Property:Reference material notes
From Complex Time
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"The Minimum Environmental Perturbation Principle" (Marsland et al. 2019) is some new work I didn't include in my presentation, but which is possibly more relevant to the irreversibility theme. The main result is that a wide class of niche models exhibit monotonic increase in the environmental perturbation under successive invasions/evolution. I would love any feedback from the ecologists about references to add, things that are unclear, etc.
Marsland and England 2017 and Marsland ''et al.'' 2015 contain in-depth explanations of the two kinds of “thermodynamic” irreversibility I wrote up on the board.
Mehta ''et al.'' 2018 begins with a discussion of the "bias/variance tradeoff", which is extremely relevant to the use of models with many parameters to make predictions. It also has a section on dimensional reduction and clustering that might be useful to people working with high-dimensional phenotype data. (Note that the wiki didn't allow me to enter the whole author list, which should also include Marin Bukov, Charles Fisher and David Schwab.)
Momeni ''et al.'' 2017 shows some of the ways in which Lotka-Volterra can fail to capture the population dynamics of a generalized class of consumer-resource models.
Fisher and Mehta 2014 shows how both niche and neutral regimes can arise in Lotka-Volterra dynamics with immigration, depending on the parameter values.
"Available Energy Fluxes..." (Marsland et al. 2019) contains a full explanation of our microbial consumer resource model in the appendix. The Python implementation can be found at our group github: https://github.com/Emergent-Behaviors-in-Biology/community-simulator.
I have also included the original Human Microbiome Project and Earth Microbiome Project data papers, which contain the large-scale patterns I was showing in the presentation.
Gutenknust ''et al.'' 2007 explains some of the subtleties of Bayesian model fitting in a very accessible way, and strongly influenced the way I think about many-parameter models.
Goldford ''et al.'' 2018 contains some of the patterns I was talking about at the beginning of my talk, which are already captured by a preliminary version of the model.
'''Temperature-dependence and size scaling of Microbial metabolism'''
The question of the temperature-dependence of bacterial metabolism came up in multiple talks. This manuscript from our lab provides some general empirical insights into this: https://www.biorxiv.org/content/10.1101/524264v1.abstract
'"`UNIQ--nowiki-000008A5-QINU`"'This paper on size-scaling might also be relevant to some: 10.1073/pnas.1007783107
'''Natural experiments on effect of temperature on ecosystem structure and function'''
Some relevant papers:
# Yvon-Durocher, G., Allen, A. & Cellamare, M. Five Years of Experimental Warming Increases the Biodiversity and Productivity of Phytoplankton. ''PLoS Biol'' (2015).
# Dossena, M. ''et al.'' Warming alters community size structure and ecosystem functioning. ''Proc. R. Soc. B Biol. Sci.'' '''279,''' 3011–3019 (2012).
# Schaum, C.-E. ''et al.'' Adaptation of phytoplankton to a decade of experimental warming linked to increased photosynthesis. ''Nat. Ecol. Evol.'' '''1,''' 0094 (2017).
# Yvon-Durocher, G., Montoya, J. M., Woodward, G., Jones, J. I. & Trimmer, M. Warming increases the proportion of primary production emitted as methane from freshwater mesocosms. ''Glob. Chang. Biol.'' '''17,''' 1225–1234 (2011).
'''Assembly/Succession/Evolution of microbial/bacterial networks/communities'''
1. Lawrence, D. ''et al.'' Species interactions alter evolutionary responses to a novel environment. ''PLoS Biol.'' '''10,''' e1001330 (2012).
2. Rivett, D. W. ''et al.'' Elevated success of multispecies bacterial invasions impacts community composition during ecological succession. ''Ecology Letters'' '''21,''' 516–524 (2018). +
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* Baker et al. Nature, 2011: Paper from Jan van Deursen's lab on delaying (reversing?) aging by clearing senescent cells in a mouse.
* Zhang et al. Cell Syst., 2016: 3 different models for aging in c elegans with evidence for 2 of the models, from Zach Pincus's lab
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* Braun et al. 2019 formalizes a notion of the energy of brain state transitions constrained by the underlying anatomical network architecture. The study also demonstrates that the energy to persist in a cognitively demanding state is modulated by dopamine and altered in schizophrenia.
* Lynn et al. 2019 develops an analytical framework to study the information generated by a system as perceived by human observers, who collectively process this information in inefficient and biased ways. Our findings suggest that many real networks are constrained by the pressures of information transmission to and among biased humans, and that these pressures select for specific structural features.
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* D. Yamins: This paper lays out the approach of using task-driven modeling to predict neuronal signals, and more generally describes a novel and very different way of thinking about how to characterize brain function using computational models.
* Avena-Koenigsberger et al (2017): This paper is the first that I know of to discuss seriously the relationship between network communication and brain computation.
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* Gratton et al. 2019 Cereb Cortex.
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* McIntosh & Jirsa 2019 present a dynamical systems framework - Structured Flows on Manifolds - that posits that neural processes are flows depicting system interactions that occur on relatively low-dimension manifolds, which constrain possible functional configurations. Such constraints allow us to characterize the actual and potential configurations of brain networks and provide a new perspective wherein behavior deficits from pathological processes could be either the emergence of an existing repertoire or the adaptation of the system to damage.
* Corbetta et al 2018 propose that large-scale nerwork abnormalities following a stroke reduce the variety of neural states visited during task processing and at rest, resulting in a limited repertoire of behavioral states. The emphasis here is on the changes in the dimensionality of brain and behavior dynamics and whether explicitly linking the two would provide a better characterization of the deficits and adaptation following stroke.
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* Meisel et al. 2017 demonstrates that sleep deprivation associated with rapid cognitive decline correlates with a deviation from critical dynamics quantified in the change in long-term temporal correlations or critical slowing down.
* Seshadri et al. 2018: using an animal model for schizophrenia, it is shown that a hallmark of the disease – loss of working memory – correlates with deviation from avalanche dynamics. Memory performance and critical dynamics can be acutely rescued with the NMDA receptor agonist D-serine.
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* Poeppel D. 2012 nicely lays out one of the central challenges of using brain data to understand mind and behavior: the elements of psychological models are incommensurate with brain measurements. Failure to recognize this problem has hobbled cognitive neuroscience and its applications to medicine.
* Huth et al. 2016 (from the Gallant group) shows how high-dimensional functional mapping can be performed in single individuals, and how we can predict individualized functional maps using a statistical model that reflects the variance and covariance of brain anatomy and brain function across individuals.
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* Translation in cognitive neuroscience remains beyond the horizon, brought no closer by claimed major advances in our understanding of the brain. Nachev ''et al''., propose that adequate individualisation, needed for accurate diagnosis, requires models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms.
* A proposal that, in the next quarter century, advances in “cartography” will result in progressively more accurate drafts of a data-led, multi-scale model of normal, abnormal and even adapting, whole human brain structure and function. These draft blueprints will result from analysis of large volumes of neuroscientific and clinical data, by an iterative process of reconstruction, modelling and simulation.
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* Warren et al. 2014 discusses a case where network models of the brain may help to provide information about behavioral disruptions after brain damage.
* Gratton et al. 2018 reviews aspects of the forms of variation available in functional MRI measurements, which may constrain which types of questions different fMRI measures are best suited to addressing.
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* Wittmann M. 2015 is a good review on modulators of time perception.
* The Morandi et al. 2017 outlines a common clinical scenario (acute brain failure) complicating medical care in aging patients.
* Hasenkamp and Barsalou 2012 article puts a systems neuroscience framework over volitional control of focusing attention.
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- Recommended for this course (1 & 2):
1) Lee, CT, and S Tuljapurkar. 2011. Quantitative, dynamic models to integrate environment, population, and society. Pages 111-133 in Kirch, PV, ed. Roots of Conflict: Soils, Agriculture, and Sociopolitical Complexity in Ancient Hawai'i. School of Advanced Research Press, Santa Fe, New Mexico.
''This book chapter summarizes the effort to integrate models for the environment and environment-dependent demography that is the focus of my lecture during the course. It's intended as an introduction to and overview of the dynamic modeling approach--details are there for folks who are interested, but not necessary.''
2) Lee, CT, S Tuljapurkar, and P Vitousek. 2006. Risky business: spatial and temporal variation in preindustrial dryland agriculture. Human Ecology 34 (6): 739-763
''This paper goes into more detail on the environmental modeling and is optional for that reason, but its introduction does a bit better job than the book chapter of setting up the context and larger questions framing the work.''
- Supplementary readings for more detail on other parts of the project (3 - 6):
3) Lee and Tuljapurkar 2008 details food-dependent demographic dynamics when populations are in a phase of long-term exponential growth.
4) Puleston and Tuljpurkar 2008 give details of how demography changes when total land area begins to limit population growth.
5) Lee et al. 2009 examine both growing and space-limited populations with environmental variability.
6) Ladefoged et al. 2008 explains the application of the coupled model to questions about social organization. +
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1) Haworth et al show that heritability of a well-defined measure of cognition (hence related to the vaguer concept of IQ) changes with age. Such studies are more reliable than GWAS modeling.
2) Steiner & me show that there is lot of non-genetic heterogeneity in complex life cycles, and how to compute it
3) Steinsaltz & Evans show that stochastic"reliability" models of complex systems do NOT lead to particular "generic" patterns of failure. E.g., we don't get Gompertz from reliability models. Humans are not cars!
4) Etges et al show that genes act as clustered networks that change with age -- see
Etges, W. J., Trotter, M. V., de Oliveira, C. C., Rajpurohit, S., Gibbs, A. G., and Tuljapurkar, S. (2015). Deciphering life history transcriptomes in different environments. Molecular ecology, 24(1):151–179. +
1) Tenaillon (2014) reviews the insights brought upon by Fisher Geometric model in evolutionary genetics: could it be useful as well for our understanding of aging?
2) Martin (2014) shows how and when Fisher geometric model of adaptation emerges from complex networks of interacting modules
3) Promislow and Moorad (2088) did use that framework to model aging; could it be used to address different questions about aging, dfferent attractors, resilience?
4) tipping points on one side and evolutonary theories of aging on the other have been discussed as distinct frameworks, which should be better connected; how tipping points may be affected by evolution was discussed in a recent review (interested in ecological tipping points mostly); it could be a good starting point to read Dakos et al. (2019)
5) another example of mutation accumulation affecting aging in daphnia in Lohr et al. (2014)
6) still another one on fitness landscapes but combined with data on antibiotic resistance evolution to address questions about how these fitness landscapes change with the environment/stress: check Harmand et al. (2017) +
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Arthur Guyton's computer model of the cardiovascular system
Hummod model, see http://hummod.org
BioGears, see https://www.biogearsengine.com +
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Baker DJ, Childs BG, Durik M, Wijers ME, Sieben CJ, Zhong J, Saltness RA, Jeganathan KB, Verzosa GC, Pezeshki A, Khazaie K, Miller JD, '''van Deursen JM'''. Naturally occurring p16(Ink4a)-positive cells shorten healthy lifespan. Nature. 2016 Feb 11; 530 (7589):184-9 Epub 2016 Feb 03
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=26840489&query_hl=11&itool=pubmed_docsum +
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Borsboom et al. 2019 challenges the idea that reductionist approaches are appropriate for studying complex human neurological disorders and suggests that network approaches might offer alternative conceptualizations explaining dysfunction. Do network approaches offer novel ways to both explain and intervene on “broken” brains? +
Both Newport et al. 2017 and Makin et al. question the idea of pluripotent cortical plasticity early or late in life, i.e, they throw doubt on the idea that areas can take on qualitatively new functions after injury. +
Cabeza et al. (2018) is a consensus opinion paper on of three popular terms in the cognitive neuroscience of aging and dementia, which are all related to the concept of robustness: reserve, maintenance, and compensation. "Reserve" is defined as the cumulative improvement, due to genetic and/ or environmental factors, of neural resources that mitigates the effects of neural decline caused by aging or age-related diseases. "Maintenance" refers to the preservation of neural resources, which entails ongoing repair and replenishment of the brain in response to damage incurred at cellular and molecular levels due to ‘wear and tear.’ Finally, "compensation" refers to the cognition-enhancing recruitment of neural resources in response to relatively high cognitive demand.
Cabeza, Stanley, and Moscovitch (2018) argue that, compared to large-scale networks, cognitive theories are easier to relate to mini-networks called process specific alliances (PSAs). A PSA is small team of brain regions that rapidly assemble to mediate a cognitive process in response to task demands but quickly disassemble when the process is no longer needed. +
