Mutual information between in- and output trajectories of biochemical networks

TL;DR

This paper estimates the mutual information between input and output trajectories of biochemical networks, specifically analyzing E. coli chemotaxis, to understand information transmission and optimize input signals.

Contribution

It introduces a Gaussian model to quantify mutual information between trajectories and identifies the optimal input power spectrum for maximal information transfer.

Findings

The mutual information can be effectively estimated using a Gaussian model.

E. coli chemotaxis network's information transmission rate is maximized at a specific input power spectrum.

The study provides insights into how biochemical networks encode and transmit information.

Abstract

Biochemical networks can respond to temporal characteristics of time-varying signals. To understand how reliably biochemical networks can transmit information we must consider how an input signal as a function of time--the input trajectory--can be mapped onto an output trajectory. Here we estimate the mutual information between in- and output trajectories using a Gaussian model. We study how reliably the chemotaxis network of E. coli can transmit information on the ligand concentration to the flagellar motor, and find the input power spectrum that maximizes the information transmission rate.