A connection between bacterial chemotactic network and optimal filtering

TL;DR

This paper reveals that the E. coli chemotactic network functions as an optimal filtering system, efficiently extracting gradient information from noisy environments, bridging biophysical and information-theoretic perspectives.

Contribution

It establishes a mathematical equivalence between the biochemical chemotactic model and optimal filtering, showing the network's design for noise-robust gradient sensing.

Findings

Biochemical model is equivalent to optimal filtering dynamics.

Nonlinear response can be reproduced from optimal dynamics.

Chemotactic network is optimized for noisy gradient detection.

Abstract

The chemotactic network of Escherichia coli has been studied extensively both biophysically and information-theoretically. Nevertheless, the connection between these two aspects is still elusive. In this work, we report such a connection by showing that a standard biochemical model of the chemotactic network is mathematically equivalent to an information-theoretically optimal filtering dynamics. Moreover, we demonstrate that an experimentally observed nonlinear response relation can be reproduced from the optimal dynamics. These results suggest that the biochemical network of E. coli chemotaxis is designed to optimally extract gradient information in a noisy condition.