A connection between bacterial chemotactic network and optimal filtering

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.

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Cited by in corpus (10)

• E. coli chemotaxis is information-limited
• Bayesian information engine that optimally exploits noisy measurements
• Optimal sensing and control of run-and-tumble chemotaxis
• Memory-Limited Partially Observable Stochastic Control and its Mean-Field Control Approach
• Pontryagin's Minimum Principle and Forward-Backward Sweep Method for the System of HJB-FP Equations in Memory-Limited Partially Observable Stochastic Control
• Performance limits of information engines
• Gradient sensing limit of a cell when controlling the elongating direction
• Theory for Optimal Estimation and Control under Resource Limitations and Its Applications to Biological Information Processing and Decision-Making
• Resource Limitations induce Phase Transitions in Biological Information Processing
• Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies