Complex systems chapter, additionals (for future versions):
https://www.math.snu.ac.kr/~syha/Lecture-4.pdf
The examples of networks modelling include: ...
- On the cellular scale, the mathematized MARS (mitochondria, aberrant proteins, radicals and scavengers) model describes the breakdown of cellular homeostasis in a networked approach 12
- Kowald, A. & Kirkwood, T. B. L. A network theory of ageing: the interactions of defective mitochondria, aberrant proteins, free radicals and scavengers in the ageing process
Nijhout, H. F., Sadre-Marandi, F., Best, J. & Reed, M. C. Systems biology of phenotypic robustness and plasticity. Integr. Comp. Biol. 57, 171–184 (2017).
- Top-down computational models with discrete health states interacting with simple explicit networks116–118 have demonstrated how population-level measures such as survival curves can be generated from conceptually simple models of damage propagation within a complex system.
Recent advances in machine learning have allowed general dynamics of continuous longitudinal data to be modeled by deep neural networks for more than 29 covariants121.
121. Farrell, S., Mitnitski, A., Rockwood, K. & Rutenberg, A. D. Interpretable machine learning for high-dimensional trajectories of aging health. PLoS Comput Biol. 18, e1009746 (2022).
- resilience in deterministic and stochastic systems
- If physiological systems are networks of interacting elements, then we can apply stochastic resilience theory to interpret dynamics of a whole suite of biological markers with aging. A key feature providing resilience to interacting networks is self-regulation within modules, while interactions among them might decrease resilience.
Complex systems chapter, additionals (add to issue):
https://www.math.snu.ac.kr/~syha/Lecture-4.pdf
The examples of networks modelling include: ...
- On the cellular scale, the mathematized MARS (mitochondria, aberrant proteins, radicals and scavengers) model describes the breakdown of cellular homeostasis in a networked approach 12
- Kowald, A. & Kirkwood, T. B. L. A network theory of ageing: the interactions of defective mitochondria, aberrant proteins, free radicals and scavengers in the ageing process
Nijhout, H. F., Sadre-Marandi, F., Best, J. & Reed, M. C. Systems biology of phenotypic robustness and plasticity. Integr. Comp. Biol. 57, 171–184 (2017).
- Top-down computational models with discrete health states interacting with simple explicit networks116–118 have demonstrated how population-level measures such as survival curves can be generated from conceptually simple models of damage propagation within a complex system.
Recent advances in machine learning have allowed general dynamics of continuous longitudinal data to be modeled by deep neural networks for more than 29 covariants121.
121. Farrell, S., Mitnitski, A., Rockwood, K. & Rutenberg, A. D. Interpretable machine learning for high-dimensional trajectories of aging health. PLoS Comput Biol. 18, e1009746 (2022).
- resilience in deterministic and stochastic systems
- If physiological systems are networks of interacting elements, then we can apply stochastic resilience theory to interpret dynamics of a whole suite of biological markers with aging. A key feature providing resilience to interacting networks is self-regulation within modules, while interactions among them might decrease resilience {cite}
klein2021computational .
{cite}ives1995measuring - check stochastic resilience theory
{cite}o2022critical - consider adding criticality subsection based on this
Complex systems chapter, additionals (for future versions):
https://www.math.snu.ac.kr/~syha/Lecture-4.pdf
The examples of networks modelling include: ...
Nijhout, H. F., Sadre-Marandi, F., Best, J. & Reed, M. C. Systems biology of phenotypic robustness and plasticity. Integr. Comp. Biol. 57, 171–184 (2017).
Recent advances in machine learning have allowed general dynamics of continuous longitudinal data to be modeled by deep neural networks for more than 29 covariants121.
121. Farrell, S., Mitnitski, A., Rockwood, K. & Rutenberg, A. D. Interpretable machine learning for high-dimensional trajectories of aging health. PLoS Comput Biol. 18, e1009746 (2022).
Complex systems chapter, additionals (add to issue):
https://www.math.snu.ac.kr/~syha/Lecture-4.pdf
The examples of networks modelling include: ...
Nijhout, H. F., Sadre-Marandi, F., Best, J. & Reed, M. C. Systems biology of phenotypic robustness and plasticity. Integr. Comp. Biol. 57, 171–184 (2017).
Recent advances in machine learning have allowed general dynamics of continuous longitudinal data to be modeled by deep neural networks for more than 29 covariants121.
121. Farrell, S., Mitnitski, A., Rockwood, K. & Rutenberg, A. D. Interpretable machine learning for high-dimensional trajectories of aging health. PLoS Comput Biol. 18, e1009746 (2022).
klein2021computational.{cite}
ives1995measuring- check stochastic resilience theory{cite}
o2022critical- consider adding criticality subsection based on this