Abstract:

Almost all biological systems possess the ability to gather environmental information and modulate their behaviors to adaptively respond to changing environments. While animals excel at sensing odors, even simple bacteria can detect faint chemicals using stochastic receptors. They then navigate towards or away from the chemical source by processing this sensed information through intracellular reaction systems.

In the first half of our talk, we demonstrate that the E. coli chemotactic system is optimally structured for sensing noisy signals and controlling taxis. We utilize filtering theory and optimal control theory to theoretically derive this optimal structure and compare it to the quantitatively verified biochemical model of chemotaxis.

In the latter half, we discuss the limitations of traditional information theory, filtering theory, and optimal control theory in analyzing biological systems. Notably, all biological systems, especially simpler ones, have constrained computational resources like memory size and energy, which influence optimal behaviors. Conventional theories don’t directly address these resource constraints, likely because they emerged during a period when computational resources were continually expanding. To address this gap, we introduce the “memory-limited partially observable optimal control,” a new theoretical framework developed by our group, and explore its relevance to biological problems.