Biological systems regulate their action at multiple levels of organization, from molecular circuits to physiological function. This “homeostasis” maintains stability of the system in the face of external and internal perturbation. How exactly this is achieved remains a topic of ongoing investigation; challenges are high dimensionality, many coupled positive and negative feedback loops, conflicting regulation demands and interaction with the environment.
Here I will introduce an empirical approach to the fundamental question – how do we know what it is that the system really “cares about”? What variable, or combination of variables, is under regulation? Two data-driven methods will be presented. one based on statistical analysis and applied to bacterial growth and division, revealing a hierarchy of regulation – from tightly regulated to sloppy variables. The second is based on a machine-learning algorithm we developed to identify regulation with minimal assumptions. This provides a different angle on the problem and highlights directions for future research.
FOR THE LATEST UPDATES AND CONTENT ON SOFT MATTER AND BIOLOGICAL PHYSICS AT THE WEIZMANN, VISIT OUR WEBSITE: https://www.biosoftweizmann.com/
