for each CpG. Which piece of the clock matches what tissue, etc. Diseaseome map.
**David Schneider (Stanford)
Resilience and disease space. How sick are you going to get with a given load of pathogens? Symptoms = f(microbe load). It's a disease tolerance curve. However, tolerance is a population measure and does not apply to individuals. How do we expect disease dynamics to vary? All models are wrong, but some models are useful (George Box). [[https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002436][Tracking Resilience to Infection by Mapping Disease Space]]. Using a range of mouse diversity to identify imporant regulatory mechanisms. 8 parental mice used to generatel RIL. He brings up that methylation clocks are actually calendars, not clocks.
**Marten Scheffer
How ecologists have been looking at resilience. Resilience: the capacity to get your acts together, to recover after damage. How quickly things come back to normal after perturbation. Sometimes things do not recover from perturbation and does not come back. Another way to think about it is that the system has become brittle. Unstable equilibrium. Theory developed in the 1960s, catastrophe theory. Salvador Dali and his own tipping point. Holling in 1973 thought about resilience. How to know a stability landscape. Tipping elements in the human intestinal ecosystem. Generic early warning signals = indicators of resilience. Critical slowing down. Dinamic indicators of resilience (DIOR). A bird shits on your head. After a hour you're ok, it means you're resilient. If after a hour you still hungry, you're not resilient, you may be depressed. This is based on patterns of micro-recovery. Fast recovery, slow recovery. Temporal autocorrelation help you predict tipping points. The previous arrow of time group has looked more at the complexity. This group is more looking at the molecular side.
**DPromislow
Common dynamical phenomena underlying description of the causes of failure. What are those communalities? This can help us get to the causes and dynamics of failure. This could be an argument of discussion. Group discussions to address communalities and differences in failure.
* Group Discussion 1
Can we reverse, halt, or slow down aging or is aging inevitable
- which components? Epidermis.
- ways to slow aging: CR
Is aging inevitable? Entropy. Does entropy at individual level
increase, causing aging? Individuals are open systems.
What are the components of aging?
How do we distinguish between intrinsic and extrinsic causes of aging?
Intrinsic: failure in DNA repair, proteostasis.
Extrinsic: falling down, UV light
Do you really return to the same initial state after a perturbation,
or are you missing a dimension.
The landscape that David Schneider showed may change as aging.
progresses. Contours over which these things occur may be morphing.
'''Day 3'''
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'''Impact of the meeting on my research'''
The main thing I took away from the meeting was that robustness to damage is property of neural networks that is important from a theoretical as well as an applied perspective. I'm motivated now to think about revisiting the old method of lesioning and perturbing with noise neural network models, so as to better understand the degree to which they are sensitive to local damage and to displacement of their dynamic trajectories.
I would also like to explore how robustness to damage and noise relates to robustness to adversarial attacks and to robust generalization performance in novel situations (i.e. changes to stimuli or more generally the behavior of the environment). Before this meeting, I would have conceptualized these different forms of robustness as largely unrelated. I would have thought that the commonality suggested by the fact that they can all be considered forms of robustness is largely misleading.
Now I think there may be deep links between robustness to damage, internal noise, changes of the body, and changes of the stimuli and environmental behavior.
Some half-formed hypotheses:
* Neural noise may help a neural network learn solutions that are robust in a variety of ways.
* Similarly, changes of environmental behavior (including stimulus statistics) during learning may help a neural network learn solutions that are robust in multiple ways.
* Predictive completion of partial neural representations (in symmetric, i.e. energy-based, or asymmetric networks) may provide robustness through redundant representation as well as enabling unsupervised learning through selfsupervision.
I'm quite keen to explore these ideas further.
'''Important open questions'''
• How can states close to criticality serve computation in neural networks?
• Might robustness to damage result from the same mechanism that enables robust generalization to new domains? (And what is that mechanism?)
'''Notes on the meeting'''
The discussion, though inspiring, was a little more wide-ranging than is optimal for making concrete progress.
It might good in future meetings to focus, and to more clearly and specifically define the topics of sessions and particular presentations.
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'''Most useful thing I've learned''': The idea of outlining several causal models and the predictions that they can make about a certain phenomenon of interest, to then test which one fits the data better. This type of deductive reasoning is, I'd say, underused in my field. I'd like to think again about the causal models for fertility and to review potential causal models for epidemiologic transitions. However, at the same time, the conclusion from the lecture (and the paper) on these causal models for fertility ended up being about the importance of considering all models simultaneously, or, even better, considering how they have a differential effect over time. That is, some of the models may have an importance in the beginning of the fertility decline while some others may be more important in the later stages. I think we need to leverage the idea of feedback loops and endogeneity in complex systems to better accommodate the presence of multiple causal models.
'''Planning to use knowledge''': I plan to use the framework on migration determinants, especially at the macro level, and to leverage some of the data sources that were mentioned in the migration lecture. In particular, I'd like to see the connections between internal migration (rural to urban) in the determination of urban health outcomes. I'd also like to incorporate some of the lessons about causal models, but applying them to epidemioloic transitions.
'''Interesting conversation''':
* On the necessity to consider endogeneity in the causes of population dynamics. X affects Y than in turn affects X again.
* On the necessity to consider inequalities and distributional effects +
'''Most useful thing learned in course:'''
For me, the most useful about this course was seeing more examples of and talking through the process of moving from 1) establishing research questions to 2) identifying the key variables to 3) developing the equations to relate the variables to each other to 4) developing models of how the systems work. For me, steps 3 and 4 have always been the most difficult and are currently the steps I am thinking through for my dissertation. Getting to talk with other researchers about this process and seeing how they developed equations and models to capture and depict their topics of interest has helped me make some inroads to get started on this process in my own research.
'''Some additional useful things learned in the course:'''
-The importance of always being aware of and periodically re-assessing/re-identifying the interplay between 1) models/abstractions of data patterns and 2) on the ground interviews and data collection that reveal the key variables/driving factors and logics/frameworks of the subjects that contribute to the observed data patterns (such as in Caroline Bledsoe’s . Without this ground-truthing and finding out what variables actually matter for the study organisms and how they conceptualize them, our models will be flawed due to either missing key variables or not being able to actually explain the implications of the patterns they show.
-The importance of considering time lengths/durations in a process. For example, as we saw in Mary Shenk’s lecture on demographic transitions, the same pattern (e.g. declining fertility rate), but over different time scales (e.g. a longer time in one area than another), can have significantly different impacts (e.g. amount of population in each of the two different areas). Another example from this workshop was thinking about what is the temporal resolution of our data and how this aligns with the temporal resolution of the variable of interest (such as Lori Hunter discussed in terms of census data and temporary migrations).
-What models to use for particular demographic problems (and how to accommodate variation and additional parameters)
'''Applications for my dissertation research:'''
As I’m building models and thinking of variables to consider for my dissertation, which deals with agricultural adaptations in light of socioeconomic changes in the indigenous American Southwest, this workshop will be extremely helpful (particularly Chris Kempes’s work on the land/resources needed to sustain a given population and Charlotte Lee’s model integrating environment, population, and society).
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'''Pamela Martinez'''
Connection between population dynamics and strain diversity. What is the response to pathogen intervention? (clear in a Chessonian framework, unclear when there is strong strain diversity). 1) Rotavirus. Robust antigenic diversity. Estimate of the parameters from cases: G-type strong specific immune response, P-type strong general immune response (concern: very few data, enough statistical power). Surprising: no effect of vaccine/intervention. 2) S. pneumonia. Frequency of strains. How does frequency change after intervention? Replicator model, predicted fitness from genes (how? Not clear): can predict frequencies after intervation (which again does not work). Questions: 1) what determines the equilibrium frequencies of genes? Why loci are under negative frequency negative dependent selection?
'''Annette Ostling'''
Stochastic open competitive communities. Niche/unique opportunities/deterministic forces vs chance/neutral/stochastic forces. Finding niche differences is a challenge. Stochastic Lotka-Volterra on 1dim niche axis . Cluster emerge. 1) Metric. What is a good measure of clustering and how is that affected by different mechanisms? 2) Data. BCI and regional pool.
'''Greg Dwyer'''
Gipsy moth & virus/fungi. Inference of parameters from time-series. Key ingredient: variability of susceptibility between individuals. Works in reproducing data (but it is hard to predict)
'''Otto Cordero'''
Hyper-diversity of microbial communities. How do we interpret that diversity? Patches of organic matters (~detritus) are hotspots of diversity: multiple species are recruited. Lab experiment: biopolymers, 100 species. -Omics + culturing + phenotyping in order to recostruct dynamics. Observed successions (are true successions? Can also be explained by variability in growth/dilution rate). How succession depends on niche-breadth? Idea: niche breadth is bimodal (small niche breadth: early colonizers, specialized. Large niche breadth: late colonizers, cannot be grown in culture). He then looks at the metabolic networks: close to the central metabolism everything is more homogeneous, close to the periphery everything is more heterogenous (high variability in copy number). He then plots #of chitinases vs the # of coevolved modules. Early colonizers are high in both. Late colonizers are low in both. Free loaders have high # of coevolved modules but low #chitinases (can eat what free riders eat). Ecological dyanmics is consistent with this classification (degraders grow initially and then decay, cheaters and cross-feeders grow later)
'''Robert Marsland'''
Two facts: A) Microbiome is very diverse. B) Taxa is unstable, function is stable (robust patterns). Assumptions: 1) classical ecological models are inadequate for understanding ecosystems 2) large / diverse ecosystems are typically random. (Random) Chemostat model predicts many patterns. Why random works well? Above stability threshold random works very well.
'''Priyanga Amarasekare'''
1) Phenotypic traits are the interface between organisms and the environment 2) Evolution: (mutation+)contraints+selection 3) Irreversible processes arise from contraints. Different types of contraints: genetic (evolution acts only on heritable traits), energetic (tradeoffs), morphological (upper limits to evolutionary trajectories). Rest of the talk on how different traits depend on environment (Temperature). Two mechanism for reaction (to T variaton) norm: enzyme activation (monotonic response, e.g. mortality) and regulatory/hormonal (unimodal shape, e.g. eggs maturation) -> conserved across taxa.
'''Fernanda Valdovinos'''
'''Samraat Pawar'''
'''Stephen Proulx'''
'''Dervis Can Vural'''
'''OPEN DISCUSSION'''. 3 themes/questions
[[File:OpenDiscussion.jpg|center|450px]]
<u>1) Identify quantities that are always increading or decreasing.</u>
(side questions: why? At what scale (temporal, spatial, ...) do they have that trend?
Possible examples (in a Markov chain): return time (as a measure of irreversibility), turnover time, how often the system returns to the original state, fraction of trajectories that go from A->B vs from B->A.
Possible examples (interpretable quantities): # of species, biomass, # of limiting resources, # of niches, properties of resource consumption (e.g. efficiency), degree of specialization, interdependence, major (evolutionary) transitions
[[File:Question1- quantities to measure.jpg|center|450px]]
<u>2) List of properties that we expect to be reversible/irreversible</u>
Irreversible: oxygenation event (major transition), increase of specialization, latitudinal gradients, adaptive radiation, cluster formation
Debated: drift, mass exctintion (in what sense they are irreversible)
Reversible: function(?), total biomass (?)
[[File:Question2b.jpg|center|450px]]
<u>3) (relevance of) transients</u>
It depends on the timescale of evolution vs population dynamics.
Relevance of timescales: timescale of evolution, environmental change, behavior vs timescale of population/community dynamics
Relevant examples: patterns of extintions (position in transient determines outcomes), cycles ("always" in a transient)
[[File:Question2.jpg|center|450px]]
'''Pamela Martinez:'''
Very interesting research on how strain diversity can affect disease spread. I learned a lot about best practices on how feetting models to data because of the discussion this work provoked.
'''Annette Ostling:'''
Impressive work of testing the prediction of a competitive model of plant species with empirical data. The authors found that clustering happens in tropical forests due to niche partitioning. I learned much on different competitive models.
'''Greg Dwyer:'''
This talk was very useful for me to understand ways in which empirical data and models can interplay to make concrete predictions that can inform management. The applied case of informing agencies when to spray the forest with virus to stop the tree disease was very illuminating.
'''Otto Cordero:'''
Interesting application of ecological theory to microbial communities. I really enjoyed the way the speaker identified biological mechanisms in his empirical system and was able to connect the modeled dynamics to those empirically tested mechanisms.
'''Priyanga Amarasekare:'''
This talk made me think in a deeper way about constraints on phenotipic/genotipic variation that can help us understand how ecological system may respond to human perturbations such as climate change.
'''Fernanda Valdovinos (my talk):'''
It was extremely helpful for my research the in depth discussion that the audience provoked on the details of my model. The dissecting questions I received on my equations and their consequences were very illuminating. I will definitely use some of the new understanding I acquired trough answering those questions in the paper I'm currently working on. I also really appreciate the philosophical question that Greg asked me over the break and Jacopo helped to answer. That question was about what are we actually learning by using a network approach instead of just many differential equations as we have been doing for years in ecology prior networks. I would really like to further discuss this question as a group tomorrow.
'''Stephen Proulx:'''
Amazing talk that helped me better understand adaptive dynamics, how we can read mutation/invasion maps and how to make better use/understanding of fitness landscapes. It was fascinating to me the trade-off example on plant fertility-survival that showed a clear case in which small vs large mutations can drive the genotype/phenotype of plants to different attractors. I also liked a lot one of the speakers questions on how to produce general theory from non-equilibrium cases and the discussion that question provoked.
'''Questions from my talk:'''
True or false?
1. Classical ecological models are inadequate for understanding microbial ecosystems.
2. The large-scale, reproducible patterns we see across microbiomes are emergent features of “typical random ecosystems.”
3. Diverse communities will almost always behave like “random ecosystems.”
'''Question I want to discuss'''
Can large-scale ecological changes over time be understood through simple general principles? (Samraat, Jacopo, Priyanga, me: flux balance/metabolic rates, random matrix theory, biochemical and morphological constraints)
Are there ecological summary statistics that change monotonically over time? (Jacopo, and some of my work that I didn't talk about -- see references)
Which properties of individual organisms are essential for predicting large-scale ecological changes?
- Pamela -- why do some vaccines fail to produce large-scale ecological change (i.e., pathogen extinction)?
- Greg -- importance of distribution of susceptibilities to dynamics of epizootic onset.
- Fernanda -- importance of adaptive foraging for understanding why pollinator systems don't collapse -- and why they might under new circumstances.
- Samraat -- taking the mechanics of inter-species interaction seriously, understanding how they affect response of ecosystem to temperature changes.
- Priyanga -- asymmetry of reaction norms for temperature variation.
What do we miss when we focus too much on equilibrium/steady states? (Prevalence of cyclic disturbance/recovery dynamics -- Greg, Samraat) +
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'''SFI WG Big Unanswered Sleep Questions'''
1. What is sleep? Behavioral definition? Phenomenological/experiential definition? EEG/physiological definition? Which metrics—fitbit, online, over what time span, Etc.? How closely matched in terms of time of onset? How closely matched in terms of functional or health effects? How do we separate sleep ability from sleep need? Do we need a species-specific definition and can we come up with a very general definition that abstractly covers everything? How strict should we be with definition of REM and SWS by voltage or time-scale and frequency characteristics across ages versus qualitatively similar behaviors? What are the building blocks of sleep? When do definitions fail? What breaks these definitions? Which questions correspond to which definitions? Can we make that mapping? Is there a universal rescaling of EEG or something else where sleep does look the same across species, life stages, etc?
2. What are the functions of sleep? Can we construct a phylogenetic tree of life and developmental stage tree of life for the function and features of sleep? Are all about evolutionary/energy optimization of processes that don’t happen as well during waking? Restoring functions vs learning functions vs other
a. Feel rested/cure sleepiness
b. Performance of tasks
c. Emotional regulation
d. Metabolic function
e. Maintenance of brain and/or body
f. Immobility during dangerous times for predation etc.
g. Memory and information processing
h. Energy conservation, adaptive inactivity
i. Immune
j. Inflammation (molecular) and pain (behavior/perception)
k. Endocrine function
l. Growth (SWS)
m. Protein homeostasis
n. Endogenous sensory input with higher signal/noise than otherwise for neonates
o. Maintain mitochondrial function
p. CSF/glymphatics clearance
q. Synaptic homeostasis
r. Breathing and cardiovascular regulation
s. Sleep cycling for vigilance to monitor fires, danger, etc.
t. Appetite control
u. Increased/decreased predation
v. Allergies
w. Kidney function
x. To dream
Consequences of sleep. These would be costs/tradeoffs in an optimization function
a. Reduced alertness
b. Reduced foraging
c. Reduced social contacts
d. Reduced activity
e. Fasting
f. Reduced information gathering
3. Does evolutionary origin for function of sleep correspond to current functions of sleep? Is one sleep function dominant and the rest subservient or piggybacking? Are all on equal footing now? How big is the selection pressure in terms of evolutionary theory and measurement? Is there a group-level selection for sleep? What is first common ancestor of sleep? Circadian rhythm came before “sleep”.
4. Do different functions compete for time? Which function has longest time constant, which is distinct from which is most important? How does this change with life stage?
5. Are there different amounts of sleep time required for different functions of sleep? IF sleep is limited, are these different functions competing for the available time? If you restrict sleep, what is the ordering and magnitude of loss of different functions? Much known for learning and memory but what about other functions?
6. What are the characteristic time scales of sleep and what sets them? Which ones are invariant, and which ones change across size, species, developmental stage, brain region, task, etc.? What data already exist for this? What were our models predict? If you take differential equations models and parameterize by correct time constants (based on scaling of neuronal scaling and axon conduction velocities or based on physiological/allometric scaling or both), as you tune them what dynamical shifts or phase transitions do you see? Why does a 90 minute nap help more with learning tasks than 5 hours of sleep? Go from fastest (ripples) all the way up to long timescales. Can we list characteristics timescales for each function above?
7. Do we mean mechanism or function? How are these all reconciled? All equivalent?
8. If sleep is for repair, does whole body repair work differently or over different time scale than just for brain? Immune response, wound healing, and bone density connection to sleep and circadian rhythm? How to quantify?
9. Does brain “saturation” cause sleep? Global saturation (no?) or local saturation? How are local limits set and what sets partition of space for each category, say physics versus French literature? Seems like it should be experimentally measurable/testable now. What exactly needs to be measured? Why don’t we do it?
10. Do we need and can we get better developmental sleep data?
11. How do we include effects of temperature in sleep models? Via metabolism? Via loss of thermoregulation in sleep, hibernation, torpor in birds? What about sleep changes in total time, %REM, sleep cycle, etc across body temperatures in ectotherms, even for similarly sized organisms.
12. Is dreaming a way for brain to devise better computational algorithms for exploring large dimensional data or parameter space for theory? Why does this require sleep? Is it because it isn’t accessible to our conscious brain because done by some other part of the brain and takes lots of energy so have to save energy from elsewhere? If it’s so useful, why do we forget dreams? Maybe because their actual content isn’t useful? Maybe if you remember too well, you start to confuse dreams with reality and that becomes big problem? Is there some wake correlate or default state or meditation or exercise in which something similar can occur?
13. What are the individual and Societal health implications of all the above?
14. Does sleep time change with city/community/social network/college size?
15. How can we best leverage large digital datasets? Can we align 23andme with fitbit and other data?
16. Can we develop a quantitative, mathematical, predictive, universal theory of all this? What would it take to do this in terms of data, assumptions, mathematical and computational tools?
17. How can we capture the heterogeneity of the data? Deviations from universal or described on its own terms or correlates and cross-effect for different features. Can we isolate which factors impact sleep? Gender, genome, SES, race/ethnicitiy, age, etc. Higher-order interaction effects.
18. How do recent experimental tools enable the answering of the questions above?
'''What/Who are we missing?'''
Ken Wright UC Boulder Metabolism and Microbiome
Eve Van Cauter U of Chicago Metabolism and Immune function
David Paydafar UT Austin Engineering Mathematical Biologist and modeling of SIDS
Rosemary Braun Northwestern Statistical Analysis of Circadian Phase from 1 or 2 samples
Phyllis Zee Northwestern Sleep in elderly (bug picture thinker/ great at networking)
Michael Perlis U Penn Insomnia and suicide
Daniel Buysse Sleep health
Sonia Ancoli-Israel UCSD sleep and chemo
Laura Lewis Harvard Clearance of CSF during sleep
Monica Haack Harvard Immunology/Inflammation/Sex differences
Mark Opp UC Boulder
More traditional Evolutionary Biologist
Book audience more towards industry, Gates foundation, funding agencies, scientists, CDC,
Present more scientific basis and evidence-based approach to importance of sleep and understanding it and its consequences.
Mostly about basic science questions we’ve discussed throughout but with bullets at end of each chapter about implications and a chapter or two at end that is more focused on implications for health and policy.
Make more focused in terms of modeling and timescales of sleep as theme of book.
Data generated at many timescales, timescales can be organizing principle but must reconcile and synthesize them, modeling needed to reconcile and synthesize these timescales, this process actually unique vantage point for answering and asking big questions about sleep.
Help bring model-resistant scientists to understand importance of modeling.
Let’s be provocative and say we can try to model dreaming!
How does a brain calculate at the neuronal level which memories are valuable?
How do we model sleep across timescales?
Connecting machine-learning to modeling approach to make it better
Importance of working and talking across disciplines
How do a mapping from questions to appropriate scales. Hard to do it as just individual group or lab.
'''Talk Notes:'''
'''Susan Sara:''' Locus Coerelus, PreFrontal Cortex, Sleep Spindles all increase about 2 hours after leaning task in rats. Higher LC firing rates even during Slow Wave Sleep, suggesting important learning and consolidation happening during sleep. LC is silent during REM sleep. Much more firing together during SWS than during wake. What sets time scales of 2 hours? It is task dependent because some take and hour. Does characteristic time scale change with species or brain size? What sets this time scale? Does it change across development? What is theoretical expectation? Any data? How to disentangle age effect from increasing difficulty of task being learned. Must be careful. Coordination and coupling of spindles and ripples. Which is first? How are coordinated? Which is functionally more important? Temporal relationships. Can replay happen without ripples? Replay in visual cortex is not related to ripples. Not clear ripples and reply are causative for memory. Maybe it’s the subsequent ripple-spindling coupling that’s disrupted instead of the present one.
'''Gina Poe:''' Norepinephrine at Beta Adinergic receptor is to increase/encourage long-term potentiation. Slow Wave Sleep goes aing with absence of Acetochioline even in unihemispheric sleep in seals. Define sleep as a behavioral state but maybe that’s hard because of dissociated states. Maybe sleep must be defined as functional question to ask what’s needed or what counts. Is millisecond sleep enough to do something functionally? Lack of norepinephrine is required to erase memory so erasure can only happen in sleep. And targeted erasure (meaning targeting memory) can only happen in REM sleep thru depotentiation. Takes about 5 days or learning mazes for consolidation and reversal of learning. Again what sets this time scale across task, species, development, or amount of brain involved? What tags or systematically decides what information you want to remember and what you want to forget? Firing rate lowest in REM sleep. Hard to see sleep in insects etc. because electric signals cancel out because not layered or striated or organized in way that gives clear signals.
Is familiarity versus episodic memory two separate states or is it a continuum? Familiarity might be the feedback to the hippocampus that this can be eraser. Seems like two separate states. Why do we want to forget? Storage limitation, not useful, scary/traumatic, updating of previous thought like Santa Claus, etc. Danger of seizure from overload or saturation but seems like little evidence for this. Maybe saturation is only at the local circuit level. Flips questions around to ask why do we partition brain memory in way we do and why we do give this chunk of memory to this specific task. Nobody has looked at this question experimentally but could be done now. Maybe not really forgetting anyway but more like downweighting certain memories compared with others. Very modular structure and organization to cholinergic and adinergic systems. Perhaps people with super memories have less redundancy of memory. Would they forget more in old age? Is this observed?
'''Sara Aton:''' Foot shock creates fear memory that isn’t consolidated if transcription/translation is disrupted, neuron firing is blocked, or sleep is not allowed. Increase in theta and ripples (so oscillations in general) after learning event across sleep. If you induce similar pace (7 Hz) oscillations you recover consolidation even without sleep. Not clear of side effects of what happens to other functions of sleep if this is all you do. Use of optogenetic tools to do cool experiments. Able to make it so there is fear response but without awareness of what they’re afraid of. Correct time scales of rhythms are main thing needed for appropriate spike timing and that happens during sleeps and that’s why sleep is needed for memory consolidation even if that’s not primary function (although it could be). Is the 7Hz timescale task dependence, species dependent, developmental dependent, etc? Going from rate code to phase code to renormalized rate code and strengthening connections to propagate information through circuit. Thinks 7Hz is circuit dependent and circuit frequency is tag in that way that depends on wiring. Like a resonance frequency. She thinks phase matters but not frequency but not clear. Could you decipher or reverse engineer these codes to read out dreams or other things that are happening. Seems like there are experimental tools where we could start to do that. Circuit itself and post-synaptic partners are themselves making decision of what to remember or forget. Is certain cells can’t keep they are in some sense filtered (probably literally) out? Could at least ask this question theoretical and see what happens. Does lag time correspond to spindles?
Slow oscillations are 0-1 Hz. Delta is 1-4 Hz. Slow oscillations go much deeper that allows for calcium rebound. Slow oscillations are more global and Delta are more local. Even individual cortical column or relay neuron can exhibit Delta by itself. Spindles are 10-15Hz layered on top of these other waves. Ripples are 80-200Hz on top of spindles. Gamma are 40-80 Hz and occur while awake. Only 15% of spindles coupled to oscillations even though learning relates to total number of spindles in sleep. Seems like paradox. 200mV for waves. Most power is in slow waves. How much of the brain’s metabolic rate does this take? A lot or a little? 1/f power happening for this. Measures of fractal dimensions of these time series? Do follow a power law. These are like temporal correlations. What about spatial correlations (using calcium imaging) or overlap in temporal and spatial correlations? What percentage neurons contribute to this? About 5% of hippocampal cells involved in a learning task and memory. 30% have place fields. About 20% decrease in glucose and oxygen consumption during sleep. About 37% decrease for humans in NREM. Would these timescales change across species. Similar in mice, rats, and humans. Calcium imaging could say more.
'''Kimberley Whitehead:''' Looks at spindle bursts that’s different from sleep spindles because also happen in wakefulness. People are now claiming to see spindles in adult. Maybe these two things aren’t that different and we just haven’t realized that yet. Help refine sensoricortical maps. If you prevent bursting, barrel cortex doesn’t get organized properly. Sleep-wake state on the scale of minutes for humans. Pre-term baby has loads of delta in all their sleep. Higher power in pre-term. Role of nREM becomes more important as you grow. Maybe phasic and tonic REM sleep have different roles. Sub-sleep state in scales of tens of seconds. Active sleep movement on scale of seconds. Sleep oscillations important for sensory cortical organization in gestation and early development. Log normal for frequency of wake bouts and active sleep bouts. Role of benefits of wakefulness versus benefits of sleep and which are more important for survival and selection. Sensory experience more intense during sleep than during wakefulness so reverse of sleep model for information input proposed in paper (Cao, Herman, Poe, West, Savage). Wakefulness set more by time to birth than time to gestation age. Why not plot versus body weight instead of age? Age explains more of the variance. It’s experience dependence response and processing which seems like the generic definition of learning, not just laying the substrate for it. Surge to breathe, arousal, etc at birth so that’s enough to switch dynamics of wakefulness and sleep.
'''Elizabeth Klerman:''' Sleep-wake cycle and circadian cycle Homeostatic cycle and circadian cycle must be coupled. Must desynchronize two clocks to decipher effects of each one. Homeostatic is need for sleep overall and not just circadian. Clocks shift for 25 or 26 hour timing. Scored in 30 second chunks of sleep or wakefulness. Organized around core body temperature minimum that typically occurs a couple of hours before you wake up. No circadian rhythm in SWS but there is in REM sleep. Not understood physiologically why that is? Tononi’s model is that SWS reverse buildup of LTP. SWS percentage is same regardless of sleep debt so total SWS decreases. Related I think to questions about %REM and %NREM across development and across species. Remember to ask this question during my own talk. Drive to wake strongest right before you go to sleep and drive to sleep is strongest right before you wake up. Like hanging on by your fingertips but need some force you’re fighting against then. Needed to help you consolidate sleep is hypothesis. Humans can know time of day even as seasons change by adjusting melatonin levels up and down. People used to be seasonally reproductive. Children and adolescents have much faster buildup of sleep pressure than older people. People who are out in bright sunlight during day aren’t as affected by devices at night. So something to do with change in magnitude of light, but just current amount of light. Different models needed for short exposure to light and effects on phase delay. Additional process coupled to process L or some type of modification of process L. How do go from average- or group-level models to more individual-specific models? How does caffeine or other drugs affect it? How does aging, disease, mental disorders, etc. affect it? What is physiological analogue of the two variables for the phenomenological model? Some amalgam of the physiological factors? Maybe different amounts of sleep are needed for different functions of sleep? Maybe they are competing ofr the time they get?
'''Cecilia Diniz Behn''': Sleep depends nonlinearly on amount of sleep deprivation and timing in circadian clock. Characteristic time scales affect shape of circadian waveform that affects timing of sleep and wake. Can we get a more physiological perspective and include that in modeling framework. Network between SCN, Wake, REM, and NREM. Choose scale at which network is specified, ranging from neurons to whole-brain regions or functions. How do you choose scale and how that affect choice of math methods. Depends on what questions you’re asking as to what scale you should choose. Time constants of homeostatic sleep for humans can be tuned to work. For rats homeostatic sleep build up too quickly so can’t compensate with circadian clock. Shows importance of time constants and that it can really give different effects and conclusions depending on what you think or use here. Could be order-of-magnitude difference between rats and humans. Populations of neurons can shift clock and likely mediated by electrical activity and firing in SCN. Can use Hodgkin-Huxley type models here. Circadian rhythm plays a role in sleep timing but not the ultimate clock! Feedback with other sleep clocks!
'''Marishka Brown:''' It is a mandated program but funding is not mandated. Largely instigated with push from circadian researchers. Trans-NIH sleep coordinating committee to build partners across all the different parts of NIH. Starting to build with NIAID. Studies on fatigue and sleep and performance joined together for studies. Connecting also with Human Health and Services. Circadian, disease, development, understanding are big parts of funded NIH sleep proposals. Turning discovery into health is motto. We scientists need to communicate better with those who fund us and general public to explain why basic science is so important to even be able to have something to translate into clinical applications. Need to do a better job to verbalize priorities to get funding behind us. Sleep disturbance is one of the best predictors of suicide. Lots of effects on heart and other functions. Correlation between sleep deficit and every type of risky behavior of adolescents. Public is convinced about sleep, just not academic medicine. Sleep and circadian rhythms are fundamental to health and life. How does circadian timing (NIAID) and sleep restriction affect immune response and vaccinations? Funding opportunity now. Ideas about chronotherapy. Does timing of organ donor for transplant need to match circadian timing of recipient or does it not matter? How does sleep deprivation affect it? What about sleep and health disparities compared with woman, URM, low SES, rural communities, etc. Why do these differences exist? (Not just cardio event in AA adults.) DOD strongly aware of importance of sleep and funding for research for it. Big upsurge in funding over last 3 years. Funding connections with cancer are expected to grow stronger. Silos of sleep research based on citation networks. NIH wants to break that down and create more communication.
'''Victoria Booth:''' Shortest time scales are bouts of minutes. (In principle, could this go down to even milliseconds because memories can form in that small of a time.) Wake durations are power law and sleep durations are exponential. Wake durations seem to follow Zipf’s law. Sleep duration follows random Poisson process like radioactive decay. More fragmentation in sleep bouts happens towards the end of the night. How strict should we be with definition of REM and SWS by voltage or time-scale and frequency characteristics across ages versus qualitatively similar behaviors. Randler et al. 2019 in Sleep Medicine for time in bed across ages. 3 to 4 months for baby to get used to day-night cycle. Sleep is more fragmented with aging. Trying to use fine time-scale modeling to predict changes in sleep across development and aging. Both wake and sleep bouts start exponential and random but become power law for wake around P15. Is this same as pre-term to term? Lesioning SCN you don’t see as much of the power-law behavior being established. By synaptogenesis P14 in rats is similar to about 3.5 years of age in humans. Younger subjects have longer tails than older subjects. Difference has a lot to do with NREM to REM transitions. Probability of waking up is much higher in older people so they sample the long-tail of the distribution and have longer individual waking bouts. LC develops early in fetus (Nakamura) and transitions/switches to alpha-2 autoinhibitory mechanism around P10 to P15. LC might be off in REM sleep. True in fetal rats but unclear in humans or other species? Diniz Behn and Booth (J Neurophysiol 2010) have physiologically-grounded model to investigate and explore this with and look at mean firing rate using ODEs. Stochasticity lengthens tail. Huge switch at 3 to 4 weeks to entering sleep in NREM phase. Can mode predict this?
'''Jerry Siegel:''' Sleep changes across species. Argues it’s driven by ecological factors. Argues sleep is adaptive in evolutionary sense. Perhaps animals are dying from repeated wakenings instead of loss of sleep for things like disk-over-water technique or experimental deprivations in general. Carnivores show little change in total sleep time with size. Recorded conditions aren’t much like natural conditions so how much does that affect sleep results you get. For example, temperature regulation. Example of platypus believed to have no REM and then they built platypusariums and there was lots of REM. Sleep changes a lot during migration. Amount of sleep in wild typically less than in lab. Elephant sleeps half as much in wild as in lab so about 2 hrs. Do they sleep at best time to thermoregulate. Bats sleep a lot (~20 hrs) but also have a very high metabolic rate. Go into hibernation though SWS that gets deeper and deeper. Could REM sleep be happening while elephants are seemingly awake and walking around? Might it be happening in only certain parts of the brain? Could you see how Mom responds to meat in terms of speed and orientation to determine how lack of sleep affects her. EEG basis for REM sleep but not based on brainstem recording. Call to include more temperature effects in models. Temperature and light are correlated. How to disentangle which drives sleep? Janet Best has paper on how temperature drive sleep cycles.
'''Geoffrey West:''' Body cannot have nearly perfect repair because of increase in entropy and second law of thermodynamics. But brain can be treated as not a closed system. It is an open system that draws from and drains off of resources of rest of body to keep nearly perfect repair. Lifespan and aging both about damage. Should be able to have formula in biology textbook for why we sleep 8 hours a day and live roughly 100 years.
'''Alex Herman:''' Subcortical REM more important in relation to number of synapses.
'''Bob Stickgold:''' More creative after sleep or dreaming. Because new creations are explored then or puts brain in better space or configuration to then do exploring once you wake up. How does this relate to patients with narcolepsy because seems like they have higher levels of creativity or lucid dreaming. Function of dreaming is not to solve problems as much as to explore different solution spaces and find what’s promising. Reminds me of trying to devise better computational algorithms for exploring large dimensional data or parameter space for theory. Maybe our brain has found good way to do this. Question is why does this require sleep to occur. Is it because it isn’t accessible to our conscious brain because done by some other part of the brain and takes lots of energy so have to save energy from elsewhere? If it’s so useful like this, why do we forget dreams? Maybe because their actual content isn’t useful? Or maybe we unconscious access to them but not direct access. Or maybe if you remember too well, you start to confuse dreams with reality and that becomes big problem.
I
'''To discuss:''' Could we make a grocery list of all irreversible processes mentioned in the workshop? Few that come to mind immediately: (1) Priyanga's idea on hot to cold invasion (2) Ecological succession. (3) Niche filling (4) something funky happens with the lottery model when there is a big mutation event (is there a name for this? Surely it is a generic thing that can happen in many other systems) (4) formation of interdependences / mutualism / specialization (I will mention in my talk tomorrow) (5) gene duplication (I couldn't follow all there steps here). Anything else I'm missing?
More specific thoughts on individual talks:
'''Otto Cordero:'''
Succession of species on hydrogel microspheres. Otto uses spheres with four kinds of nutrition. '''(1)''' why are species either specialist (able to digest only one type of sphere) or generalist (able to digest all types). '''(2)''' Why don't the cheaters (those who do not produce digestive enzymes) take over. '''(3)''' Prima facie, I would expect cheaters to have much lower detachment rate. They should just stick onto spheres and wait for the digestive bacteria to arrive. For the bacteria doing the work a better strategy is to detach quicker, at least before cheaters arrive. Is this observed in experiments? '''(4)''' is the interaction between bacteria indirect (i.e. they compete for the same resource) or do they secrete antibiotics or consume one other? '''(5)''' what is the role of diffusion lengths? The commensalist bacteria (those who do not secrete enzymes, do not compete for the main resource, but utilize the metabolic byproducts of others) gather whatever they can within diffusion length. So we can calculate the limit the number of layers of bacteria on a surface. For resources (e.g. dead crab shells) that are smaller than the diffusion length, the shape and size of the resource will also make a difference. Also calculable.
'''Pamela Martinez:'''
Markov process to describe the spread of pathogen with multiple serotypes (a kind of SIR model). How to differentiate between different models with sparse data. Data could be equally consistent with randomly connected states or even a single Poisson process with appropriate mean. A good suggestion during the talk: generate synthetic sparse data using the model, pretend the data is real, and estimate model parameters. Do they have a similar value? '''(1)''' why does the efficacy of vaccines not show up in the population data (they do make a significant difference in controlled studies). '''(2)''' why does the vaccine work on some countries but not the others '''(3)''' Rotavirus somehow interacts with the gut microbiome. There is some literature that shows that vaccines work for people with microbiomes of the "european kind".
'''Annette Ostling'''
Starts with Lotka-Volterra type fitness function. Species are assigned traits between 0-1 and and the interaction matrix is structured such that species with similar traits antagonize each other. This is done with a gaussian kernel in the sum. '''Questions:''' '''(1)''' what determines the number of clusters. Can I use Turing analysis to solve this analytically? '''(2)''' Can I view phylogenetic branches as "clusters"? e.g. animal kingdom, plant kingdom etc. are, in some sense, clusters. And then, there are sub-clusters within these clusters, and sub-sub clusters. What feature should be added to the model to obtain sub-clusters. '''(3)''' Given an empirical distribution of features (within a species or within multiple species supposedly filling a niche) how do I distinguish between environmental filtering vs exclusion?
'''Priyanga Amarasekare:''' Her argument is, species respond to temperature in an "asymmetric way" (specifically, you hit a wall at high temperatures, but the negative response to cold is more gradual). This leads to an irreversible flow of species (via mutant invasions) from hot regions to cold ones. '''Comments: (1)''' I like the idea a lot, very plausible. Here is my alternative (and quite possibly false) point of view: A high population is more evolvable, because there will be more mutants/innovation. Warmer climates have higher biomass (just because it receives more energy) and will therefore generate viable invaders at a higher rate. Maybe.
'''(2)''' She had some discussion about constraints vs selection. It's a possible to dichotomize, but I view these two things as one thing. A constrained region in phenotype space is just one with fitness=minusinfinity, so no one visits there. Possibly just a matter of semantics, but in any case, I don't see how a constraint implies irreversibility.
'''Jacopo Grilli:''' Three-body interactions surprisingly stabilize the community (unlike those with two-body interactions). I found this surprising because the model with three-body interactions is really an effective model of a two-body interactions. e.g. species A,B,C come together; first A interacts with B, then the winner interacts with C. (and you symmetrize this, because sometimes first A interacts with C first). As such, the outcomes of this model should reduce to a Lotka-Volterra model, (with specific structure, under specific conditions). However, I was not able to figure out what this structure is, and what the conditions are. Whatever the structure and conditions, the stability of the system with three-body interactions should not be a surprise if the equivalent Lotka-Volterra equations are also stable. Either way, I would like to understand this better.
'''Greg Dwyer:''' The viruses that infect the pests have multiple DNA's, so I thought that might give rise to an interesting cooperation/cheating dilemma, similar to the one we see in sperm trains. Also there is an interesting three-species coevolution going on between the pests, and the virus and fungus that infect the pests.
Greg likes things he can measure and doesn't like discussing the meaning of life. But then he was converted, and found the meaning of life. Turns out meaning of life is measurable after all.
H
''Overall thoughts: A system can take a certain # of hits. A lot of failures are linear with age on a log scale. Why? Resiliency? Due to redundancy? What explains variance in a population? Natural selection has acted to limit physiological decline to the point that maximizes reproductive success (balanced against costs). So risk of physiological failure are not zero during reproductive years, but are minimized, certainly relative to competing risks for most of our (or other animal's) evolutionary history.''
<u>Bernie Crespi</u> presented on how selection works stronger on the weaker components. Slow, invisible weaknesses increase over time. Can engender exponentiality of decay. Or is it that damage and mutations accumulate over time? Risk of failure is related to the degree of complexity in a system, and tightness of coupling between components. For example, power grids are tightly coupled but not hugely complex. Aircraft are both coupled and complex. Mental disorders represent alternative attractors in a highly coupled system. For immune system, autoimmunity, chronic inflammation, cytokine storms represent alternative attractors potentiated by tight coupling. Both can have diseases associated with over-defense. Both do not trade off well with other systems. Immune system is under greater positive selection, relative to brain system which is more stabilizing. ''Thoughts:'' ''I would ask how the failure rate of systems, complex or not, is impacted by environmental disconnects. For example, failures in the immune system (like autoimmunity) may be common today, but were not so common in ancestral times (re. hygiene hypothesis).''
<u>Dario Valenzano</u> described how relaxed selection shapes the rate of aging across species. Killifish get tons of cancer late in life; almost all die with cancer. Not clear if they die of cancer, just with cancer. Some species live in areas where water is available for a few months of the year. Have drought resistant embryos. Can desiccate embryos. Hatch when rains. 5-6 rounds of spawning. Wetter areas – longer lives. Did a lot of genetic mapping. QTL of longevity. Can do crosses and single alleles can have big effects. Show heart, skeletal, and many other aging phenotypes. See evolution of life spans multiple times across killifish species. So can filter out phylogenetic relatedness. Short lived (annual) species have a larger genome mostly due to expansion of transposable elements and introns. A lot of LINE expansion. Probably due to reduced purifying selection. Much more of coding sequence is under purifying selection in non-annuals. Populations evolving in dry environments have smaller Ne - even within the same species found in dry and wet environments. See expansion of mitochondrial genomes in annuals too, with a higher mutation rate (again consistent with weakened selection). In fact, the mitochondrial polymerase is more error prone. ''The question that I would raise is WHY is selection weakened? Clearly, a smaller Ne would do this, but why is there a smaller Ne? Is there a bottleneck with each season where only a small fraction of dried embryos survive and reproduce again?''
<u>Shripad Tuljapurkar</u> – As we get older, get more response to similar challenges. ''My thoughts - but do we? Or do we simply return to homeostasis more poorly? In fact, sometimes responses are exaggerated and prolonged (such as with inflammation) in old animals.'' Escape from “well” for a phenotype is dependent on depth of well, size of fluctuations, and multidimensional space. Old age involves shallowing of healthy well and deepening of sick well. ''Thoughts: the well may also be broader, with a greater diversity of phenotypes (cellular) for the same cell type. Relaxed purifying selection, as well as epigenetic drift, could contribute to this.''
<u>Sabrina Spencer</u> discussed how replication stress and DNA damages induces p21 in mother cell (somatic cells in culture), leading to increased p21-high offspring which stay quiescent longer. And these cells are stress resistant (survive better D dam). See much less pausing at early passages of foreskin fibroblasts relative to late. ''Thoughts: It will be interesting to see how these dynamics contribute to tissue aging. Also, in a competitive tissue do these same cells with DNA damage get purged? There is evidence from flies and mice that that damaged cells can be pushed out by healthy neighbors. From discussions during hike, it would be great to have a method to identify cells with critically short telomeres in a population (such as by marking gH2AX colocalized with a telomere binding protein - but not feasible for living cell...).''
'''<u>Summary of Day 1:</u>'''
Themes: 1) Basins of attraction, 2) Failure (gradual, rapid, can we predict?), 3) Underperform or misperform, 4) Tradeoffs and pleiotropies, 5) Networks, not discrete compartments. 6) complexity vs simplicity; complexity vs complicatedness, 7) Don’t make a part that consistently outlives the whole.
'''<u>Day 2:</u>'''
<u>Morgan Levine</u> – DNA methylation landscapes in aging and disease. Changes on the molecular level propagate up through networks, increasing risk of disease. Every level above can tolerate some level of failure below. Biological aging proceeds, phenotypic aging, which proceeds functional aging. Biological age can be different from chronological. Use epigenetic clocks – methylated CpGs. Measured at 353 CpGs throughout genome. R=0.98 for chronological age. Do correction for <12 year olds to account for rapid change in early life. Developed “phenotypic age” predictor of aging related mortality based on clinical measures, which can predict disease. Had almost 10,000 individuals aged 20 and up, with up to 23 years of mortality follow up. Combine 9 clinical markers (albumin, CRP, MCV, etc). Incredibly predictive; 1 year increase in biological age coincides with a ~10% increase in risk of CVD, cancer, diabetes and lung disease. Comparing top 10% to bottom 10% of a group shows ~2x difference in HR. Most people will fall within 5 years of their chronological age. And difference remains stable. For all cause mortality, get about a 1.05 HR (per year of change in biological age, I think). Correlations with DNA methylation clock too. Horvath clock corrects for differences between tissues (there are differences). Developed transcriptomic signatures, correlated with DNAm clock. Cellular respiration, lipid oxidation, and nucleotide metabolism correlate well with biological clock. ''Thoughts: how do changes in DNA methylation correlate or not with changes in DNA mutation accumulation? Some data from Sanger indicate that the increases in mutations from birth to death are linear, but epigenetic changes show a sharp increase during growth/development periods. Why?'' ''How can we take these results and make them useful for the individual? It's not enough to just tell someone they're going to die earlier than their chronological age suggests, but what could be done about it?''
<u>David Schneider</u> – measuring resilience of hosts to disease. Can measure tolerance to an infection. Can we predict trajectories for individuals? Not yet, but can for populations. Looking at tolerance across 150 mouse strains from crossing 8 different inbred strains. If plot NK cells by RBC, get loop. Can figure out where someone is on a “disease clock”. Can see wave of parameters, as one rises while the previous falls. IFNg is first, gamma delta T-cells last. Kynurenine comes on when animals are sick. Orotate is late. Can look at curves for different mice strains (some die, some survive); 29 metabolites correlate with resilience (can predict who will die and who won’t). If arginine bottoms out, the mouse will die. And arginine really is critical for surviving infection. Kynurenine pathways is also critical (as it is for aging). Parasite load correlates poorly. If treat with anti-parasitics before d6, reverse on curve. But not if after. Also showed that glucose can prevent deaths from infections (provides energy). Our mouse houses are at 20 C, while prefer around 30 C. Get hypothermic upon infection instead of fever. Check out his blog: Phasecurveblog.wordpress.com
''Thoughts: how do we alter responses so that recovery is favored over failure (death)? Can we manipulate metabolites? His and perhaps other labs seem to be doing this.''
<u>Marten Scheffer</u> – ecology of resilience. How quickly is the return to normal after perturbation? He studies lakes. Sometimes they don’t recover. Due to severity of insult or the brittleness of system. Catastrophe theory on tipping points. How to know a stability landscape? For example, a tropical rainforest can be become less stable if there is less rain. If plot tree cover with rain across Earth, not a simple correlation, but discrete clumps – attractor states (dips in landscapes). See for bacteria in human gut with age. Basins of attraction are shallower in old age – smaller disturbances can push out to new state. Greater resilience can reduce cross talk between systems (negative cross effects). Can find early predictors of climate change, depression (mood), etc. Do we need to make perturbations in humans to predict future health? ''Thoughts: how do manipulate resilience? Can we increase it or at least prolong it later in life?''
<u>Wrap up</u>: Develop frameworks for understanding biological failure. Can we derive concepts that inform models? Bernie – can connections be made between larger theories (Antagonistic Pleiotropy and Mutation Accumulation) with molecular changes like in epigenetics. What are patterns of change? Why do they change? Feedbacks maintain homeostasis and restore it post injury.
Hmmmm. Why is so much of what bad happens to us as we age exponential?
Our charge: Frame what we agree on, what are the big questions, and what we could do to get answers (with unlimited resources) and what can we not answer?
1) Try to come up with fundamental and important outstanding questions in term so biological failure?
2) What are the kinds of connections that we can make between conceptual approaches that will allow us to work on these?
(1) General problem/question
DNA/RNA/proteins - cells - cell-to-cell interactions - tissues - organs - organ systems - whole organism - groups of related organisms as in colony - community including non-relatives
how/why does senescence occur at these different levels, in the contexts of antagonistic pleiotropy and mutation accumulation? does balance of early benefits vs later costs vary across these levels and systems?
does the complexity/stability/entropy of the system matter? how?
why does senescence accelerate exponentially in humans - synergisms between failure types/levels? why up so fast around age 60-65 in particular (later for women)-becoming less useful as a helper/contributor/transfer-er then, and are being 'replaced' these roles by next generation?
(2) Caloric restriction - reduces deleterious aging effects on health but does not lengthen lifespan if started after adulthood - why not? increases investment in maintenance, reduces growth and reproduction, based on energy, thus mitochondria
all cells use energy - does energy restriction impact some cells/systems more (eg brain if not fully protected); or are all systems just ramped down - how does this impact on disease risks, exactly - do diseases require more energy? or are fewer mistakes/mutations made?
(3) Single cell talk p21. Is this work key to cancer biology in that tumor suppressor functions being lost leads to all cells just keeping reproducing? can one show that senescent cells are less likely to transform, using this system?
(4) Idea that senescence is an emergent property (of defense against worse failures) appears quite important to me
(5) Comparing biological failure between organisms and higher 'levels' (societies, civilizations, ecosystems, etc): how might societies/civilizations age in same way lower-level systems do? maintain current system even when its benefits decline, due to environmental or other change? external causes of increased weakness of units at different levels? Fit with Jared Diamond's ideas about Collapse of civilizations? (what were those need to check).
Parallel causes, biology and higher levels - complexity and coupling of components? Too much investment in defense? (Russia 1991)
DAY 2 questions
how do CR genes relate to antagonistic pleiotropy?
what are roles of germline mutation in relation to somatic mutations in senescence?
intelligence/brain size related to aging/senescence both within and between species (and related to CpG calendar/clock methylation) - why? do immune systems have 'intelligence'? (measure of their efficiency of functioning in some manner).
why length of post-reproduction life vary (esp females) - fewer stresses when young? more resilient senescence system? other? related to intelligence (more than pre-menopause lifespan)?
age around 60 represent tipping point into accelration of aging effects/disease risks? synergistic failure across systems?
adaptive landscape model - flatten with age as selection weakens, lower adaptive peaks? mutation moves you downhill easier?
prioritization of organ systems with age - how vary - costs of degradation is each system? brain, immune at top.coindcides with ability to trade off across systems, higher->less prioritization
aging and loss of information in networks, higher entropy, protein folding, feedbacks in networks, how measure these changes, chaperones, perturb system like insulin challenge test in pregnancy
DAY 3 thoughts, questions
How well does theory interface with data in study of aging?
Agendas of proximate-mechanisms, biomarker geroscience researchers how much integrate evolutionary or other theory insights, how useful will they be for advancing theory?
Empirical ideas
Crispr on CR genes then analyze behavior of cells/tissues across age
CR genes for maintenance (rheostat) how much trade off with growth and reproduction genes, strong theory predictions here
[aside: do empiricists who focus on specific function tend to not think about tradeoffs (in evolutionary contexts) because they involve competing/beneficial but incompatible functions?]
DIfferent organ systems ought to senesce at different rates
W
* Add information/discussion about non-brain "functions" of sleep (e.g., metabolism, immune function) with their time scales
* Add Other brain "functions" of sleep to include are: mood/psychiatry; objective neurobehavioral performance; "cleaning" of toxic substances (glymphatics); neurochemistry
* What are target/pivot points for changing some aspect of sleep?
* How to define and then study sleep "need"? Is it different for each function? with age? How does it interact with sleep duration or timing of sleep?
* Feedback between sleep and circadian rhythms; not just one way circadian-> sleep
* Two comments from discussion:
** Comment: Frame the “needs” of sleep not as competitive with each other but as parallel.
** Comment: Not all functions of sleep bring you back to where you were before; some are to bring to a new state (e.g., learning, immune)
+
H
* Considerations of time-scales. Where/how is time encoded?
* With time, new structures emerge, network connectivity changes
* Can hyperactivity that optimizes growth and development contribute to aging (antagonistic pleiotropy)
* Peto’s paradox—what things besides the number of cells, which should influence probably of neoplastic transformation, confer differences in species-level cancer risk?
* Only the intercept, not the slope for the age-specific rates of lung cancer differ between smokers and never smokers.
* May not be intrinsic damage accumulation and instead may be the difference of the “aged” vs. young environment which differentially selects for oncogenesis
* Clonal hematopoiesis could be facilitated/accelerated by the old versus young environment?
* How does this fit in to the findings from heterochronic parabiosis? Would changing the environment of the niche from old to you or vice versa alter 1) clonal hematopoiesis, or 2) probably of neoplastic transformation? What is the impact of HRAS in young versus old environment?
* Aging may be both the small perturbations to the system and the reaction to perturbations in neighboring systems for which one depends on. With a healthy neighbor idea, a small perturbation may not matter if the interacting system is functioning well, but ass the systems all go down hill together, there will be feedback that will cause an acceleration in the rate of multi-system declines. This may account for why things that change linearly with age, produce exponential morbidity/mortality cures at the level of the population.
* CR is changes a lot of pathways that together produce both lifespan and health span extension.
* One can start to map the CR response to systems-level (or network) changes involved in a variety of pathways.
* Interesting overlap with the genetic architecture of insulin resistance
* The same pathways that come up in CR are also the ones we are finding relate to the epigenetic clocks in humans. Also evidence that CR decreases mouse epigenetic age.
* Wells that define healthy vs. sick and transition out of a well depends on depth, curvature, and the size of the fluctuations.
* A hidden Markov is a good way to model this because the transition to a given well/state depends upon the current state—doesn’t matter how you got there just the parameters above (I would also add that there are likely many well and proximity to a well influences probability of transition).
* I would ask whether the overall landscape is changing, or whether the available landscape (what is in your vantage point given where you are) is the only thing that changes. If we think of the wells as discrete states, they shouldn’t actually change, just your probability of sampling a given space given your current space will change.
* Can you also model entropy using these wells? Basically you will have a tendency to move lower in a landscape towards equilibrium and moving uphill requires energy input.
* Does it make sense to consider single-cell data and investigate the probability distributions of having a multi-dimensional profile as a function of age (could use something like Mahalanobis distance).
A
- General disagreement on the definitions of aging and senescence. It does not seem a purely semantic problem, but reveals the lack of universally accepted essential conditions to define the aging process.
Example: Role of asymmetry (necessary condition or not) in the cellular division process for the emergence of aging.
- Discussion on the more appropriate measure of time to observe aging of living things.... The intrinsic timescale of different organisms (for example doubling time in fast-growing conditions) and the timescale fixed by the environment (e.g. feast and famine cycles) should be taken into account.
[interesting parallel with the approximately constant lifespan of mammals if measured in heart beats shown by G.West]
- In several different systems the hazard rate (for cell division or for death) shows an exponential increase followed by a plateu (e.g. in mortality rate in E.coli shown by Uli and from Lindner group) . Different theoretical models can reproduce such a trend. Is there a way to do model selection?
- Need for measurements of metabolic rate in bacteria in order to build a complete phenomenological theory of resource partitioning and its relations with growth and mass. An extension of existing theories built on data based on balanced exponential growth probably need to include survival and the cost of maintenance... +
C
- dynamics: if we think at aging as the approach of critical transition we have specific predictions: critical slowing down, flickering, etc
- information: where is information in this dynamical / tipping point picture? Do we need it?
- scales and information: at what scale should we look at the brain to "explain" the interesting (information) phenomena? +
-applying different approaches in complexity towards understanding age-related changes in brain organization (e.g., thinking about flickering, network 'clogging')
-prospects of expanding/linking our work to additional scales of analysis +
H
<u>'''Day 1.'''</u>
<u>Bernie Crespi.</u>
Accidents (failure) are inevitable (normal). Challenger disaster, Max 8 jet. Risk of failure depends on complexity of the system. Tight coupling vs modular. Tight coupling – butterfly flaps wings causes a storm elsewhere.
Plot of tightness of coupling vs complexity. Brain and immune system go in top right corner. Is biological failure mediated predominantly by the brain and immune system?
Can make this plot at top right corner at the cellular level too.
<u>SLS:</u> how to test resilience of cells? Can we come up with a set of perturbations and appropriate responses? A stress test for humans before a surgery, or for populations of single cells?
Disease and senescence anti correlate with intelligence. Correlation between intelligence and lifespan is mostly genetic. A result of having more ‘good’ genes. Peter Visscher lab 2016.
Mental disorders represent alternative attractors due to tight coupling. Schizo, depression, autism.
Neurons have to be well-defended bc they are long lived and aren’t replaced.
Neuronal stress: age, apoe4, infection, energetics, insulin resistance, sleep deprivation.
Bodily senescence is mediated by over-defense and inflammation.
Failure due to bad tradeoffs.
Is senescence due mainly to defense against death? The defense ends up killing you.
The immune system is what is keeping people alive in the face of infectious disease.
Loss of coordination among organ systems causes health risk. They are networks. brain is made up of many different kinds of cells including immune cells.
<u>Dario Valenzano.</u>
Relaxed selection shapes the rate of aging across species. How can we reverse time and intervene in aging?
Killifish live 4 months.
Some strains live 15 weeks, others 30 weeks. Cross them and get genetic maps controlling lifespan.
<u>James Degregori.</u>
Why do we get more cancer late in life? Not the accumulation of oncogenic mutations. Loss of tumor suppressors actually reduces renewal of stem cells. but past authors focused on increase in cancer.
Investment in tissue maintenance during youth.
New model of multi stage carcinogenesis where a mutation has a negative impact early in life and a benefit later in life. Bc a stem cell is not well adapted to an old lung and thus will evolve. The same mutation can be maladaptive early in life but adaptive late in life.
Bad cells are poorly adapted to their environment and are pushed out.
It’s not the cell, it’s the mismatch between cell and environment. Adaptation is more likely bc the system is not working.
Senescent cells are a huge part of the environment.
Adding another dimension to somatic evolution.
IL37 transgene is anti-inflammatory. *
Inducing a genetic translocation with crispr in young mice did not cause tumors. But it did if you induce in old mice. But not if you suppress the immune system.
Quality control goes down late in life.
Lawrence Loeb showed that increasing mutation frequency by mutating DNA Pol delta’s proofreading function does not result in increased cancer.
We are loaded with oncogenic mutations. We are not avoiding cancer by avoiding mutations.
Can capture the effects of caloric restriction by increasing autophagy.
<u>Rozalyn Anderson.</u>
Caloric restriction (in the absence of malnutrition).
RNA processing is different in the caloric restriction group.
Push pull of metabolism vs growth… gets disconnected with age.
Increase expression of PGC1alpha in mitochondria by 1.5x to mimic caloric restriction.
Wound healing is slower in CR animals. They would not be vigorous in the wild. They do things more slowly. The animals are smaller so there’s a growth effect of CR.
Constant vs periodic caloric restriction? Intermittent fasting. Fasting and resilience research is growing.
<u>SLS</u>: How well can you capture the benefits of continuous caloric restriction by doing 12hr fasting?
The periodic '''ketogenic''' period is important. Can now look for panels of molecules and patterns of change.
<u>Shripad Tuljapurkar.</u>
Back and forth between evolutionary timescales and one person’s lifetime.
Assume we are the same today as the Romans were.
When we are young we can handle challenges (like missing the bus). We have a muted response to challenges when we are young. Older people worry about things and have higher amplitude responses.
People get less homeostatic as they age.
Recovery is harder as you get older.
Menopause has to do with the number of reproductive follicles that you are born with. (?)
Grandmothers; learning and wisdom. Theory of transfers. Can transfer care, knowledge from old to young.
How much added longevity can we explain from transfers? 15 years. 50yrs to 65yrs old.
<u>Questions I got from the audience after my talk:</u>
· Is high-p21 CDK2-low state a trap? Bc p21 goes up and up, suppresses CDK2 more and more, which makes it harder and harder for a cell to escape and re-enter the cell cycle.
· Would every cell go through the CDK2low state at some point? Can you develop a sensor that would turn on once a cell goes into that state once. (sc)RNA-seq to identify features of CDK2low cells that would be long-lived or permanently on.
· Does % CDK2low cells increase as cells become senescent? Use primary cells at increasing passages.
· Have you normalized p21 and 53bp1 curves by CDK2inc? Set CDK2inc trace to 0 and look at points of convergence with the other 3 subpopulations.
· Similarity to yeast aging and asymmetry of mothers-daughter division?
<u>Barbara Natterson Horowitz.</u>
High adrenergic cardiovascular events = stressful events.
Vasovagal syncope = Fainting. An external event (like getting blood drawn) triggers slowing of heart, vasaodilation, underperfusion of the brain.
Alarm bradycardia. Loss of tone and fainting is a life-saving event when animals are being hunted. Playing dead. It is primarily in juveniles.
In adults, stressful events lead to fight or flight, not fainting.
A developmental response. Until you’re able to run away successfully, there’s a parasympathetic response. 13 years old is age of highest fainting
High adrenergic events cause heart attacks. Spike during Northridge earthquake.
Heart rate variability.
Restore dynamic properties of autonomic nervous system with exercise or mindfulness meditation.
Trapped shorebirds – 10% have heart attacks (and die?) after being trapped with a net. Some species fare worse than others – can you use the differences to predict which patients will have heart failure at the next stress.
<u>General discussion.</u>
Themes that have come up:
Variance.
Potential wells. Hysteresis. A to B is not B to A. light stress vs strong stress.
Resilience / homeostasis.
Can you see how frail a person is before a surgery? Don’t protect the elderly from all stress, give them some slight challenges and some exercise.
Measures of potential wells. Cell-to-cell variability. Maintenance of a proper phenotype.
How can we compute/quantify potential wells?
Define health: Physiological, psychological, social.
What is surviving? The ecosystem.
Scales: Genes, molecules, cells, tissues, individuals, populations, ecosystems, civilizations
Why do cars age and collapse?
Tipping points.
What is aging? Relationship to immortality?
<u>'''Day 2.'''</u>
<u>20190409 Day 2.</u>
Themes that came up again and again:
1. Basins of attraction. Wide, narrow, deep, shallow. For sick and healthy.
2. Failure. Gradual, rapid, can we predict?
3. What is failure? Underperform, misperform
4. Tradeoffs. Investment in critical functions could be associated with aging.
5. Everything is connected as a network (eg organs)
6. Complexity (many knots) (vs complicated – many folds)
<u>Let’s define aging:</u>
Programmed development. As well as non-programmed stochastic decay.
Process of aging vs pathology of aging. Separate disease-related aging.
Chronological age vs biological age
How does body protect itself and make itself new again?
It’s an open system so you just age.
Lack of rejuvenation and regeneration.
But a 40 year old woman can make eggs that produce a 0 year old baby.
Aging is entropy
Aging vs longevity
A series of repair mechanisms for wear and tear.
Cataracts and fibrosis are pathologies of aging. These are repair mechanisms that allow longevity.
Aging is suboptimal adaptation to environmental stressors
Aging is an active response, not passive.
Don’t forget fitness.
<u>Morgan Levine.</u>
Geroscience. Failure starts at molecular level, each level up can tolerate some issues.
People age at different rates. Can we quantify biological age.
Epigenetic clocks. CpG methylation. Usually turns down transcription if it’s in the promoter.
Horvath clock. 53 different tissues. Many post-mortem. Measure 353 CpGs across the genome and get a strong age predictor.
Hannum clock was trained on whole blood.
Uses machine learning to minimize the residual of the fit to a line. Want some residuals since an r=1 gives no information (you wouldn’t be predicting their age, it would just be their age).
It’s exponentially changing under 15 yrs old, then linear.
Get phenotypic age, then try to infer biological age.
10k people. Take out accidental death and HIV.
Q: Why focus on all-cause mortality? And not likelihood of a specific disease? Bc age increases your risk of all diseases.
We have multiple biological ages across different tissues.
Most people fall within 5 years of their biological age.
People are stable. Age at one time point is predictive of age 9 years later. People age 1 year per year on average. People don’t reverse.
Levine clock. Can predict age at menopause.
Most of the CpGs do not overlap across the 3 clocks.
Want to cluster the CpGs into module to figure out what they represent.
<u>SLS</u>: do you do any longitudinal studies? Do take your own tissues over time and predict age?
<u>SLS</u>: have you compared these clocks to telomere lengths?
<u>David Schneider</u>
Measuring the resilience of hosts to infections by mapping disease space.
How sick will you get with a given load of pathogen?
Tolerance to microbes is a population-level thing. Can’t do with one individual.
Health is a stable state, death is a stable state, but sickness is a set of many transient states.
Resilience: 1. How stretchy the system is. 2. How far you can go before you break.
With age, the phase diagram might change such that a state that was survivable when you are young is no longer survivable when you are old.
Measure cytokines, parasite density, NK cells, RBCs, etc over the course of the infection.
Could potentially predict where in the trajectory a kid is who comes to the clinic with malaria.
IFNG is first, gamma-delta-Tcells are last.
29 metabolites before infection predict which mice will survive.
Looking for critical transitions.
Q: Do animals have to always go all the way around the curve? No, if you treat them with drugs before day 6, the curve reverses, but not after.
Can rescue ketogenic mice from death by providing glucose.
Now want to do it all in humans.
Q: have you done this in old vs young mice? Yes, in 70 week-old mice
<u>Marten Scheffer.</u>
Resilience. How quickly things go back to normal.
In ecosystems, sometimes they don’t recover from a perturbation. They become brittle.
Landscapes are a useful metaphor.
Tipping points – 2 alternative basins of attraction.
How do you know where you are in a landscape?
Look over every square kilometer at tree cover vs amt of rain. Scheffer Science 2011.
Forest is stable, savannah is stable.
Abundance of a particular gut flora increases with age.
Systemic resilience of humans and other animals. Scheffer PNAS 2018.
Can we infer fragility? Climate collapse, societal collapse?
Generic early warning signals = indicators of resilience
Critical slowing down happens at mathematical bifurcations. If you push a ball and the slope is shallow, it takes a long time to return to normal. Fluctuations are longer, are more correlated over time.
Resilience in mood if a bird shits on your head. One hour later, are you happy again or still down?
All systems, human, earth, are continually being perturbed.
<u>SLS</u>: Can we make use of low-level fluctuations in single cells and compute autocorrelation to calculate resilience? Can we use this to measure cellular age? **
Measure resilience by measuring small fluctuations over time. Systems close to tipping point show:
1. Larger fluctuations
2. Stronger autocorrelation
What the other working group talked about: Measure balance on a balance plate (elderly do worse); Measure blood pressure during rapid standing from sitting (blood pressure slower to return to normal in elderly). Once you are frail, it’s already too late.
How can you predict from autocorrelation how far you are from tipping point? You cannot measure the distance to the tipping point. Bc it depends on chance.
A
<u>'''Talks'''</u>
'''Matteo Osella'''. Interesting idea of connecting laws of physiology (Hwa) with aging/senescence. Not trivial how to do that for single cells.
'''Lin Chao'''. Aging and asymmetry in E. coli. Advantage of asymmetry is portfolio diversification. Somewhat optimal level of asymmetry emerges.
'''Uli Steiner''' . Fitness as combination of fecundity and mortality. Death in the mother machine (surprisingly high): mother (early daugther) has an increased mortality rate with age, while her latest daughter has an approximately constant mortality rate. Idea: late daughter inherits the damage, while the mother was starting with minimal damage. No correlation between mother and late daughter lifespan.
'''Sri Iyer-Biswas.''' Cool data on C crescentus and collapses. Interesting observation of memory of past conditions lasting for long time.
'''Owen Jones.''' Senescence across the tree of life. Measure shape and pace (timescale)
'''Sabrina Spencer.'''
'''Bree Aldrige'''
'''Chris Kempes'''
'''Martin Picard'''
'''Geoffrey West'''
'''Ideas'''
What is aging? Requires asymmetry in division and the ability to label individual with a "time stamp". In E. coli age of the pole, in mycobacteria cell wall. Senescence is the loss of function associated to aging. The question then is what is function. We have a bias for growth rate.
It is very unclear to my whether asymmetry is adaptive or not. It is also unclear how to prove it.
The other axis is memory. Memory (information) about the environment. Unclear how that is related with aging. +
C
A number of critical questions were raised about the best levels at which to establish causality when it comes to understanding both natural and disease-related aging. Namely what are the best observables to consider? Should these be single measurements or network based measurements. Could the best indicators involve comparisons across genetic and cognitive networks applying similar methods, or as is more typical time-dependent changes in a given network at one level of analysis. A recurring question was the relationship between energy and information and how their reciprocal dependencies change over the course of time and the course of disease.
Some very general issues that arose in conversation that require further exploration include:
#Approaching disease from a first-principles theoretical perspective - as is common in ecology - thus establishing principled data collection objectives (this would require a rigorous operational definition of the disease state in formal terms)
#The value and limitation of the current inductive, big data approach, that focuses on time-dependent associations
#The meaning of cognitive reserve, exercise or error correction, and the limits to these
#How adaptive phenomena that are ongoing mitigate the disease state or at some point perhaps accelerate it.
#How we might better explore causality in large systems with extensive non-linear feedback mechanisms. +
H
A theme that I see across the talks and in the discussion is the issue of complexity and how integrity of complex systems is lost with age and how it might be retained to impinge on health and resilience.
The idea of the adaptive landscape is very useful as is the idea of tipping point - i particularly like the idea of aging as a series of transitions where the path taken dictates the possibilities open for the future
Hochberg: Concepts that caught my attention, as a function of age is loss of resilience equally felt through the lifespan ie young v old? Also Diverse/idiosynchratic networks – how should models be informed. The idea of hierarchies of regulatory or adaptive nodes is interesting but I wonder do we know that there are grades of nodes in the first place, if there are how do we find them?
Crespi : Biological Risk Matrix… coupling versus complexity. I had difficulty with this idea because the systems were assigned importance but it wasn't clear to me what the basis for those assignations was. I have viewed the organ systems as different but inseparable pieces of the organism as a whole. I do like the idea of viewing the aging of specific processes in terms of trade-offs - I wonder about the inbuilt redundancy of systems and think we could consider the possibility that age-induced adaptions might just as well be beneficial - ie tailored to the prevailing internal environment or the current disposition of regulatory nodes.
Valenzano: The fact that ecology predicts genome size was super interesting and that the expansion is explained by transposons! I also loved the idea that the long-lived species had more emphasis on positive/purifying selection and the short-lived species had more evidence of the relaxed selection. These are an amazingly useful species for the interactions of genetics, environment - the exposome!
Di Gregorio: Among cancers age dependence in risk is shared despite differences in etiology and mechanisms of tumorigenesis and differences in the stem cell pools that these cancers arise from. All map to a common cancer curve – incidence as a function of age all lie right on top of each other. This nicely captures the idea that aging creates a ubiquitous risk increase for cancer incidence. Metastasis – moving from the environment where the tumorigenesis initiated – giant hurdle for success but likely to be huge number of cells that slough off. Idea that youth is associated with “Healthy Neighbors” proximal to the initiating cancer cells. Aging is not just accumulation of mutation: idea that the tissue changes create the promiscuous setting. Essentially: the behavior of a single mutation is not equivalent in young and old environments
Tuljapurkar: Response to challenge changes with age – makes the case that the amplitude of the response in young is muted and un-muted and over-amplified in aged, although I would think of it as a disconnection in the response whether that be under or over reactionary. I was very taken with the idea that response to a challenge could push you out of the equilibrium space and into a different state altogether & that you would need to consider the following: Depth of well; curvature of the well; size of the fluctuations.
Spencer: very interesting model for thinking about non-genetic sources of heterogeneity looking at individual cells through the lens of Proliferating v quiescence as a cell state. Spontaneous heterogeneity in asynchronously cycling cells used to identify key nodes in dictating the pace of cell cycle - really nice cell biology and time lapse imaging to uncover CDK2, p21, and stalled forks in the mechanisms. Interesting observation that Mothers pass damage on to the daughters so that the intent to enter quiescence already established in G2 of the mother. Genetic approaches to manipulate CDK2/p21 show that the slow cycling cells have higher stress tolerance – If you force CDK2 in the pausers have a fitness deficit - I wonder though if this is just because cells not ready for division were forced into it creating a vulnerability to stress rather than exposing a beneficial role for the pause.
Natterson-Horowitz: Using examples from different species the relative balance of sympathetic/parasympahtetic v vagal response to stress was explored. High-adrenergic events: eg sudden cardiac, death cardiomyopathy refelctive of a dysregulation of autonomic balanceTonic immobility in response to attach – seen in many different species. Alarm bradycardia is primarily a juvenile response. Not called syncope (because not people) but it sure looks like it. As the animals transition to adulthood they swap over to the sympathetic/parasympathetic response. I do wonder how this is coordinated and communicated with maturation - could there be a signal to indicate that a critical physical threshold had been reached for example, like myokines even? I like the idea that with age there may be a disconnect where there is a loss of the ability to toggle between sympathetic/parasympathetic and vagal responses☂Open questions are whether early experiences inform future balance in terms of response to trauma, Can we build predictive models?
OPEN DISCUSSION: Identified 3 themes: 1. Vulnerability with age, 2. Variance among individuals, 3. Complexity
Some of the ideas that caught my ear include the following: a) The theme of return to homeostasis; b)Changing landscapes with age – and changing landscapes with exposure, the Idea of tipping points applies here too, are there sets of perturbations that would allow you to track their responses and use that to predict the transitions among wells. Expectation that the well tracks with fitness may not be realistic – items roam around the ridges, c) idea that using “Layers” might be a useful way to parse the different components, can we use layers to frame the aging landscape – feedback loops among layers. This concept is likely important for understanding resilience, d) I really liked the analogy to collapse of ancient civilizations: ebbs and flows – tendency toward senescence – more complex, more overheads, more fragility. Is there a way to articulate loss of integrity or resilience and how it might be conceptually related to ecosystems or larger scale entities? so you might think of system failure as an emergent property, e) Nailing down the semantics – framing the language by context would be helpful for developing strategies to move these ideas forward.
Levine: Changes on the molecular level propagate up through the system. I really liked the concept that some degree of failure is tolerated up to a point. Important goal to provide an endophenotype that allows evaluation of intervention efficacy, provide insight into aging - the strategy is to work with risk identification – biomarkers of vulnerability. The methylation "clock" has been developed independently by several groups. Want it to predict more than chronological age so the goal now is to capture the true residual. There is some fascinating biology during development: under age 12y get a boost in methylation that has steeper trajectory from about 15y on the slope is consistent up to 60y. Interestingly, clocks with highest association to chronological age do not have the best predictor ability for disease when age is used as a covariate ☂. Looking now for gene networks – weighted gene correlation networks and then establish associations among networks and the various clocks – amazingly mitochondrial function OxPhos is a primary network ☂. Great next steps will be to take any given module and ask how will they are conserved across the clock – see congruent and discordant depending on what is being looked at for a given context- or in other words, which piece of the clock is driving the associations at different age group and diseases – building “disease-ome” maps.
Schneider: In traditional strategies to determine the resilience of hosts you infect and look at the dynamics of load and symptoms make a regression – generate a tolerance curve. Works at the population level but not at the individual level. To get around this employ a strategy where you generate phase plots – hysteretic curve- this nicely demonstrates that at each point in the infection you ae in a different space- curved around arrow. There was a wonderful theme centered on how to best visualize complex challenge and recovery data - so creative! One idea was to explore Resiliance/Tolerance/Robustness versus Frailty. Taking this a step further you can add new dimensions – generating a disease manifold. Superimposing genetic diversity on this framework revealed the beuatiful underlying biology: Cytokines, metabolome: orotate, parasitic density, granulocytes, reticulocytes. See basically the same changes among the different mouse strains but occurring at temporally distinct phases but always tracking with the course of infection and recovery. In a higher dimension see a wave move through the system – like ripples on a pond. As the shape of the wave changes that indicates where you are in disease course, if the order is perturbed then that might indicate morbidity. This is perfectly set up for exploring aging and comorbidity in the loss of implementation of the infection response and recovery. Some very interesting metabolism is at the heart of this response. Parasite load doesn’t correlate well with outcomes across lines but metabolic status going into the infection does.
Scheffer: Resilience: capacity to recover from challenge, or how quickly homeostasis is recovered after perturbation. Idea of brittleness. System state v conditions (and their interactions)– tipping points where you have two basins in unstable equilibrium – loose resilience when basin of attraction becomes small ☂. An interesting example is abundance of particular bacterial group in the gut, the landscape changes with age and there are apparently tipping elements in the human intestinal ecosystem. As you age more likely to have a particular population distribution. A really fascinating idea is that of Universal rules that govern critical points – true for all dynamic systems where critical slowing down occurs at mathematical bifurcations. The idea is that there may be generic early warning signals for tipping points that could potentially be used for determining indicators of resilience.Using this concept, you might investigate natural fluctuations – the uniformity and small scale of resilient systems becomes increased in variance in the less resilient and so the fluctuation change serves as a dynamic indicator of resilience. The emphasis is on patterns of micro-recovery that inform of the dynamics of the system rather than on the state of the system. If the system as a whole reduces you then get more cross-correlations between subsystems. Failure in one system is communicated to other connected systems. This is a really clever idea and a terrific match for understanding the slippage of system integrity with age.
I
A. A group of few simple species can evolve into a large number number of interdependent species. There is an information theoretic entropy increase in this process. This means that if you reach into the ecosystem and randomly pick a species, you are more uncertain about what you will find that you were initially.
B. There could also be a more direct irreversibility associated with ecosystems: do larger and more complex organisms like us generate more heat (and hence entropy) than simpler organisms? This will need to be answered experimentally. How does entropy production per unit time compare among 100kg of bacteria, 100 kg of insects and a human weighing 100kg? Are complex organisms more efficient at using energy and resources than simpler organisms?
C. '''Jacopo's''' discussion on evolutionary games reminded me of a paradoxical class of games called '''''[[wikipedia:Parrondo's_paradox|Parrondo games.]]''''' These games involve a combination of games that are all losing games, but when played in succession lead to a winning strategy. They have recently been used to explain some ecological and biological features (see references within the link).
'''Pamela:''' Could you please post references and /or tell us about the data pump techniques you used?
'''Fernanda''': I like your philosophical idea of finding interactions where all organisms benefit. The second law does work against us by stating that for order to increase somewhere, there must be disorder created elsewhere. However, I do not agree that two species that mutually benefit must compete with or harm a third species. They could be harnessing energy from abiotic sources such as the sun, wind or thermal vents. Is it mathematically possible to have systems with only positive interactions between the living components? Are there any such systems on earth? +
D
Alfonse Hoekstra's discussion of multiscale resilience was fascinating to me. The network simulation models of Dervis and Peter Hoffman were very interesting and provide useful insights, but to mimic the complexity of human physiology, we would need hierarchically structured networks. I wonder if there are some invariance principles in multi-scale resilience that could reduce the degree of complexity of this type of modeling. The principles and results of hierarchy theory could be relevant here.
Sanne's talk on DIORs (dynamic indicators of resilience) was also quite interesting. There are several open questions here: how to model temporal autocorrelation; how to handle non-stationary time series; how to do systems identification with DIORs, i.e. how can we predict responses of frail/nonfrail using estimates of DIORs. I also think the idea of reactive tuning to stimulus can be examined using novel metrics of DIORs. I would be interested in exploring these ideas in my work!
The second day's talks were also very interesting. Heather's case history was captivating, highlighting the challenges of treating a human being as a complex physiological system. I liked her point that we need to observe and let the system tell us what needs to be done. Ingrid's talk was very informative on the modeling of complex ecological systems. Porter's talk on the resilience of the Pueblo Indian nation to colonization was very educational for me. I can relate to my own Hindu/Indian culture's resilience in having survived several invasions and colonization over the centuries. The idea of axiology, the systems of values which provide the core resilience to a culture, was most interesting. Warren presented some exquisite data on mouse resilience. To me, tlis hehighlighted the huge potential of using mouse models to develop a comprehensive modeling framework for resilience. +
An excellent first day. We heard theory-based perspectives and came directly upon the challenges of human subjects research. The theory based perspectives from Dervis Vural, Peter Hoffmann, and Alfons Hoekstra illustrated the (relative) simplicity of models that effectively abstract and recapitulate several well-recognized characteristics of human aging and frailty. Yet human-derived data are messy, do not lend themselves easily to hypothesis testing because they are so often observational and incomplete, and are confounded by the outbred nature of humans, their varying allostatic loads, and the variety of acute-on-chronic illnesses that bring them to research studies and/or clinical care.
The most interesting part of the second day, perhaps, was the presentation on resiliency among the indigenous peoples of NM. It became quite clear that the ability to (re)generate networks and interactions was foundational to regenerating the population. A reasonable inference , and mirroring Dervis Vural's presentation on Day 1, is that the capacity to reconstruct networks is foundational to resilience. It is unclear whether it is the ability to reconstruct some evolutionarily specified or developmental network is required, or rather a more general capacity. But "fixing a failed node" is unlikely to work unless that failed node is the foundational "network spawner".
The need for a marker that reliably informs clinicians that the capacity to recover from perturbation is now (and forever) exhausted is apparent. The problem around end of life is acknowledging that it is indeed end-of-life, that the physiological derangement exceeds reparative capacity, with or without the stabilization that clinicians can provide. As technical medicine gets better at dealing with minutiae, such markers of inevitable collapse become even more important. As a reminder, 27% of the US Medicare budget is routinely spent in the last year of life, with a substantial uptick in the last month as part of the "rescue phantasy" to use Freud's term. "slowing" and delayed correction of spontaneous or engineered perurbations is a start, but seems by itself insufficient as a basis for clinical decision making.
H
April 9, 2019
Charge for working groups
# Come up with a list of major ideas/problems/concepts that you think we need to work on.
# Think about what conceptual areas could be linked to better address the major questions discussed in #1.
Apr 8, 2019
A few thoughts about general questions for discussion:
# What do we mean by "Biological Failure"? Aging? Senescence?
## Is there such a thing as a truly non-aging organism? An immortal organism?
# Things that change with age...
## Why do so many things appear to increase exponentially, and in parallel on a log-linear scale, with age?
## Are there commonalities across other levels of organization with respect to how things change with age, and by 'things', this could be function, or selection, or failure.
# What maintains variance among populations in aging. Even after controlling for G and E, we still see high levels of variance.
# Issues of complexity/simplicity and networks were touched upon today, but we have not yet gone into detail, discussing this.
Bernie Crespi
Suggestions idea that complexity can be bad. We can think of organismal organization (and function and failure?) in a two-dimensional space of couple (loose<-->tight) and complexity (linear<-->high). Berni suggests (I think) that biological entities with high complexity (brains, immune system) are more prone to bring the entire system down with failure. Entities that are simple and loosely coupled are less likely to fail badly. Suggests a negative correlation between senescence and intelligence, though the GWAS data supporting this are problematic (like the recent study claiming to find genes for SES--https://www.biorxiv.org/content/10.1101/457515v1) are likely due to social stratification. Bernie's neologism of the day: ''badaptation.''
Dario Valenzano
Comparative analysis of annual life history in killifish. Would benefit from Nathan Clark approach to look at rates of protein evolution in annual/perennial species: https://elifesciences.org/articles/25884. Expansion of mitochondrial genome is striking. I wonder if the finding of increase in genome size in the annual species is true of annual plant species (like corn and rice, which have very large genomes) relative to perennial plants.
James deGregori
Notion of adaptive oncogenesis, with stem cells well adapted to niche. As tissue ages, stem cells are no longer well adapted to the niche in which they find themselves. I wonder whether these models are also relevant to the selection process that happens *within* a tumor once cancer growth (and mutator phenotypes) is underway.
Roz Anderson
CR animals show a few major clusters of correlated -omic features, while AL animals show a very large number of small molecules. We should discuss just what these correlations mean, both statistically and biologically, and why these correlation structures (adjacency matrices) differ among the two groups so dramatically.
Tulja
Finishes talk with three general questions:
a. How strong is the trade-off between added longevity and lost fertility
b. Can we explain environmental plasticity?
c. Does stochasticity matter? On any time scale?
For all three of these questions, I wonder if high-dimensional assays (metabolome, epigenome, etc) might have something to add to this discussion...
Sabrina Spencer
The technology that Sabrina is development could add tremendous power to the work now ongoing to track yeast cells as they age in real time. Also shows that quiescent cells are resistant to various stressors. Is that simply that they are metabolically quiescent and so not taking in these toxins?
Barb Natterson-Horowitz
Suggests that the disease-associated heart responses that we see could be maladaptive responses that evolved for adaptive reasons ('capture myopathy', 'alarm bradycardia'). Barb finds evidence for these phenomena in non-human species, but these could well be a large underestimate, simply because the vast majority of these events are never observed, and when observed, not reported.
P
As I am working with conceptualizing the formation of human settlement patterns I came searching for the state of art knowledge on socio-ecological processes influencing their formation. Santa Fe and the Institute greeted me with even lot more than expected starting from eclectic societies of art and science in the city to the bold focus on "the most important" backed by art and mathematics in the SFI.
The course on population and environment offered a wide range of bits and pieces related to demographic dynamics, population size, migration, economy and spatial processes all essentially connected to my own study. All those bits and pieces created a confident methodological backgrond that significantly advances my work. Several ideas in the talks and personal exchange with other participants were directly related to my work. The themes of demographic transitions, migrations, ecosystem services gave me a lot of tought food. More philosophical questions asked in population axiology presented paradoxes in maximization in ranking which in addition to ethical inquiry also makes you think on the level of abstraction of models.
Another topic I was secretly pursuing was the projection of long term processes to the future. As my own work is based on archaeological data from the past, envisioning it towards to the future creates a powerful motivator. And I did observe several interesting trends. Although in most parts we were presented analytical aggregate models the reasoning and understanding behind those macro-level processes involves choice. So a somewhat emotional takeaway from the course for me was - if we want to project from the past to the future, we need to conceptualize our models to the level of choice, and even further to the level of agency (and hopefully use agent-based models on the go).
I really thank SFI, all the organizers and participants for the great opportunity. +
D
As a clinician, it resonated with me to hear Dr. Vural describe that in his models, sometimes "strategic repair" may be necessary in order to re-stabilize a complex system that is progressing toward critical decay (but has not yet reached the critical point). I like the notion that success or failure of the whole system could depend on the order of which nodes are repaired first. I am often faced with the clinical challenge of multi-organ failure and often an intervention that would benefit one organ system might put another at risk, so it is hard to know what sub-system to prioritize. If we could understand the human system better and it could guide "strategic repair," this could have real clinical utility.
It also resonated with me to hear Dr. Hoekstra's description of a similar stressor resulting in vastly different outcomes in his model systems. If there are feature of the system BEFORE or JUST AFTER the stressor that reliably indicate which outcome is going to occur, that would be very useful for prognostication and for making treatment decisions.
Dr. Gijzel's descriptions of data management challenges when dealing with time series human data was helpful (and also cathartic, because I deal with the same challenges). The tension about whether analytical decisions should be guided by conceptual framework and clinical judgment, as opposed to empirical decisions, was something I recognized.
I feel more strongly that the field will benefit from semantic harmonization and precise terminology.
My perspective has changed in that I will be more attentive to opportunities to study resilience changes across the lifespan. I learned about childhood metabolic changes I was not aware of. I learned a new parameter for characterizing temporal autocorrelation across varying lag times. +
H
Bernie Crespi:
The hypothesis/framework presented that risk of failure increases with linearity and tightness of coupling in the functioning of a system is interesting and stimulating, but I wondered about how to quantify these two axes in a practical manner in order to test this idea. How easy is it to classify biological systems on these axes? How much of this classification is artificial? I was also puzzled about how to separate these processes (brain functioning, immune response, and others) given their interactions and integration; but if all these different systems are integrated, then does it make sense to attribute risk of failure to properties of each system? Can this approach be adapted to take into account this integration?
How to define complexity in this context?
The fact that some aspects of senescence may be due to overdefense in later age, seems in line with other observations and theories that senescence may be the result of protective systems dysfunctioning with age; I wondered about the role of our interaction with infectious diseases in shaping our biology and therefore our life history through these mechanisms.
I would like to learn more about how mental disorders match "attractors" potentiated by tight coupling; should read more about this.
Dario Valenzano:
A very nice study system in killifish populations and species where climate/aridity/habitat (temporal ponds) correlates with divergence in life history. Lots of different approaches (QTL, genomics, comparative analysis, phenotypic studies of aging, mutation rates estimates): overall suggest that populations/species living in drier/more ephemeral habitats have accumulated a high genetic load affecting aging and lifespan through relaxed selection, which may be due in part to their demography with frequent bottlenecks. Nice to see such a complete study showcasing how mutation accumulation affects aging evolution. Reminds me of the work by the group of Christoph Haag on daphnia (also in ephemeral ponds)); see reference attached.
James DeGregori:
claims for a change in paradigm in our way of understanding and modelling cancer: number of mutatations would not be so relevant, because of selection on malignant cells. Model of fitness landscape with different peaks, changing with age. What are the mechanisms that make malignant cells unfit in young individuals (niche? policing by other cells? Can we model the interactions between cells to generate such fitness landscapes and their change with age?
What does explain that the increase in rate of cancer on a log scale is linear with time, with the same slope: can we predict that? what does it require in terms of assumptions?
Rozalyn Anderson:
Effect of caloric restriction on lifespan in 40 years long term experiments in monkeys; growth and immune function intertwined. What is the effect of caloric restriction with infectious disease? More generally would a better integration of infectious disease-driven evoluton and life history/aging evolution be necessary?
Shripad Tuljapurkar:
Hidden Markov models driving transition rates could be fitted using longitudinal data on health, frailty and morbidity: could we then estimate how fitness or potential wells change with age?
Barbara Natterson-Horovitz:
Fainting in young individuals broadly distributed taxonomically; could be an adaptation to escape predators in inidviduals that have no other options; links with capture myopathy
What are the life history correlates associated with capture myopathy vulnerability; points to insights in considering relationships with other species (here predation) in shaping evolutionarily features associated with various conditions
Morgan Levine:
aging dynamics at different levels of organization or different aspects of aging (biological, phenotypic, functional) may be different; what are the articulations between these different levels and aspects of aging?
Idea that markers of biological age may be improve our predictive power of condition beyond predictions based on chronoogical age; changes in methylation with age; predictor of chronological age based on methylation age (R=0.98 idea that residuals may inform on condition, but not much residuals variation to play with? not much extra information? methylation not as predictive as biological markers; methylation increases in all tissues but not at the same rate with age: why and how is it related to function?
Schneider & Sheffer: in both presentations, slow recovery signals approaching a critical tipping point; can we get an idea about how close we are from the tipping point? Martin says that there are no obvious way. With systems that have no fixed points as equilibrium but naturally cycle, it is also harder to find signals of a critical transition
general discussion: questions that I like= 1) how to connect ideas about tipping-points/resilience/netowrks to classic evolutionary models of aging, 2) can we design simple models predicting exponential increase in rate of mortality with age? with constant slopes? 3) can fitness landscapes/potentail wells be more than useful images to help us think about aging? Can we parameterize these models? Can they be used to make more quantitative predictions?
other ideas: can we build models with both antagonistic pleiotropy and mutation accumulation and study their interactions? are there distinct predictions about collapses of networks based on these different source of genetic variation affecting aging?
role of social interactions at different levels in biological failure (cell to individual), role of interactions with other species (parasites, predatiors, mutualists)
W
Big idea: consideration of multifactorial definition of and function of sleep
Susan Sara: Hippocampal ripples are important for memory consolidation; how would the timing of increased ripple density post-learning change with species and/or development? Can this be connected to brain size/networks involved in memory consolidation?
Gina Poe: How are memories tagged for strengthening or weakening? What computational approaches could inform our understanding of this? Complexity of memory likely influences the time scale - how might we explore this experimentally or computationally?
Sara Aton: Mechanism whereby a rate code is translated to a phase code. How does modulation of neurotransmitter milieu affect the propensity of network to propagate particular rhythms? How does the power of rhythms relate to metabolic rate of the brain? Does synchrony play a role?
Kimberley Whitehead: How do mechanisms for spindle-burst production relate to mechanisms for spindle production? What are appropriate comparisons for pre-term human infants and animal models of different species (rat, sheep, etc.)?
Beth Klerman: Seasonality is observed in the duration of the biological night as measured with melatonin expression; how does this relate to mechanisms for seasonality encoded in the SCN? Important new direction to take insights from groups and translate this to predictions for individuals? How do we handle missing data and investigate the tolerance of models to missing data?
Victoria Booth: What do changes in the structure of sleep tell us about changes in physiology? Blumberg showed a lack of consolidation of wake in OXKO infant mice emerged during development when WT mice were able to consolidate wake bouts to transition to a power-law-like behavior. We showed similar differences in a mouse model with acute orexin cell loss (Branch et al., SLEEP, 2016). What can we learn about sleep in development in the absence of orexin neurons? How would physical distance between neuronal populations and degree of myelination affect the time scales on which these populations interact? For example, would slower transmission allow more bistability between states? Neonates have more wake to REM sleep transitions. How would we understand this in terms of different network configurations for NREM/REM regulation? Would this be a useful constraint?
Jerry Siegel: Fur seals can switch between bihemispheric sleep and unihemispheric sleep. Modeling has suggested that this can be understood based on relative strengths of contralateral and ipsilateral connections between brain hemispheres (Kedziora et al., J of Theoretical Biology, 2012). How do predictions compare to anatomical evidence? What other insights from physiology could we get from this system? Important to consider the role of temperature and thermoregulation in sleep dynamics. This has been explored a bit in a recent model (Banuelos et al., Effects of Thermoregulation on Human Sleep Patterns: A Mathematical Model of Sleep–Wake Cycles with REM–NREM Subcircuit, in [https://link.springer.com/book/10.1007/978-1-4939-2782-1 Applications of Dynamical Systems in Biology and Medicine] pp 123-147). What are other ways that these ideas could be included in mathematical models of sleep?
Van Savage: What can theory tell us about the quantities we should be measuring experimentally? For example, the theory suggests the importance of considering the ratio of time asleep to time awake rather than durations only.
Geoffrey West: Developing a theory that connects sleep duration and lifespan. Lifespan scaling is derived from ideas of sleep for repair; could we introduce a modification that involves a different role for sleep in development? How is this impacted by differences in species development/maturity? Can we understand this scaling in terms of the DNA methylation view of aging (e.g., converting dog years to human years: https://www.sciencemag.org/news/2019/11/here-s-better-way-convert-dog-years-human-years-scientists-say)?
Alex Herman: If synaptic density drives metabolic rate of the brain, is there also a role for different rhythms? Reporting a sharp transition in the relationship between the ratio of time asleep/time awake to brain mass. What else does this transition correlate with? Changes in behavior? What would this be abrupt and not graduate? What does synaptogenesis peak and how does it relate to this transition? Follow up with Junwu Can regarding her method for threshold detection of this transition.
Bob Stickgold: Different types of sleep for different types of learning; gist words - how does this relate to other measures of creative thinking? What is the role of forgetting for dreams and what is important about the dreams we remember? What about problem solving that can occur while sleeping/dreaming?
C
Brains fail - like bridges, like suspenders, and like relationships. The brain constantly operates at a point of criticality - at least for our optimal cognitive state. Adaptive systems with connectivity often have cascading failures. Can the brain heal itself? Is this robustness? Can that self-organization go wrong, or be improperly applied? It's not a bug - it's a feature. I am reminded of the "swiss-cheese model" familiar in root cause analysis. Multiple failures must be serially associated
Why do we care about a diagnosis? What is meant by a "proper" diagnosis? Does "diagnosis" imply stationarity. Must we have a tight mechanism to have a diagnosis? Or perhaps simply a cluster of symptoms. Or a basis in which to guide therapy I approach my patients based on what is the next thing I am going to do. Sledgehammer solutions. - may be best. Borrowing from vaccines, can we look at treatments as "learning". Pain is an example for a top-down approach to disease. Much like traditional Chinese medicine.
Highlight 1
Richard's buzzing that described experimental determination of the boson and how it related to inconsistencies in Alzheimer's Disease was simultaneously the most confusing and the most entertaining part of the conference. +
W
Common principles of sleep-related oscillations that subserve some form of 'learning', e.g. bursting on a background of neural quiescence - this definition would encompass the unusual features of sleep in early development. Very interested by a) Susan's reference to the Aston-Jones 1981 paper that shows attenuation immediately prior to firing as if to 'boost' the effect of the firing, b) that oscillations can occur but the ones that have many frequency bands coupled together are the most important for memory consolidation, c) Susan's quote 'synchronous activity during sleep provides a substrate for []' (the square brackets could be replaced by nearly all functions!)
Remembering: we need augmented oscillatory power for a stimulus to be learned (Sara's talk) and this can be artifically provided (Ognjanovski et al. 2018), how might this relate to augmented sleep EEG power post brain injury - is it possibly not just a biomarker of damage but a marker of the synchronised activity necessary to 're-learn' new circuits? If a neuron which has learnt something continues to stay more active, for how long? How does that link to the renormalisation mentioned in final slide?
Forgetting: Is it REM sleep or forgetting or dreaming specifically for forgetting? Very interested by Gina's reference to Crick 1983 Nature paper - how could that relate to infants who have MORE REM sleep but probably don't yet dream like we conceive dreaming
Sleep oscillations as a spatial filter: because some neuronal ensembles can't keep up with the speed of oscillations, creates a spatial filter
Cortical region specific projections from Locus Coeruleus (Chander et al. 2019 ref from Gina's talk): a way that sleep-wake states could differentially modulate sensorimotor areas in the developing brain, to help them to differentiate?
Durations of sleep: Bob's conceptual theory that 100ms of sleep would be sufficient to encode a memory e.g. a microsleep. Could that be reversed to think about some adaptive function of the extra-short WAKE durations pre-term infants have e.g. 2 minutes?
Body weight vs. age predicting sleep-wake bout durations: Following up on Geoffrey's question, I will go back to the literature to check those references that biological age is more predictive than body weight. But thinking about his question, my forthcoming project on intra-uterine growth restriction would be an ideal opportunity to look at this in more detail.
Other ways I intend to improve my practice following this meeting: report ambient temperature
Evocative metaphors!: 'Mountain of wakefulness'. Bob's 'Memory evolution', more dynamic than just consolidation which doesn't fully capture sleep's functions. 'Ground truth' - the challenges of establishing this in many aspects of sleep research
Does LC firing trigger upstate or is LC firing triggered by cortical down state?
Can paramaterizing models with anatomical distances and scaled time-constants lead to emergent behavior in dynamics of sleep wake cycles consistent with data?
What is the history dependence of sleep states? Are all REM and NREM sleep bouts created equal over the course of a night?
What
Oscillations: What sets the intrinsic rhythm of a cortical area? What reads out the phase-ordered temporal sequence of spikes?
Can animals have more localized forms of sleep?
How are the electrophysiological signals of REM sleep related to its functions?
How can animals with close phylogenetic relations show such different sleep patterns?
Do we have a phylogenetic history of the different functions/phenotypes of sleep? +
D
During the first day, although the talks were very different, they very nicely complemented each other. It is amazing to see that we are all adopting slightly different approaches to investigating resilience in human aging but that they can all be placed in the larger framework of resilience of complex dynamical systems. We are all pioneering in this area and only sharing our struggles and recent insights was already very valuable, at least in my experience.
During the second day, I began seeing that we are working along 2 parallel lines:
#Finding ways to quantify resilience / resiliencies
#Increasing our understanding of the dynamics of the complex system in terms of resilience
Some reflections:
- I liked Alfons' idea of making real-life examples of "Resilience is ........" in the form of a short narrative / artwork / graphical illustration / equations. I agree that these can be very helpful to increase our understanding of resilience and involve more people (e.g. clinicians) in the resilience thinking and discourse.
- Marcel commented that for humans, it's clear that there are alternative stable states in health, but we do not know what are the precise perturbations and positive feedbacks causing the transition. To increase our understanding about this, I think we need to start with making mechanistic models. We can use these mechanistic models to generate new hypotheses.
- Ingrid pointed out the difference between acute stressors (perturbations, e.g. a stimulus-response test) and slow stressors (drivers, e.g. aging). +
C
Excellent presentations were given highlighting the descriptive power of convolutional deep networks, also illustrating its partial explanatory power and where it fails. This pointed out some interesting ways forward that have to go beyond their current architecture, in particular taking dynamics into account. Interplay between structural and functional connectivity was highlighted. Limitations of stationary metrics were evident (functional connectivity), but nicely showed how far it can be pushed successfully in applications. Model approaches providing explanatory approaches were often too simplistic, not in terms of realism, but in terms of simplifications of concepts (brain states, behavior, as static entities). In the discussions it was evident that there is a need for a formalisation of the internal state dynamics of the brain, before perturbations can be applied to it (breaking the brain). A formal frame work for provision of and recovery from such perturbations is needed, several good attempts were provided and need to be pursued in the future, and supported by data. Need for individual predictive capacity of these frameworks was highlighted rightfully. +
A
Experimental models to study cellular aging vary dramatically.
We clearly need a consensus definition of aging, or more specific concepts. Is aging the loss of specific functions, the loss in the ability to divide, "senescence" (which itself does not have a consensus definition), the movement towards mortality, or the accumulation of "information" over time? Can there be a single definition of aging across the tree of life - from single cells to complex multicellular organisms like mammals?
If the definition is a functional one - aging is the loss in the ability to perform X function, then aging needs to be contextualized. Organisms at different scales (prokaryotes vs birds vs humans) have dramatically different "purpose" in the living world, and they carry out very different functions. Is there one type of aging that unites them all? Or qualitatively different forms of aging, or aging processes?
An interdependent challenge with the previous one is the issue of measurement. What are good measures of aging - again it depends on how it is defined.
If aging is defined as ''something'' that tells us how close to death an organism is to end of life (i.e,, mortality) or to loss of function, then it implies that aging biomarkers need to be developed prospectively. In other words, the aging marker need to predict some future behavior. One example is the DNA methylation or epigenetic clocks. +
D
Fascinating, multi-level, presentations and discussions.
Some points that have come to mind for me. Concerning Dervis' model, I was thinking how having the nodes have an internal structure, that is, each node would have a network structure -- self-similar structure -- would affect the dynamics... I think such an approach would connect nicely to the other presentations concerned with the multi-scale organization of the system.
Concerning the analysis of physiologic signals and the ability to extract different types of information, the site https://physionet.org contains a lot of useful information and may even include a community for discussion of issues (sadly, I haven't worked in this area for a while now).
Ingrid's talk has raised many interesting questions about the different types of models and their different purposes. My thought is that we need research occurring at all types of models with the understanding that each type can contribute to the others. For example, the parametrized, complex climate/ecological models enable us to conduct computational experiments that otherwise would be impossible. However, because of the complexity of the model it is impossible to gain insight on which specific experiments to conduct. Simpler, stylized models, that could be developed and tested against the complex models, could provide the insight to select what computational experiments to conduct.
Axiology is an extraordinarily interesting concept. Knowledge is a responsibility not a right for the Pueblo peoples. When we talk about resilience, what are the things we are valuing... Resilience of Pueblo peoples to colonial injuries: system had developed redundant relations that can prevent failure in case of injury to system.
Porter's talk reminded me of a study of mortality in Chicago some decades ago during a particularly extreme heat wave. Tow communities with similar socio-economic, ethnic and educational characteristics had very different outcomes -- the strength of the social networks was the aspect that distinguished the communities and the one that predicted the outcomes.
P
First and foremost, I would like to thank the Santa Fe Institute for this incredible opportunity! This experience was intense and intellectually stimulating in many different ways. As an archaeologist, I found Dr. Lee's presentation on agroecological and environmental-dependent demographic models to be fascinating, and I certainly can see the relevancy of such models in generating better prehistoric population estimates. In addition, I found Dr. Hooper's presentation on statistical model selection to be very useful. Last, but certainly not least, I truly enjoyed the opportunity to interact with my fellow attendees. There is an incredible amount of value in simply chatting with so many interesting and intelligent people in one place. In my opinion, the synergies that derive from the aggregation of so many great minds in one location is one of the many benefits of a place like SFI. I hope to have the opportunity to return to SFI in the near future. +
D
First day: I learned about modeling strategies that can be used to better understand potentially universal properties of damage and repair of the dynamic system. Some were compared to empirical data. Hormesis was introduced (in the context of bone health) as an important consideration in modeling resilience. Open questions included that some patterns/observations obtained from simulations remain to be explored further (e.g., three 'trajectories': die, recover/die, & recover). Still open for me is how to actually apply some of the great ideas discussed today. For example, I had already considered the life-course in outcomes in (older) adults, but still don't have a good handle on how to actually incorporate or study this in an already aged population, or if it's possible. I have some of the same questions regarding the most useful (pre-)processing of time-series data. However, the 'middle out' approach seems to be a useful way to reduce the dimensions of complexity associated with modeling.
Second day: The extended discussion on the differences in the definition of resilience (e.g., engineering vs. ecological) and the addition of the concept of reserve further highlighted the need for standardization of definitions to make sure researchers are all on the same page. Mechanistic models and mice models show promise of better understanding the dynamics of the (aging) human, but caution is advised in trying to translate these interpretations. A case study brilliantly demonstrated that apply these concepts to 'real life' situations (e.g., patient care) requires much more thought. This was reinforced by another case that was interesting, not only from a network interaction standpoint, but also because the patient often knows their state/potential outcomes better than typical 'objective' tests.
Particularly impactful for me was the example of resilience on a community level in the Pueblo people. I find it a wonderful model to follow for understanding what factors contribute to the resilience in other contexts.
C
HIghlights:
Hard to prioritize as every talk expanded my perspective and triggered new associations.
I enjoyed two talks in particular – David’s introduction into ‘breaking’ which provided are nice meta-overview into brain dysfunction outside the usual context of development and aging. Refreshing and lots of food for thoughts. The triade of ‘breaking/perturbation, critical transition, and cascading failure’ nice transitioned into three more concrete directions, which I would loved to have explored more in that workshop:
‘breaking = scale of anatomy’
‘critical transition = brain state’
‘cascading failure = developmental disturbances’?
Also very much enjoyed Nikolaus’s overview talk and insight into convolutional deep networks. Very clear, transparent and a great platform from which discussions emerged.
My favorite open question:
What is the computation mechanism/dynamics at the network level ? Move away from correlation analyses.
Change in perspective: I would like to move away from the discussion of imaging results and move more towards the nature of computation.
Impact on my own work: Converging ideas on collective decision making and coherence potentials. +
A
Highlight: As a scientist, for me the highlight was learning a huge amount about how composers, dancers and choreographers think about the concept of time, and incorporate it into their work. Also fascinating was the realization that the medium within which an artist works places strong limits on how time is handled (to use an overly mundane word for a far from mundane process), and yet, as often occurs in the sciences, those constraints or limits can enhance creativity.
Open Question: Aside from the many open questions about time in physics and biology, the open questions that stood out for me revolved around how artists working across multiple domains (e.g., conducting and dancing) can best deal with incongruities in timing.
How has perspective changed: I began with no perspective on the arts, so rather than it changing, it originated and enlarged!
Impact on my work: Honestly, probably none. But that wasn't the point of the working group.
Echoes of the discussion: reminded me that artistic creation has similarities to creativity in the sciences (see comment on constraints above) but there are important differences that I had been unaware of. Out of our discussions the first day, a wonderful outline of the structure and content of a ballet on the theme of time emerged, mostly in one creative and intense blossoming from Karole at the end of the first day. Her design (blueprint) will be improved upon with iteration, but the core is there. I have never participated in a purely scientific working group, or lab group meeting with my students and postdocs, in which something similar happened. The usual timeline of creativity in the sciences, even by those with the most experience, is far slower, more halting, following a pathway with far more dead ends. +
Highlights at A Stab at Time: The beginning for this venture that we are at now has been very productive. Working here at SFI with Karole, Greg, & John is going to be exciting. I think all the ideas are inspirational for this piece to be produced in 2021. +
Highlights of the Stab At Time Meeting are various. This was a unique occasion to share perceptions on topics including physics, dance, music, traditional Navajo culture and ballet culture. Several concepts that dancers know deeply but never articulate verbally were shared at the meeting, including the way in which dance is fundamentally the architecture of time. Greg Spears' summary of the relationship to time in baroque music, medieval polyphony and how in Stravinsky's neoclassical period this was translated into musical voices expressing various time frames simultaneously was enlightening. Continuing his look at the use of time in music, led to learning about a contemporary approach in process oriented music, which is one of the exciting ideas in the music language of today. This involves using frameworks such as the notion of decay to reveal natural processes that become a part of the listening experience. John Harte's summary of the history of ideas in physics from the Greeks to bosons, quarks and other post quantum processes delivered a history of science as well as philosophical points of view that was profound, succinct and filled with exciting conceptual material that we are translating into images for dance and music. The ideas of entropy giving directionality to time and how at the smallest scales there is no arrow of time were enlightening, confusing to my mind and exciting. His precise articulation of discreet time with the herky jerky sense of movement to match the sense of continuous time that involves solid stance and smooth movement was a great gift in seeing the dance come alive. John's articulation of the ultimate sense of paradox at the heart of time is a great highlight to serve at the core fo the dance production .
The many points of view on time led to an exciting discussion on the instrumentation and spatial configuration for the instrumental layout on the stage serving as a metaphor for time and includes the use of negative space to serve the thematic material. The casting was finalized to capture the ideas of discreet time, continuous time and the paradoxes involved in the limits of our understanding of time. Jock's sharing of experience at the most profound level of dance thinking was a special highlight for me. The meeting resulted in a clear outline shared by all of us - John, Jock, Greg and myself - for the substance and vocabulary of the dance production, one informed by art, paradox and science.
P
Population and the Environment: Analytical Demography and Applied Population Ethics/ChristopherCowie +
Highlights: People genuinely seeming to care about philosophy.
Open questions that came up: questions about the nature of duties to merely potential people and the application of my approach to personal choices as well as public policy choices.
How your perspective changed: I wouldn't say my perspective changed. But I now think I was wrong to assimilate personal choices as well as public policy choices in my approach.
Impact on your own work: A bit more clarity on the above, as well as on the sense of should used to frame my questions (= should of morality not of rationality). +
Highlights: Very many of the issues with which I've wrestled in my own thinking--from big-picture and philosophical questions to methodological ones at various levels of detail--are being studied and advanced by others at this meeting. There have been a few ways of measuring things, or of thinking about them at all, which were completely new and cool to me. And of course several questions and topics about which I personally haven't thought much, but are clearly important to population and environment
Impact on my own research: I've come to a renewed awareness of the value and difficulty of interdisciplinary integration. For example, there are many places in my research where social organization plays some role in the dynamics of food supply and population change, and sharing this here has reminded me of how much can be important and how much there is to find out. +
C
I VERY MUCH LIKE THE idea of taking one disorder -- say Parkinsons and see if we can 1) describe what is meant by Parkinsons at multiple levels of analysis 2) accumulate observations (genetic, circuit level, behavioral and environment-social) that contribute to individual variability - again at multiple levels - especially looking at those patients with more rapid or slower disease progression and 3) account for differences among cartoon models of the disease and the actual disease and 4) develop a dynamic understanding of disease progression and what deviations occur and why.
I am not sure why David K disparages the use of the term brain state -- as I could see that there are constellations of factors at multiple levels that lead to a "healthy" functioning state adaptable to the context. ANs this space could be quite large. One could then imagine vulnerabilities or insults that could push the brain in ways that result in a "state" change such that the brain is now dysfunctional in a life context or loses adaptability. And one can further imagine a brain getting itself trapped in a part of brain space where it is hard to see that any perturbation (treatment) or slow recover processes would allow for recovery. It is probably not a coincidence that the numbers of individuals with severe brain disorders are about what one would expect from being in the very tales of a distribution. The individuals who are 2-3 standard deviations out are those for whom treatments could work -- but what moves someone and what keeps them?
We need a framework that gives us some deep multi level understanding so that we can better access if tweaking X really does impact Z or is the tweak in X actually resulting in an adaptive response in Y that then impacts Z (or maybe stabilizes Z so it does not change or becomes resistant to the X perturbations).
In aging I believe we need a better understanding of what happens to adaptive dynamic systems over time ("as the age" or over life span) so we know whether the changes we see are nothing more than what we should expect and maybe it only seems maladaptive because the environment changes or what we now expect our systems to do at different ages has changed. How do we keep our brains adaptive and responsive -- to continue to explore rather than exploit. This is a different challenge than diseases.
Today I was very struck by our inability to work across levels or to even identify what level is meaningful for what we care about -- and what I care about is using neuroscience and complex systems to advance our understanding of and care for individuals with brain disorders - particularly disorders with no identifiable anatomical lesions. 25 years ago I initiated a program supporting neurorehab research on the premise that information learned about brain-function relationships should be useful in delineating what is and is not possible for recovery.
We have to understand the dual nature of individual differences 1) the many to one mapping -- there may be lots of ways for us to use of our brains to live adaptably in the world and yet - there seem to be a small number of stereotypical ways that brains break.
Mental health probably offers us the biggest challenges. If we could make a difference there -- even re-framing the way we currently think about these disorders - I think this would be a HUGE contribution.
Could it be that mesoscale dysfunctions -- depression, schizophrenia could benefit from mesoscale interventions -- perhaps all the lower levels changes we come to catalog will then come along for the ride,
For aging -- in pathology -- neurodegen -- treatments might require both a perturbation and a stabilization?
P
Population and the Environment: Analytical Demography and Applied Population Ethics/KaileyMartinez +
I am so grateful to have been given the opportunity to attend this short course and grateful for the instructors that took the time to attend and teach us. Obtaining my undergraduate degrees in both Wildlife Ecology & Management and Anthropology provided me the chance to learn about two subjects that have always been deeply interesting to me. Five seasons of archaeological field experience, starting right after my freshman year, provided me practical skills to partner with what I was learning in the classroom. Through undergraduate courses and now my graduate program though, I have found that the kind of interdisciplinary thinking I naturally lean towards is not always fostered in a traditional academic setting, which is why I was so excited to see that it was the main focus throughout the course.
The topics concerning fertility and human population were very interesting, as I had little knowledge in the form of relating these concerns to deeper global issues, besides the basics of an increased population, and the solutions that could be found through more critical understanding and application. The topics presented by Chris Kempes, Caroline Bledsoe, Lori Hunter, and Andy Rominger were most directly related to my interests and helped me to better understand how I could apply my variable knowledge, as well as what I learn in the future, to globally relevant issues that I feel compelled to work with, such as climate change and conservation/sustainability. Being able to talk with the other students was also extremely interesting. As one of the few students at the Masters level, hearing about research being conducted by all of the doctoral students and professionals, was inspiring. The two thought provoking days we were able to be a part of have impacted me greatly and will provide me with greater ideas to include in my thesis as well as hopefully help me explore and decide upon a path for me to pursue in my future doctoral studies. +
C
I am stuck with a picture of aging and collapse, motivated by catastrophic shifts in ecology, which simply takes the form of a saddle-node bifurcation. A functional and dysfunctional system are separated by some energy barrier. Aging (somewhat by definition) corresponds to decreasing energy barrier height (and therefore increasing probability of transition). This (at this level tautological) view comes with two interesting consequences:
- (critical) slowing down: the typical timescale at which fluctuations relax increases over time
- in multidimensional system there is an effective one dimensional trajectory describing collapse
The latter point, suggests high reproducibility in collapse trajectories. At what scale this framework is useful is unclear to me. At the coarser scale, when only two states exist (functional and not functional) the only thing that matters is transition probability (the when, and there is no how and why). At that scale bridges and brains fail in the same way (as lifetime distributions sort of match). I am very confused about the confusion around the scale(s) at which we want to study aging and breaking of brains.
I found extremely interesting the discussion of machine learning / neural networks as toy models of representation and/or learning in brains. +
P
I am very grateful that I got a chance to attend this very great meetings and met with great people from a very diverse background, and diverse field of knowledge. I am an economics graduate student, whom before came to this meeting has a very limited knowledge on how broad is the population-climate problem. As I am exposed to the knowledge I received from this very meeting, I now have a sense of more factors that made my mind opened quite larger than before. I now understand that as some more people out there debating on which one to do first from which part of the world and what scientific method to be used, the more effective way to do is to tackle population-climate problem in collaborative scientific methodology, the way this meeting has been set up since the very beginning. +
C
I found NIkos's presentation enlightening and on point. I will certainly go to his primer article. I think the imbalance between abstraction and empiric work was strong. There was an uncomfortable level of abstraction for me. I am not sure my perspective has changed too much, because of this. I would have liked to spend more time on the questions Russ raised at the end regarding ways to "get things together". +
P
I have learnt the linkages between various themes presented from extinction to population demographics to family planning and conservation. Although most of the data presented has limited and very specific variables but the presentation of such variables and correlation between the variable is non-conventional. However, much of the focus of explanation of such linkages is quantitative rather than qualitative. Thus, there is huge scope of finding more meaning to the data and substantiating the quantitative findings with the qualitative ones.
In this two-day seminar, I have identified around 18 new interdisciplinary topics to research on. In my coming teaching and field sessions at Tata Institute of Social Sciences (Mumbai-India), I will be working on these 18 topics with my Master's Students +
C
I have three brief comments:
(1) Regarding brain explanations. I agree with others that we should seek to explain behavior, at a fine-grained level, in terms of measurable brain functions. However, it is important to acknowledge that broad analogies involving architectures or cost functions will NOT do this. Those kinds of findings are interesting and possibly necessary, but not sufficient for explaining behavior except in the broadest strokes. What is needed is rich mathematical models that link brain measurements to behavior. When such models are available they can be translated into whatever expressive system is most useful for the purpose.
(2) Regarding brain measurement. Neuroscience is currently strongly measurement-limited. We have a wide variety of tools, but each tool is limited in spatial resolution, temporal resolution or coverage, and most tools cannot be used in humans. Given this, the best that we can do is to use our measurements as efficiently as we can given our modeling/prediction goals. In the end, all brain measurements are merely different views of the same system, so they will all be correlated with one another to some extent and in the end they should all converge on the same explanation.
(3) Regarding brain dynamics. The brain is a spatially distributed nonlinear dynamical system. To understand such a system requires that we recover the whole trajectory of the system through space-time. However, as noted above we are measurement-limited. We can recover the spatial marginal alone (e.g., in fMRI) , or the temporal marginal alone (e.g., in EEG), but we can't recover both simultaneously (except in very reduced systems or in very special local cases). The fact that we cannot recover the space-time trajectory of the system inevitably limits the provisional explanations and models that we can construct; it limits how well one can answer different kinds of questions; and it limits the usefulness of dynamical tools for analyzing and modeling our data today.
W
I like starting our meeting “What is sleep?” with a session on function, because sleep looks different in different animals. so to figure out what sleep is we need to figure out what it does, then figure out what is necessary (conditions) to do that function then look for those conditions across ages and species.
Of course we have to start with some fundamentals and commonalities of sleep, even though we don’t know if they do - or should - define sleep or whether it is incidental-- a side effect, instead of an essential to sleep. I believe that thanks in part to the people here today, we have that starting point. We also have some basic functions of sleep identified, although it has been messy because until recently we have not known enough about those functions to adequately test whether sleep is important to those functions. But, again, we are at the point where we we have enough clues to start our reverse engineering phase.
In those early days we could not reverse engineer because we haven’t known what brain areas were responsible for what kind of learning and what neurotransmitters and electrical activity signatures were needed. But now, thanks to a horde of thousands, we know a few essential things:
Theta, gamma,
ACh, NE/DA
Phosphorylation
Potentiation/depotentiation, engrams, ARC, cFos, mRNA, protein synthesis,
Circuits: what is interconnected. How specifically NE and ACh targets forebrain,
Order of play: cortical registration, Hippocampcampal assembly, and eventual cortical strengthening...
Now we are ready to roll up our sleeves and figure out what sleep really is!
----------------------------------------------------------------------
Why awakening-based sleep deprivation is not great:
# As Jerry Seigel points out, if you wait until an animal shows electrographic or behavioral sleep beforee you awaken it, then you get the arousal-response.
# My 2016 Sleep paper shows (replicated by Duran et al., 2017) that cortical electrographic signs of sleep misses much subcortical sleep even normally... how much more when sleep pressure is high! We also show that hippocampus is in REM when when the neocortex remains in NREM normally, but even more when the hippocampal homeostatic drive (learning-induced) is high. Thus, any sleep deprivation protocol that relies on posture, EEG, or muscle tone would and probably does miss a lot of sleep that goes on "locally". Thus any negative finding, e.g., "REM sleep is not important for X learning task" could be completely wrong if we are not measuring that particular type of sleep from the area involved in that type of learning and/or memory consolidation. Recommendation: measure the amount of sleep from the actual structure essential for the function you are testing.
# Some of the sleep deprivation studies use pharmaceuticals like caffeine or some other stimulant, which is good because it eliminates the awakening response discussed in point 1 above, but of course stimulants have their own effect on the function under study.
----------On time variances of total sleep time vs sleep bout lengths----------
One function of sleep, like the memory process of sleep, may take only a few minutes or even seconds to accomplish, while other functions of sleep, like repair and clearance, may take much longer to accomplish, and be counterproductive to the learning function, and therefore sleep may need to intersperse the learning function at intervals during the longer sleep period in order not to lose, e.g., what you consolidated.
Either the undoing of one sleep state's function by another or the processes or side effect outcomes of one state requiring another sleep state to clean up after it may be why healthy sleep proceeds in an orderly fashion and unhealthy sleep, even if it is adequate in total length is disordered: REM sleep occurring too soon in depression, REM sleep occurring at sleep onset in narcolepsy, etc. If REM sleep serves to clean up synapses (strengthens those spuriously weakened but tagged for keeping, and weakens synapses spuriously strengthened by SWS or those ready to be erased now that slow-oscillation coupled spindles have consolidated them to their final place) then that would explain why the length of REM sleep is related more to the length of the prior NREM state than to the length of waking.
----------
It may be that the cycles of sleep are timed as they and the states follow each other in order as they do because should our planned long sleep cycle be interrupted, we will at least get some of all of the different kinds of necessary work done. even though having only one cycle leaves it incomplete, it would be better than nothing. Interrupting a sleep cycle midway (e.g. being awakened out of SWS) may be like interrupting the wash cycle midway, leaving clothes soaked and soapy rather than rinsed and clean. A power nap with only stage 2 sleep may be like a quick rinse and spin-dry which is better than the dry but filthy state that the party of wakefulness has left us. But a sleep disturbed from slow waves may, like the middle of the wash phase with soap and soaking wet, leave our brains in a worse state than the previous dry filthy one.
----------
C
I started my section with the premise that when we discuss the brain "breaking", we often operationalize this in terms of changes in complex behaviors, and that these behaviors may be subserved by large-scale systems of the brain. Much of the work to date has focused on the typical average structure of these systems. I showed some recent work we've done aimed at moving these analyses to the individual level, and discussed some observations we've made based on this.
(1) I showed that functional network measurements (at rest) can be quite reliable even in single individuals, given enough data
(2) I showed some data demonstrating that functional network measurements are dominated by stable factors including group commonalities and individual features. Task-state and day-to-day variability is also present, but much smaller in scale.
(3) I discussed our characterizations of punctate locations of individual differences in functional networks, showing that these locations are present across repeated recordings, relate to altered function, and individuals cluster based on the forms of variants they exhibit. While these individual differences explain some (gross) behavioral differences, the variance they explain is very small. I left off with a question to the group of why this might be: why do we see relatively stable behavior in the face of some large individual differences in brain organization.
Discussion centered on how we might think about these effects in the context of distributed organization (or not), to what extent these effects can be overcome by functional alignment that does not assume spatial correspondence, and whether manifolds might be a way of modeling variation in brain function that can lead to a similar functional outcome. We also discussed whether behavior has been measured well enough yet, or if we've been too non-specific in our functional assessments.
General meeting reflection: There were some interesting discussions of multiple different scales and ways of thinking about the brain. I would have liked to have seen a little more cross-talk integration, and/or thoughts about practical directions on which to move forward. How can we better unite models with data? What are the right types of data to collect and theories to test?
P
Population and the Environment: Analytical Demography and Applied Population Ethics/RajanBishwakarma +
I think talks/lectures selection reflected the overall objective of the course. From an economists's viewpoints, most lectures were thought provoking and I thoroughly enjoyed. Personally, not only that the lectures helped me to improve my dissertation, I am grateful to some of the organizer for their help. +
C
I thought that the discussions around the nature of distributed versus local function (arising from Caterina's talk} were really interesting and pointed to the way that our field uses these concepts very loosely. This dovetails, I think, with the issue that I raised about the disconnect between computational neuroscience and network neuroscience approaches.
I can't say that my perspective changed, but the way that I think about how to express some of the ideas was definitely changed. In particular it was really useful to talk through the ways that different ontologies might be useful for different purposes. +
D
I was amazed by the alternative vision of Dervis Can Vural about damage accumulation and aging in random network perspective. Among others it again helped me realize that downstream targeted therapies in chronic age related diseases (which mostly are directed by mechanisms of aging) probably are not to be of great help. The connections of nodes higher upstream, still being damaged or producing damage will still end up in causing the disease related decline.
The presentation of Peter Hofmman showed an elegant 'simple model' of stochastic damage accumulation and repair. It opened the perspective of ramdoness in resilience mechanisms, when the redundancy of reserve function has depleted. The three trajectories of damage and death occurence are very interesting, and I look very much forward to the statistical analyses of these data, after repeated runs of these models.
The talk of Alfons Hoekstra, showed that the multiscale modeling is fit for supporting resilience research and so is fit for being part of the workshop (though he humbly stated that multiscale modeling might not be mature enough for this challenge). The examples Alfons showed in the time -space scaling diagrammes and the connecting interactions do inspire to connect and likewise model subsystems depending on one another and creating a meaningful representation of challenges for aging persons.
Ravi Varadhan and Chhandi Dutta gave an excellent overview of the research grants given in the field by the NIA, and the models already published, respectively. These are excellent sources of comparison for further work.
Second day, we had a very inspiring lecture of Ingrid vd Leemput. The theoretical and empirical reasoning and analyses of ecosystems can be of great help in studying resilience systems in aging man.
Heather Whitson followed up with a nice clinical example. But not only the patient but also the family and physicians make up a complex adaptive system. She also paved the way to stress tests of different kinds, which was completed later on nicely by Warren Ladiges on animal models. This connection added new insights and clearly showed that beside computational models, the animal models are very valuable.
Last but not least Porter Swetsell gave eye openers on population resilience based on his scholarly and personal experiences with pueblo population resilience over time. These all got intertwined in the group discussion, which also opened up new opportunities for collaboration.
The meeting so far showed emergence of many new options, and warrants, inspires aand invites for further interaction and collaboration.
I was concerned that “high-level” models would not be useful in this context, but that turned out not to be the case. It was fascinating to see how actual physiological data can potentially be analyzed and understood in the context of metastable states, complexity, networks, critical states, 1/f noise etc. A full understanding how these things connect and how they relate to real data is still in its infancy, which should make this an exciting area to think about.
The goal will be to "marry" conceptual models and data collected from real systems. How can conceptual models capture stress and perturbation responses, time scales, tipping points & attractors, feedback loops, variability, noise and complexity seen in real systems? Conceptual models should be helpful if we want to learn what measured signals (transient, noise etc.) can tell us about the structure and dynamic state of the underlying system. +
A
I was stuck by the constant "laws" across species that are scale free. I am interested in how this may extend to single-cells in a populations and whether the scaling laws can be expanded from multicellular to intracellular organisms.
Aging (and the fitness objective) needs to be continuously defined and multiple forms of aging coexist in the same population.
Asymmetry is a mechanism to encode aging. Is the functional consequences of aging an evolutionary driver of asymmetry?
How do we experimentally measure aging and the effect of aging in single-cells? We are missing key experimental approaches. +
C
I'm interested in datasets that could serve as a diagnostic for failure of brain networks. It was mentioned at the meeting that there exists data for response time as a function of age. Perhaps this could be used to understand the performance of the brain network as a function of age.
More general question: is there an unambiguous way to determine age by measuring some aspect of brain function? +
P
I'm still (Thursday) trying to internalize and make sense of the intense intellectual experience that was my attendance at the Population and Environment conference. Firstly, the Institute itself was an amazing discovery, that such places exist beyond the realm of Sci-fi was eye-opening; and the atmosphere in the conference and the wider Institute really encouraged a level of deep thinking that often goes on in private but is rarely talked about in such an open and relaxed way. I loved the short, sharp shock lecture style; i.e. all these relatively brief but penetrating talks from experts in a wide variety of fields. I woke up around 3.30am on Tuesday grappling with two thoughts that wouldn't go away; Mary Shenk's concept of the Homo Sapien discovery of 'Co-operative breeding' and the implications of that especially as that process seems to be reversing in the Western World, and a couple of sentences by Simon Levin on the differences between optimization and game theory and which models the actual process more effectively. Thankfully, I got a chance to have a great talk with Mary in which she helped me think about the implications of globalization and development, especially as they relate to the areas of Sub-Saharan Africa that I study. Also, Simon gave me some of his time to discuss the prevalence of optimization, especially in the field of economics and pointed me to some excellent papers to help to develop my thoughts.
So much more I could say: both Mary and Caroline Bledsoe gave me a deeper insight into anthropological methods, Lori Hunter's talk touched directly on a lot of my research and though I'm not a biologist, there were many parts of Caroline Lee's, Chris Kempes's and Andy Rominger's talks that really spoke into my research. Hearing Sir Partha Dasgupta at the end was a great inspiration, and aided many of us economists because he pointed to ways that we could integrate the wider research into our own work. I could write more, but I'll end with thanks to Paul, Amy, Carla and David for the warmth of the welcome, the creation of such a unique atmosphere and the constant supply of food and coffee.
A
Immunity to antigenically variable pathogens arises from an individual's history of exposures to multiple strains. The success of a new strain in turn depends on how strongly it is recognized by immune responses generated against previous strains. Traditionally, cross-reactivity between two strains is thought to depend on the similarity between their antigenic structures. Antigenic maps are a widely used visualization tool in which the distance between strains (represented as points in Euclidean space) provides a measure of their antigenic similarity, and potential for cross-reactivity.
However, the concept of a fixed antigenic distance between two strains implies that all hosts, regardless of their age and exposure history, would gain the same degree of cross-protection against strain B, given exposure to or vaccination with strain A. This contradicts a growing body of experimental and epidemiological evidence, showing that individuals with different exposure histories can exhibit vastly different levels of cross-protection against the same viral challenge.
We are developing an individual-based model which we will use to explore how differences in individual exposure history can cause hosts to perceive different antigenic distances between strains. We will use this model to explore how history-specific differences in immunity arise, and to what extent they cause immunity to differ from the predictions of existing maps, which assume a fixed distance between strains. +
H
Interesting conceptual idea organisms traversing a state space with multiple local attractors and one absorbing state (death), and aging as changing that landscape and thus the probability of transitions between states. Even if we just looked at aging as a plain old flattening of the landscape (or accumulation of noise in the landscape/transition probs?), that would already pop out properties like breakdown of homeostasis/loss of resilience to perturbations (previously attracting basins aren't as steep) and a propensity to reach regimes that were previously hard to get to (e.g. cancers). At first glance it seems like flattening the landscape would lead to more variability across the board, more wide excursions to various states, but maybe that's not quite right - I could also picture a scenario where loss of local variability -> landscape dominated by broad features that haven't eroded away -> loss of diversity/flexibility, effectively being left with a small set of wide highways instead of a larger set of little paths.
Related idea came up today: how do "near flat until sudden acceleration of risk" disease incidence v age curves arise from more gradually creeping molecular aging? Possible mechanism could be that idea of gradual landscape change leading to a threshold where falling out of the basin of attraction becomes much more likely. I tried a toy model over lunch: stochastic logistic growth process with gradually declining carrying capacity. What do survival times look like? Turns out they do get that nice elbow property - could imaging evolving how much you invest in repairs to slow the gradual decline tuning that elbow to an appropriate age of "I probably already died by other causes and my expected # of future offspring is low."
[[File:Fraction-surviving.png|thumb]] +
P
It was a nicely designed course. It has developed a new sense of getting solutions with the help of complex modelling.
I am thinking to evolve a new model with the mixture of burning issue of climate change and increasing population growth into the macroeconomic model for Pakistan. I am sure that it’ll be unique with the addition of complexity.
Dr. Shenka’s presentation was very interesting, either as a citizen of South Asia, the outcomes were not unfamiliar to me but her way of presentation was amazing and easily understandable. +
I
It was encouraging to see that people were intrigued by and interested in the issue of non-equilibrium/steady-state dynamics. Dervis asked a very pertinent question about the appropriateness of the Arrhenius equation for the temperature dependence of population growth rate and other "higher-level" processes.
== Robert Marsland ==
Fascinating work -- the mathematical model opens up interesting new avenues for theoretical development for microbial ecosystem theory. The statistical mechanical approach, and the discussion about links to Lotka-Volterra type models and random matrix theory (including Robert May's results) were very insightful. The possible links to Otto Cordero's empirical results were exciting to see. Later Bobby and I discussed the possibility of of including temperature and size-scaling effects, and extensions of the model to include phytoplankton as well. These papers are relevant from this perspective:
* https://www.biorxiv.org/content/10.1101/524264v1.abstract (An analysis of thermal responses of bacterial and archaeal growth rates)
* DeLong, J. P., Okie, J. G., Moses, M. E., Sibly, R. M. & Brown, J. H. Shifts in metabolic scaling, production, and efficiency across major evolutionary transitions of life. ''Proc. Natl. Acad. Sci. U. S. A.'' '''107,''' 12941–12945 (2010). (size scaling of microbial metabolic and growth rates)
* Tang, S., Pawar, S. & Allesina, S. Correlation between interaction strengths drives stability in large ecological networks. ''Ecology Letters'' '''17,''' 1094–1100 (2014). (example of using metabolic constraints to parameterize random matrix theory/model).
== Pamela Martinez ==
Intriguing results about inconsistency between data and inferences that have been drawn in the past about the efficacy of antibiotics. This paper might be interesting/useful:
* Cruz-Loya, M. ''et al.'' Stressor interaction networks suggest antibiotic resistance co-opted from stress responses to temperature. ''ISME J.'' (2018). doi:10.1038/s41396-018-0241-7
== Annette Ostling ==
The clustering of trait values on nice axes is a cool result. I think constraining the Niche-Neutral assembly model's parameters using ecological metabolic theory (especially, size-scaling) would provide further insights, and could lead to more precise predictions about the clustering of traits. I know Annette has published one on neutral theory constrained by size scaling (O'Dwyer, J. P., Lake, J. K., Ostling, a, Savage, V. M. & Green, J. L. An integrative framework for stochastic, size-structured community assembly. Proc. Natl. Acad. Sci. U. S. A. 106, 6170–5 (2009)). More to do along those lines, especially as competitive interactions too are strongly determined by size scaling and thermal responses. Our recent paper on phytoplankton competition is relevant:
* Bestion, E., García-Carreras, B., Schaum, C.-E., Pawar, S. & Yvon-Durocher, G. Metabolic traits predict the effects of warming on phytoplankton. Ecol. Lett. 21, 655–664 (2018).
== Greg Dwyer ==
Fascinating study -- remarkably detailed modelling and model fitting to data! Raised in my mind again the (seemingly eternal) question question that biologists face about models: specific or general? The problem of Fungus vs Virus would benefit by characterizing their temperature-dependence ''in vitro''.
== Otto Cordero ==
Very interesting study. Particularly useful to me as my lab is increasingly focusing on microbial ecosystem dynamics. The idea of using artificial nutrient particles/spheres is really innovative! The repeatability of bacterial community/network assembly / succession / turnover is striking. The low efficiency (~20%!) of uptake/use of metabolic byproducts was interesting to hear about -- looks like diffusion/turbulence/mixing plays a big role. Makes me wonder about the effect of turbulence/mixing on these dynamics (something we are particularly focused on in our modelling). The fact that early succession bacteria are more motile was also very interesting. Some of the detail about strategies adopted by generalist bacteria was particularly interesting.
== Priyanga Amarasekare ==
The Hawaiian Tree-creeper example was a great start to open a real debate! I guess the question is about irreversibility of timescales -- given enough time, is a reversal of beak morphology really impossible? I think the evidence for traits such as attack rates, which occur and are measurable at short timescales have a less right-skewed was not quite convincing. I don't quite understand why attack rates should be hormonally regulated - the onset of foraging by a consumer may be hormonally regulated, but once a consumer is foraging, the interaction rate should be under biochemical (enzyme kinetic) control. However, I do agree that the temperature-dependence of certain rates/traits that are the result of an organismal process integrated over a longer timescale, may have a different, potentially less right-skewed response because of hormonal and other type of regulation. Worth doing a detailed analysis, using a wider range of organisms, I think. the new version of BioTraits would be suitable for this. I have invited Priyanga to participate in this year's VectorBiTE meeting ([http://vectorbite.org/ vectorbite.org]) in Italy which I am co-organizing, where we could discuss this further and perhaps undertake such an analysis.
== Fernanda Valdovinos ==
Very interesting model with interesting results! I found it strange that plants can produce nectar without cost. Perhaps as Fernanda said, this is a negligible factor, but would have been good to see some evidence for this, and some exploration of the model's structural robustness. But the approach of modeling the rewards as a separate pool with its own dynamics and imposing adaptive foraging altogether provided me with interesting new insights. I think that using movement biomechanics for bounding the interaction/visitation rates of pollinators would be worthwhile.
== Dervis Vural ==
Very cool work. I absolutely agree that a mechanical approach towards understanding microbial interactions and evolution/co-evolution is the way forward. I raised the point (maybe too many times!) that organismal properties (locomotion) and environmental temperature need to be added to such modelling/theory. Also, why no turbulence? But overall, I found the results really insightful. I agree with Dervis' idea/claim that a general theoretical framework that allows the physical environment's properties to constrain interactions (and their ecological/evolutionary outcomes) within and between populations is possible and necessary. This is also the message I was trying to deliver in my talk.
== Jacopo Grilli ==
Much-needed formalization of higher-order interactions, with compelling results. Would need some work to reconcile/test with empirical data, but a important step forward, I thought. The issue of indirect (e.g., trophic cascades) vs higher-order interactions (e.g., modification of a pairwise interaction by a third agent) came up. There is considerable confusion in the literature and even among us as to what the two terms entail. Indirect interactions are not the same as higher-order interactions, but ecologists very often use them interchangeably. There is a recent Ecology Letters paper that also tries to get at the distinction between the two:
Terry, J. C. D., Morris, R. J. & Bonsall, M. B. Trophic interaction modifications: an empirical and theoretical framework. Ecol. Lett. 20, 1219–1230 (2017).
A
It was exciting to participate in an interdisciplinary discussion of physics, dance, and music. I was also intrigued to hear a great dancer and choreographer talk about their art, which often exceeds language in favor of an embodiment of ideas. That reminded me of music, which makes its arguments sonically. It was particularly interesting to hear the ways in which our response to specific questions regarding time shifted depending on our training and our disciplines. We discussed how rhythm, tempo, and meter affect how music is perceived in time, whereas a series of movement events or a movement process can suggest time in dance. (Process kept returning as a theme for all of us.) John spoke of how entropy plays an important role in the directionality of time.
After a long discussion on our approach to the material, I now feel like John, Karole, Jock and I have a shared collaborative vocabulary to discuss the project going forward. I also have a better sense of Karole and John’s initial inspiration for this work and how music might support that vision. Specifically, I am hoping to generate music that is the result of a collision of various musical processes. My hope is that this approach will resonate with Karole’s movement-based experiments that seek to dramatize the collision of two types of time. +
P
It's clear that an extended version of this course should include treatment of inequality (and more generally the distribution of the benefits and costs of environmental impacts within societies) and conflict between and within states. The #1 highlight is of course the group of people assembled here. +
Loved learning how demographers, philosophers, anthropologists, economists, ecologists, scientists... approach the subject, and the breadth of work taking place from these different fields.
I was struck by the need to be able to produce decent estimates of the return on investment of family planning. +
Many thanks for the most wonderful opportunity to learn from and engage with an incredible group of researchers. It would be preposterous to limit the list of things I've learnt to one. C. Cowie's presentation on population axiology has opened up an area of inquiry that I was not even aware existed. M. Shenk's paper was probably the clearest introduction to demography I could wish for, similarly to L. Hunter's talk on migration. All the talks on mathematical underpinnings of some of the questions raised (and especially S. Levin's) were pitched at such a low level that even non-math folks like me could follow (THANK YOU!) which is much appreciated. I could go on but shifting to the 'how are you planning on using what you've learnt?' question. The socio-natural model presented by C. Lee has a good potential for being an absolute game changer for my research. It really opened up new avenue for linking environmental variables to demography that I was not aware of previously. Stay tuned! +
H
Meeting participants have had some productive disagreement about what exactly defines aging. Is it the entirety of the change that occurs between birth and death or perhaps beyond, or just a subset of the changes that occur during one's life? Two main categories of change occur during life: one category is a sequence of programmed developmental milestones including embryogenesis, puberty and menopause. In semelparous species, even death can be viewed as a programmed developmental milestone. Another category of change is deleterious degradation of function, manifesting as cancer, heart disease, weakening of physical strength and life-sustaining activity such as predation, and even increased susceptibility to infectious disease. Some, including Roz, argue that only the second category of change should really be called aging. However, it can be hard to prove that any type of age-related decline is truly random rather than programmed.
In the classical view of aging as the breaking down of the body, participants make use of analogies involving the breakdown of man-made machines, e.g. the failure of a one-horse shay or Henry Ford Model T. The one-horse shay is a machine rooted in folklore that is perfectly efficient because all components fail at once. To the extent that human bodies fail to disintegrate at once like the one-horse shay, are we maladaptively wasting resources on our slower-to-fail organs? Or is longevity more of a neutral side effect of evolving bodies that are robust to the challenges we may encounter during our reproductive lifespans?
Barbara's work challenges the mechanical breakdown view of aging by comparing physiology between species and showing that age-related "degradation" can sometimes be an adaptive response to a stress that can in theory occur at any age. For example, age-related thickening of the heart ventricle is a rampant cause of human death today, but it is physiologically rooted in a type of phenotypic plasticity that can help a young animal adapt to high blood pressure and still live to reproduce. This suggests that when we die of old age, we are dying of the most severe negatively pleiotropic side effects that inevitably accompany adaptations that outweigh the cost of dying in middle or old age.
James challenges the view of aging as random mechanical breakdown in a different way than Barbara does. He mainly focuses on the cancer mode of death and the random accumulation of cancer driver mutations as a particular mechanism of breakdown. In the classical breakdown view of cancer, oncogenes are essentially ticking time bombs that have a constant probability of mutating each time a cell divides. This implies that everyone will eventually get cancer if they live long enough, with an exponentially distribution of ages at incidence. However, James notes that there is no appreciable difference between 20-year-olds and 30-year-olds in their probability of dying of cancer, whereas in the exponential mutation accumulation model, the difference between these age groups should be comparable to the very significant difference between 60- and 70-year-olds. To explain this violation of the simple exponential health decay model, James proposes that a breakdown in the cellular environment occurs during middle age that allows precancerous cells to proliferate in a way the same cells cannot do in younger tissues.
Morgan's work on the epigenetic clock outlines one way in which aged tissues are different from young tissues ''en masse''. She has identified a set of CpG sites that are differentially methylated between young and old individuals and whose methylation state predicts mortality slightly better than calendar age does. From her presentation, I couldn't tell whether young individual had less variation in methylation status than old individuals at these sites. In other words, does aging cause decay from a deterministic methylation state toward a random state, or does it look more like a programmed transition from one state to another? To the extent that methylation is decaying toward a random state, mechanical breakdown seems like a better analogy, but if the aged state is as low-variance as the young state, programmed developmental transition seems closer to what's going on.
C
Much of the emphasis was placed on describing the necessary basic principles, models or data, for describing brain functions.
These included:
# Resting state correlations from imaging data
# Behavioral psychological experiments
# Local field potentials
# Deep neural networks
# Information theoretic formalisms.
Much emphasis was placed on either justifying or discovering appropriate levels for prediction and explanation. On this topic;
# Is there a preferred level based on fundamental principles?
# How to reconcile computational models (with strong time separation) with dynamical systems models (with a spectrum of time scales)
# How to present and justify theoretical frameworks with many free parameters - theory for complex systems (in contrast to mere complication as in physics).
# How to triangulate among levels of description
My own question dealt with the general problem: does the fact of the brain as a computational organ imply distinct regularities in the way in which it breaks?
One approach to this would be to ask about:
# Robustness and adaptability
# Critical transitions: order disorder regimes
# Cascading failure and percolation.
This triplet provides a possible informal coordinate system in which to situate a system to include the brain. The rather unique scale and connectivity and general function of brain might suggest that it sit near a critical point, balanced between robust and adaptive regimes. +
My brief presentation (I didn't give a talk) focused on the concept of “compensation” in cognitive neuroscience of aging and dementia, and the difficulties of interpreting patterns of changes of brain activity or connectivity as compensatory. I emphasized the need of linking these changes both to a deficit and to enhanced behavior, and the importance that the latter link is established at the intra-individual rather than the inter-individual level.
The meeting was extremely interesting, particularly because it allowed exchanges between researchers with very different perspectives, which doesn't typically interact in standard scientific meetings. I found particularly exciting the idea of generating a theory of how the brain brakes that is not limited to one particular level of neuroscience analysis (e.g., molecular, cellular, systems) or one particular disorder or pathology.
The meeting reminded me of a conference I helped organized in Montreal in 2017,in which the goal was to clarify terminology (such as the term "compensation") rather than just presenting new data. As in this meeting, we also worked with a small group of researchers, without an audience, focusing on thinking rather that on just presenting and seeing new data. +
I
My two questions:
'''1) How can we incorporate analyses of non-equilibrium dynamics *and* be able to make general theory?'''
'''2) How often do ecological feedbacks results in bi-stable evolutionary states?'''
Following up on my first question about non-equilibrium dynamics: We had a discussion of how to analyzed and communicate these kinds of results in publications. It seems that there is a missing set of tools to be able to categorize and communicate these kinds of results.
Pamela Martinez:
One of the ideas here was that frequency dependent selection could lead to coexistence, but also that this coexistence was complicated by environmental variability. The model fitting approach involved modeling both process and observation error, and some of the results were consistent with relatively constant total levels of infection even while reported cases could still vary.
Robert Marsland:
Some of what I was particularly interested in from this talk was liking the May type stability analysis to some sets of more mechanistic models. It was really interested in the possibility that the transition between communities that allowed as many species as niche to coexist and as the noise level goes up then the system transitions to maintaining only half the species. +
D
Notes from day 1
In general I think we need to have the core theory group come up with terms and definitions that would be used consistently across all the working groups so words like robustness, resilience, homeostasis, energy, etc are understood to mean the same thing in different topics.
Regarding the challenges to resilience - some recent work by Eve Marder is relevant. She has some knockouts in the crab STG that have wide molecular variability but produce the same phenotype - except under conditions of environmental variability. One of t(e conditions she looked at was temp since crabs in the wild naturally experience a range. The KOs crashed at different points - so stressors can separate out variants that would not be seen under controlled conditions.
A question I had was differentiating homeostasis from resilience from persistence from perseverance.
I also though it might be interesting to look at how ideas, fields, disciplines, and academic institutions age?
I like that Dervis raised the issue of how systems break - this linked back to the Breaking Brains working group.
I may be making a spurious connection here but the idea that you need 65 % of nodes to avoid failing reminded me that whole brain energy metabolism has to be about 65% to maintain conscious awareness.
Peter’s talk also made me think about what cells we should be paying attention to in the brain - so much of the focus is on neurons but what do we know about the aging of the heterogeneous cells in brain tissue?
Another challenge we have is going back and forth between general theories and empirical case studies. At some point in the future be nice to test theory with very specific case study data. +
Noteworthy concepts and questions:
#Ravi: "Gerontropy". In additional to directional changes in health indicators do we get an increase in variance?
#Alfons: Multiscale approaches. Do multiple length and time scales really matter when they are separated? Can interdependence network approach be improved to take into account hierarchical structures of organs/tissues/cells/molecules?
#Ravi, Chhanda: How can theorists make themselves useful for NIH? How to communicate "theoretically driven" projects to NIH?
#Chhanda: Resilience builds up over time. Effect of early life history on aging. Comparative biology approaches e.g. naked mole rat
#Ravi, Chhanda: Very interesting plasticity effect: Physiological state does not come back exactly to the same point after perturbation. A theoretical description of physiological elasticity vs. plasticity
#Ingrid: Idea on multiple tipping points that are coupled. I recommend checking out Kramer's escape problem. Chhanda excellent question: Are young ecosystems more resilient, just like young people. Alfons had an excellent question: what can you say about the dynamics by knowing only qualitative causal relationships. An idea: if there are multiple models describing the same subsystems and their interactions, can these be combined/reconciled to get a result more accurate than all models individually?
#Peter: Potentially useful model but one should be careful about drawing conclusions from single runs. e.g. Flipping coins would also yield similar ups and downs if one looked at individual runs. It would be good to check if the model gives Gompertz (exponential) mortality curves or Weibull. I would also have critical questions about sensitivity to parameters and system size, i.e. if the damage rate was close to repair rate I suspect that the system would never collapse (given large system size).
#Heather. Very interesting conceptual graph derived from a real patient where multiple systems failing at different times at different rates. This resonates with Chhanda's observation that resilience is not one thing, but a multi-dimensional vector.
C
One discussion that emerged after the talk was the question of what the right level of analysis was for understanding brain dysfunction in aging and PD, and what form of causal argument or mechanism can be derived from these network descriptions of brain organization and dysfunction. A very interesting direction to go would be to create more theoretically driven models of brain dysfunction in PD, that might explain the disconnect between the functional network effects and known pathology in the disease. These models could then be tested in future experiments. +
A
Outline
A. What is aging? Classical definition from Medawar aging is passage of time and aging is the deterioration of function with time. Aging and senescence is nowadays used to mean equivalently the deterioration with time. We will used aging and senescence in this more contemporary context, and refer to the passage of time specifically as chronological aging. However, because in some organisms the deterioration can be reversed, we will describe those instances as reversed or positive senescence or aging (discuss?).
B. Following Medawar, we also can distinguish aging that results from wear and tear from interactions with the environment much as a automobile parked by the ocean will rust and fragment. However, because the hallmark that distinguishes physical objects such as a car and a biological organism is the latter's ability to change through evolution by natural selection, aging can be accelerate in living systems beyond physical wear and tear. The acceleration results from the production of asymmetrical daughters by dividing mother cells. While the asymmetry can result from a combination of factors, some beneficial and others deleterious, a possible cause may be damaged cellular molecules and organelles. The daughter that receives more damage ages and the other rejuvenates. The aging daughter can be viewed as the continuation of the mother, the daughter receiving less can be regarded as the new juvenile offspring. This concept can be extended to metazoans and the asymmetry +
D
Overall, it became clear to me how much perspectives there are from which you can study the subject of resilience and how valuable and complementary each of these perspectives is. I have identified a number of axes along which we can organize each of these perspectives to create some structure:
- Axis 1: from Understanding resilience to Diagnosis and prediction of levels of resilience in individuals.
- Axis 2: from Systemic to Subsystemic resilience(s) (links to spatial scale) and from longitudinal, long term trends to Short-term dynamics (links to temporal scale)
- Axis 3: from (in silico) Modeling of theory to Empircal data collection and validation
- Axis 4: from Between individual to Within individual differences
From this I expect we can think of a number of initiatives we can take together:
- Developing a measurement tool box to capture in a standardized way data we need to empirical study systemic and subsystemic resilience in human aging. This toolbox should begin to identify which core (organ) systems and physiological processes are (based on theory) involved to emerge resilience from. Then we can identify the stimulus-response test, time-series to follow (to calculate multiscale entropy and DIORs from) and crucial outcomes/function to be studied for each of the core systems involved.
- Standardization of perturbation quantification +
I
Pamela's presentation reminded me of the importance of basic competition theory in understanding competition between pathogen strains, specifically in terms of the interplay between frequency-dependent and density-dependent selection in the insect pathogens that I study. That has in turn helped me to begin to see how theory of pathogen competition is related to more general theories of competition, as Priyanga pointed out, and as became clear from seeing Annette's and Otto's and Bobby's presentations.
Something I am unclear on, however, is how and whether such theory of such generality has practical implications for pest control. Those comments apply even more strongly to the whole idea of irreversibility. I can see what Dervis and Jacopo mean by "ecological irreversibility", but I can't see the practical applications. Meanwhile, I can't see what David Krakauer's ideas have to do with killing pests in any way. That said, I can appreciate that I may need to think about all these ideas quite a bit more.
My first 2 paragraphs were based on the first day of talks. Now that the meeting is done, I have 2 more thoughts. Off and on during the meeting, we had long rambling discussions about the semantics of irreversibility. I found much of that discussion to be a waste of time. After the meeting was over, however, we were able to identify metrics of irreversibility, and those metrics will be directly useful in work in my lab.
Questions I would like to know the answer to, and that are motivated by the talks I've listened to:
Is the outcome of pathogen competition irreversible, or can it be reversed by climate change?
To what extent are high-level abstractions useful in understanding ecological problems, and in applied ecology more specifically?
Are statistically robust tests of ecological theory necessary for the theory to be useful? +
C
Presentation highlight was about AI techniques, didactic, informative and comprehensible - thanks Nikolaus Kriegskorte.
There was tension between model and data led approaches.
I had a relatively stable view of the methods by which functional and structural imaging mao to anatomy and local function in human brains. Those views were not shared, which meant a rethink is required. I remain unconvinced by what the resting state can inform us about mapping function and structure +
A
Really interesting set of talks that blended into a good set of discussions on projects the group could work on. There will be a big emphasis on human immunity and how it first gains 'experience' and then breaks down with age.
I'm likely to focus my attention on developing body sized scaled models for immune system. These could be both fairly simple models for immunity mainly capturing differences between Type I and Type II immunity, but then expanding this to take Jean Carlson's model for human immunity and rescale elements of this with host body size and BMR. +
C
Recognition of depth of knowledge in related disciplines/communities/clusters.
Recognition of how little we connect between disciplines/communities/clusters.
What disciplines/people/agents are missing from our meeting? What important elements are missing from our model of the system needed to understand/influence failure of the brain network?
How do we maintain communication between these agents/communities in between physical or virtual meetings? +
W
Sleep is a variable state depending on species, developmental stage, available physiological markers:
· Kimberly Whitehead talk: In preterm infants, the active sleep stage fulfills a development function that is distinct from the proposed functions of sleep in full-term infants and adults. Namely to develop the somatosensory maps in the brain for processing external inputs like the barrel formations in whisker barrels.
· Jerry Siegel talk: Sleep in different species doesn’t show same physiological markers as in usual lab animals and humans. Dolphin and killer whale unihemispheric slow-wave sleep – is it the same as human sleep? Brown Bat sleeps ~20h per day – is that the same state as other animals? Can we relate all animal “sleep” states to human sleep states?
· Summary: the question of what is sleep is not fully answered. Sleep is different in different life stages and different species
What are key physiological processes that contribute to sleep timing, duration?
· Jerry Siegel talk: Body temperature has large effect on sleep durations, especially REM sleep occurrence. Should be considered in models
· Beth Klerman talk: Circadian rhythm acts to consolidate sleep and wake episodes. At end of the wake episode, circadian rhythm generates a wake-promoting signal, the no-sleep zone. This acts to keep us awake longer so that when sleep occurs it lasts longer and is more consolidated. Similarly, the circadian sleep-promoting effects are strongest at the end of the sleep period, so that we stay asleep longer and the subsequent wake episode will be more consolidated. Also has new paper coming out in J Biol Rhythms on the phase shifting effects on circadian rhythm of very short light exposure in the dark period
· Cecilia Diniz Behn talk: Signalling of SCN to sleep-wake centers may not be 1-dimensional. In rat, requires both sleep-promoting and wake-promoting effects to account for SCN lesion data. In squirrel monkey, can be 1-dimensional effect perhaps because homeostatic time constants are longer relative to circadian rhythm than in rat.
Role of EEG signatures of sleep, such as spindles. Ripples, slow oscillations, theta range oscillations, depends on developmental stage and type of EEG event
· Kimberly Whitehead talk: In neonates, twitches are initiated in periphery and cause a spindle-burst that propagates throughout the somatosensory cortices. This is a signature of propagating activity throughout brain regions and contributes to constructing the neural map of the sensory system. In neonates, sleep oscillations subserve sensory cortical organization
· Sara Aton talk: Different oscillations occur in cortex and hippocampus in different sleep states. In NREM, have slow oscillations (different than slow waves) in cortex and have sharp waves and ripples in the hippocampus. In REM, cortex shows asynchronous activity and hippocampus shows theta oscillations. Oscillations are the substrate for temporal coding of information. The relative phase of cell firing to the oscillation peak sets the temporal frame for temporal coding. A rate code during wake and shift to a temporal code during sleep oscillation with most excitable cells firing first during the oscillation. The temporal code during sleep oscillation also support appropriate patterns of synaptic plasticity for memory consolidation.
H
Some ideas I had in response to talks and conversations:
Morgan said something about organisms continuing to age post death. There might be work to support this in Drosophila and demonstrate that it is under evolutionary pressure. Dan Hultmark made an argument that the fly's immune system wasn't good at fighting pathogens, rather it just prevented the fly from turning into compost before death as it defended against the microbes that decompose the dead fly.
Daniel mentioned "The secret lives of trees". Sometimes it it useful to look at fiction that explores these ideas to see what could happen if you don't worry about having to do the experiment. If that sounds interesting there are a couple of novels worth reading. Powers wrote "The overstory" that deals with plant interactions with other plants and humans in which the plants are the main characters. Likewise, "Semiosis" imagines humans colonizing a planet where the plants are far more intelligent than the humans and manipulate the people.
I finally got an explanation as to why I haven't been able to see critical transitions in my data. It looks like my trajectories are too dynamic and multidimensional for this to work, which is good to know because it was going to be very difficult to gather data and the necessary rate.
Someday I would like to see a method of showing how a network can evolve over time. I'm not sure of how to do except by showing a movie. It would help me understand how the connections in a network change with age.
I'm wondering about social interactions beyond loneliness that could affect aging. Are there social behaviors, that are the equivalent of monkeys grooming each other that humans perform that can increase resilience? +
P
Some of the highlights of the meeting for me were:
Chris Cowie's detailed presentation of the consequences of various axiomatic assumptions about how to make decisions affecting entire populations in terms of the two dimensions of welfare and population size. This type of thinking is nice in that it forces people to explicitly express their preference regarding different types of outcomes and understand the tradeoffs therein. The discussion that followed between Simon Levin, Chris Cowie, and Partha Dasgupta regarding the ultimate moral responsibility to unborn children was fascinating, and touched on some of the deepest moral philosophy questions. Namely tradeoffs between responsibility to self, society, existing children, and potential children where the decision to have an unborn child is connected to which of these categories of welfare one is weighting most strongly, and what one expects the future condition for the child, self, and society to be.
Caroline Bledsoe's discussion of the variety of husband perspectives on contraception across multiple wives was fascinating, highlighting the stronger connection to individual relationships rather than blanket opinions. For example, if a husband viewed contraception as a means for an individual wife to recover from child birth and delay the next birth lead generally to a receptive perspective of contraceptive use. This work connected strongly to Aisha Dasgupta's plot of a negative correlation between fertility and contraceptive prevalence across countries, where outliers in fertility at the same contraceptive use may indicate detailed cultural processes.
Mary Shenk's overview of the demographic transition and contrasting of humans with other primates was also very useful for understanding the broad-scale history of human populations. +
Population and the Environment: Analytical Demography and Applied Population Ethics/MarcelGMOldeRikkert +
Test reflection +
A
The discrepancies what aging entails seems to be related to the difference of fields and questions tackled. To me as an evolutionary biologist aging is simply the process of senescence, where senescence is the deterioration of function, or more precisely the change of function with age. This change does not need to be a directional decline. Function should be somewhat related to fitness, which explains that survival and reproduction are first targets to quantify aging, though all functional traits could be and should be considered for understanding senescence. However if fitness is the parameter that integrates the processes, it is evident that a cell within a multi-cellular organism has a different definition of fitness than a whole organism in itself, be it unicellular or multi-cellular.
The generalities as described by Chris are highly interesting and inspirational. I gained much inspiration on how senescence is unified across cells of different level of biological organization, but where, how, and why these universal patterns fall apart is something I would love to deepen discussing.
The differences in heterogeneity and homeostasis among cells that has been shown by Sri, where I was really puzzled how similar cells are and how such similarity could be maintained, and Bree's system where the heterogeneity is large, though spatially still well structured.
What are the most prominent markers that we should focus on? How can we measure these? +
D
The first day consisted of an excellent range of thought provoking presentations. An important issue which was raised was the need for further discussion on the usage of words such as robustness since it may have been used in slightly different contexts by different speakers. Also the group discussions seemed to focus on the characterization of resilient vs. non-resilient (vulnerable) individuals. There too some clarity is needed. It is likely that people will differ in their resiliencies and vulnerabilities to different stressors and display different degrees of resiliencies. Thus tests of resiliencies at any level, should consider graded tests (i.e., stressor is applied at diff intensities, magnitude, duration) so that such tests can distinguish between different degrees of resiliencies. This would be especially pertinent to assessing changes in the degree of resilience over time (e.g., longitudinal studies) and especially for the testing of potential therapies intended to improve resiliencies. +
P
The interrelated topics of population growth and resource depletion are central to sustainability, and ideal topics for SFI
Potential for greater integration of conceptual foundations and applications is high; these are prototypical complex adaptive systems, and problems of the Commons are at the core as regards resource use, disease management, etc.
Would like to see even more-post-meeting integration of these topics.
I may work more on migration. +
The meeting has really emphasized the vast potential (and need) for interdisciplinary collaboration in the arena of population and environment. I have long been engaged with the social demographic research community focused on environmental demography, but we have not sufficiently bridged to those with expertise in anthropological or analytical demography nor with those in population ethics.
An important open question for me is "How do we make our research more policy relevant?" With the recent imperative from the IPCC, the research community must come together to generate impactful, meaningful insight that can help in identifying and prioritizing policy and programmatic response -- now.
On the prospective of shifting my own perspective, I don't know that my perspective has changed, but I certainly have greater appreciation for, and understanding of, the myriad ways in which scholars are thinking about population issues, including as related to philosophical questions around population ethics.
I can imagine that this workshop pushes me to more centrally engage anthropological demographers within my own work. Dr. Scott Ortman is an affiliate of the University of Colorado Population Center, for which I'm Director, and I can now better see the potential to consider commonalities and distinctions in population-environment linkages across long periods of time. Key, though, is I would aim to engage this work in ways that would yield impactful findings as related to our contemporary demographic and climate challenges. +
C
The meeting was illuminating in a number of regards. In addition to the new content knowledge, the framework of emergence/causality/time/complexity was of great interest and utility. In terms of specific knowledge that will inform my future work, the limitations of DTI as a metric for human structural connectivity was important to learn. Also, the lecture on critical dynamics was- in my opinion- important in linking scales from neuronal spike activity to large scale networks. Criticalitycan potentiallyfunction as a surrogate for optimal "health" in the system and distance from criticality can potentially function as a surrogate for "disease." +
P
The most useful part of this short course for me was learning the high level perspectives of experts outside of my field. As a systems science PhD student I am interested in studying social-ecological systems and how we as a society are going to adapt to a changing climate, resource depletion, population growth, energy transitions, etc. My personal interest is in implementing circular economies at the community level (neighborhoods, towns, etc) and so I found Chris Kempes' "Ecological & Metabolic Population Constraints" and Charlotte Lee's "Environment, Food Supply, & Demography" presentations particularly relevant to my work, and I am interested in utilizing some of their findings into the development of agent based models of local economies. Additionally, I have not had significant exposure to demographics, fertility, and migration in recent memory, and so I now have plenty of additional information to consider when designing models or community interventions. And lastly, the "Anthropogenic Change & Biodiversity" talk by Andy Rominger and the closing thoughts by Partha Dasgupta reinforced my understanding of the larger problems at hand and provide the larger context for the smaller investigations and projects that I am involved in.
Insofar as how I plan to use this knowledge, over the past several years I have formed the equivalent of what physicians would call a "general impression" of the great changes that are taking place. The content of this course has largely validated my differential diagnosis and has enriched my understanding by providing details backed up by rigorous research. As I move forward with my academic and professional work I will undoubtedly refer to my lecture notes and dig deeper into the research papers cited. And, although I will not go into detail in regards to a number of interesting conversations during the course, suffice it to say I am delighted to now be a part of this intellectual network and I plan to connect with several students and faculty for further conversation and collaborations.
C
The take home message for me was that the principles of how the brain is designed that makes it unique and similar to other complex systems. Some the terminology/features in complex systems can, and should, be applied to the brain, but how these features are realized may be unique to the brain. There are many methods used in empirical neuroscience that can provide a springboard to this, such as graph theory metrics, coherence measures, etc, but these should be conceived in the complex systems framework to how they support features like robustness, criticality, and cascading failures.
The challenge will be to establish the common dialogue to build this bridge and the technological foundation to support it (e.g., modeling platforms for deep learning, dynamical systems that can take the empirical data as direct constraints). +
D
The talks today brought out insight into the theory of resilience, based on historical concepts of aging. The focus was on connecting these concepts to human clinical conditions, and how to measure resilience based on response to artificial as well ass naturl sressors
Several specific questions are of interest. The concept of protective factors was presented but how these protctive factorws would actually be measured is of great interest. A second question is the challenge of how to define the variation that would separate out resilience snd nonresilience. A third question is hwo to address the epigencitc impact that environemtn might have on resilience, or lack of resilience.
The multiscale modeling concept is of interest to apply to mouse studies, since it could enable preliminary study data with a more structured format that would provide more translational impact. It would be especially of interest to pursue multiscale sublevel interactions in relation to data already generated to see if future effort would be productive.
One of the points of the second day was the global view of social networks and how interactions and connections could be viewed as resilience models. In addition, clinical and preclinical presentations were made that showed ways of aligning more naturally occurring stressors with stress situations at the population or ecosystem levels. An indepth discussion on how to develop markers of resilience in humans was very productive, but uncertain what the next steps will be. More discussion after my talk on mouse modeling was informative as to the potential of applying specific stressors to predict resilience to aging in mice to clinical situations. Dual tasks assessments in people are currently being done to determine risk for such things as Alzheimers dementia, and other age related conditions. A focus should be to to connect these with other healthy aging paramters to determine variation and risk for developing age-related conditions.
I
The work on vaccines presented by Pamela clearly makes a connection across working groups. As the role of immune function and aging become increasingly of interest be interesting to think of how the effectiveness of vaccines changes across life spans - including of the vaccine and those vaccinated. +
D
The workshop was a good mix of theoretical and practical insights on resilience in human aging. I see a lot of parallels with the work we do on ecological resilience, and I think we could learn a lot from each other. One of the things I noticed is the different definitions of resilience used (and other semantics), which may become confusing. It would be good to make an overview of the different definitions, and to not invent new words for the same concept.
To really get a proper understanding, and to develop well-grounded indicators of the system dynamics and resilience, a combination of the different presented methods would make a lot of sense. This ideal path in my opinion would be 1) for each 'sub-system', to develop a solid idea of the large-scale feedbacks and dynamics, and develop a mechanistic model based on that, taking into account temporal and spatial scale. This should lead to some idea on the stability, and dynamics of the particular sub-system: is the sub-system expected to have alternative stable states/ tipping points/ oscillating dynamics/ chaos/ flickering/ spiraling? 2) This basic understanding should lead to hypotheses on what type of indicators of resilience could be useful and realistic (e.g. perform stress tests, measure DIORs, potential analysis etc) 3) These hypotheses should be tested both in the field, and in more realistic, fully parameterized models, as presented by Alfons Hoekstra.
I enjoyed the talk by Porter a lot. On one hand, it touched me personally, to see how resilience these communities can be after so much suffering, but also it reminded to think about what makes a system more resilient? We talked about feedbacks a lot, but not so much about other relevant factors, such as functional redundancy, response diversity, and connectivity. A vary obvious example of functional redundancy is, I think, the two kidneys a human being has (you can survive without one).
In my opinion, the mouse models, while a mouse is not a human being, can be extremely useful to get a grip on the coupling between subsystems, and the way we could approach the resilience questions in human beings.
The last discussion was interesting, because Sanne Gijzel pointed to an example case, in which several sub-systems failed in a row. I think we can learn a lot from these type of examples (also the example of Heather Whitson) about the coupling between subsystems.
There was fascinating information presented during the first day. My background is not in the field of geriatrics or human aging so I learned a great deal during the course of the day. I was particularly interested in systems theory and its connection with aging and stress factors. This information resonated strongly with my own thoughts regarding human societies and cultures through time. My own interest is in social systems and I wonder what these findings mean for aging within these systems. How do specific social systems promote or degrade resiliency? What is the role of culture in the study of human aging? How often are cultural differences considered in bio-medical studies of aging?
The presentations during the second day gave some practical insights into resilience. The presentation of ecological models by Ingrid Leemput were useful analogues in thinking about human aging and resilience. I cannot help but think about how human experiences are often extracted out of ecology and perhaps vice versa. How do perturbances in our environments impact human resilience in multiple ways? Heather Whitson's real-life example resonated with many of my own experiences and made the idea of resilience much more visceral. I appreciated the discussion of mice aging in comparison with human aging. Overall, lots of food for thought. +
P
This has been a great meeting with many good ideas and excellent people. I was left with several thoughts: populations in wealthier countries have lower fertility: why? This was from Mary's talk and is really fascinating. In particular I wonder how economic pressures versus cultural pressures drive this. In my very naive view I can mostly think of cultural reasons--cultural pressures that empower women and change symbols of status away from family size for example; and the economic pressures would seem to work in the opposite direction: it should be economically easier to have more children in wealthy countries, connecting to observations Partha presented earlier. And yet, Mary's work points to economic drivers being more statistically supported--I'll be excited to engage with her findings more. Also, again born of my ignorance on the subject, I wondered when we speak of morality around populations, how do we avoid arguments that facilitate (while not explicitly being) eugenic views on who should reproduce and who should not? If evolution drives populations to higher fitness can fitness maximization be a moral construct? +
This has been a very productive meeting for me. At first I thought "I don't do environmental work, so what do I have to contribute to this course?" But I was interested in the topic so I decided to participate, and it turns out that there are many interesting intersections between my work and that of other participants who are more directly focused on the environment. I have also found an environmental perspective embedded in my own work that I have been able to make more explicit as part of my presentation for this workshop. In terms of professional outcomes, I have already developed one new potential collaboration relevant to human population and demographic transitions in the past and an idea for a future workshop. +
