Information transmission in a two-step cascade: Interplay of activation and repression

TL;DR

This paper develops an information-theoretic framework to analyze how activation and repression influence signal transmission in different two-step cascade architectures, providing analytic expressions and simulation validation.

Contribution

It introduces a formalism that categorizes cascade architectures based on activation and repression effects on information metrics, with analytic derivations and simulation verification.

Findings

Activation and repression significantly affect mutual information and signal-to-noise ratio.

Analytic expressions relate biochemical parameters to information metrics.

Simulations confirm the validity of the Gaussian and linear noise approximations.

Abstract

We present an information-theoretic formalism to study signal transduction in four architectural variants of a model two-step cascade with increasing input population. Our results categorize these four types into two classes depending upon the effect played out by activation and repression on mutual information, net synergy, and signal-to-noise ratio. Within the Gaussian framework and using the linear noise approximation, we derive the analytic expressions for these metrics to establish their underlying relationships in terms of the biochemical parameters. We also verify our approximations through stochastic simulations.