Connecting molecular details to macroscopic behaviors with thermodynamics and Information theory
Abstract: How can we quantitatively connect our growing understanding of biology's molecular details to functions and macroscopic behaviors at larger scale? I will argue for a reverse-engineering inspired approach; quantifying behavioral needs and comparing them to the tolerances and fidelities of the molecular systems which instantiate them.
First, I will discuss recent work interrogating information flow in E. coli chemotaxis1. Using a minimal model of chemotaxis we show that a limited information rate to the chemosensory system places an upper bound on E. coli's ability to climb a gradient. Then, using sophisticated experiments to probe the internal dynamics of signaling, we were able to estimate the rate at which information arrives at their sensors while navigating a shallow gradient - about .01 bits/s in a typical experimental condition. Finally, by measuring their gradient climbing speed, we showed that E. coli chemotaxis is information efficient- climbing almost as fast as possible given the limitations of their sensor.
In the second part of the talk I will discuss our ongoing work to bound the intrinsic energetic costs of sending information. Biology uses many physically distinct strategies to send information; in neurons ion channels mediate specific currents which depolarize distant membrane. All cells produce or release second messenger molecules that diffuse to distant targets. Using minimal models we analyze the cost per bit of using these different strategies. Our goals are two-fold, first we wish to understand when biology should use each strategy. We would also like to account for the order of magnitude of energy consumption used by biological systems for information processing.