Speaker
Description
Biological systems must maintain stable functions despite environmental perturbations and intrinsic molecular noise. Robust perfect adaptation (RPA) enables biological networks to restore their output to a desired level after disturbances. While antithetic integral feedback can guarantee adaptation of the mean output, it often amplifies stochastic fluctuations and therefore fails to control single-cell variability. In this talk, I present a mathematical framework that achieves noise-robust perfect adaptation, enabling simultaneous control of both the mean and variability of molecular outputs. The key idea is to combine a classical mean controller with a newly developed noise controller that regulates the second moment of the output species. This architecture allows stochastic biochemical networks to maintain both their mean and noise levels even after perturbations. We demonstrate how this framework stabilizes gene-expression dynamics and reduces failure rates in a model of the DNA damage response in E. coli. These results illustrate how mathematical modeling and stochastic control theory can provide new principles for regulating cellular variability in biological systems.
