Information transfer through a signaling module with feedback: a perturbative approach

TL;DR

This paper introduces a perturbative analytical method to quantify information transfer in a biological signaling module with feedback, revealing how feedback strength influences information gain through stochastic state switching.

Contribution

It presents a novel perturbative approach to analytically compute information transfer in feedback signaling modules, valid for small feedback and input strengths.

Findings

Information gain depends on total activation/inactivation events.

Explicit analytical expression derived for information transfer.

Feedback strength influences information transfer through event count.

Abstract

Signal transduction in biological cells is effected by signaling pathways that typically include multiple feedback loops. Here we analyze information transfer through a prototypical signaling module with biochemical feedback. The module switches stochastically between an inactive and active state; the input to the module governs the activation rate while the output (i.e., the product concentration) perturbs the inactivation rate. Using a novel perturbative approach, we compute the rate with which information about the input is gained from observation of the output. We obtain an explicit analytical result valid to first order in feedback strength and to second order in the strength of input. The total information gained during an extended time interval is found to depend on the feedback strength only through the total number of activation/inactivation events.