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Dynamic Multi-System Resilience in Human Aging/Emergence of Aging in Natural and Synthetic Multicellular Structures

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November 12, 2018
10:00 am - 10:40 am


Dervis Can Vural (Univ. Notre Dame)

  1. Evolution of interdependence. Neutral constructive evolution ties genes/cells/tissues/organisms together. A random walk in the space of networks leads to the most likely arrangement: random networks.
  2. Statistics of catastrophes in interdependent systems: Gompertz law, dynamics that are qualitatively independent of network structure and model details.
  3. Aging in synthetic tissues. Cells die at a slower rate when allowed to exchange goodies. Intercellular interactions are more important than chronological age or damage agents. Failures propagates from outwards in. Edges and boundaries are more susceptible to failure.
  4. Failure as a microscope: Failure times can be used to infer the structure of interdependence networks.

Many simple organisms such as ferns, hydra or jellyfish do not age. Their mortality rates remain approximately constant at all ages. In contrast, complex organisms typically have a probability of death m(t) that increases with age, t. Furthermore, the functional form of m(t) for many different organisms show a remarkable degree of similarity. The difference between simple and complex organisms, and the universality of aging patterns among complex organisms strongly suggest that aging is an emergent phenomenon that depends not on the individual properties of biological building blocks,but rather, on the interactions between them. Indeed, we die not because we slowly run out of live cells, but because of systemic failures that manifest in complex organs. In this talk I will present a quantitative theory of aging based on evolutionary and mechanical arguments, and show how aging appears as an emergent phenomenon as one moves across the scale of complexity, from large molecules and cells, to tissues and organs. I will particularly focus on aging in synthetic tissues, since this is the simplest structure that admits controlled experimental observation of emergent systemic damage. I will end my talk by showing how failure can be used as a "microscope". Specifically, how failure times can inform us about the structure of the interdependence network.

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Post-meeting Reflection

Dervis Can Vural (Univ. Notre Dame) Link to the source page

Noteworthy concepts and questions:

  1. Ravi: "Gerontropy". In additional to directional changes in health indicators do we get an increase in variance?
  2. 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?
  3. Ravi, Chhanda: How can theorists make themselves useful for NIH? How to communicate "theoretically driven" projects to NIH?
  4. Chhanda: Resilience builds up over time. Effect of early life history on aging. Comparative biology approaches e.g. naked mole rat
  5. 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
  6. 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?
  7. 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).
  8. 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.

Reference Material

Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
Aging in complex interdependency networks Dervis C. Vural, Greg Morrison, L. Mahadevan Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 2014 19 28
A Tissue Engineered Model of Aging: Interdependence and Cooperative Effects in Failing Tissues A. Acun, D. C. Vural, P. Zorlutuna Scientific Reports 2017 6 15
Interdependence theory of tissue failure: Bulk and boundary effects Daniel Suma, Aylin Acun, Pinar Zorlutuna, Dervis Can Vural Royal Society Open Science 2018 3 21
Inferring network structure from cascades Sushrut Ghonge, Dervis Can Vural Physical Review E 2017 3 14