Speaker
Description
A bang–bang control in a reaction system refers to rapid switching between two extreme behavioral regimes of a species in order to optimize an objective, such as population growth. Because such transitions occur abruptly, intrinsic stochasticity typically induces large fluctuations in the system. We have recently identified a class of bacteria that employs a remarkable strategy to suppress these fluctuations while achieving population dynamics equivalent to those generated by bang–bang control. In this talk, we examine how biological systems mitigate noise while reproducing bang–bang–like behavior. To this end, we compare two reaction network models describing bacterial division mechanisms, each formulated as a continuous-time Markov chain. Although both models yield qualitatively similar mean population dynamics, their fluctuation levels differ substantially: one exhibits significantly reduced noise. This lower-variance regime corresponds to the so-called size-control mechanism, which is supported by experimental data reported in prior studies.
