Dynamic Information Transfer in Stochastic Biochemical Networks

TL;DR

This paper introduces numerical and analytical methods to quantify information transfer between molecular components in complex biochemical networks modeled as continuous-time Markov chains, capturing causal interactions in intracellular processes.

Contribution

It generalizes previous models to networks with multiple components, providing efficient Monte Carlo and analytical tools to measure path mutual information and transfer entropies.

Findings

Effective Monte Carlo method for large networks

Analytical approximation for mutual information

Application to feedforward and feedback networks

Abstract

We develop numerical and analytical approaches to calculate mutual information between complete paths of two molecular components embedded into a larger reaction network. In particular, we focus on a continuous-time Markov chain formalism, frequently used to describe intracellular processes involving lowly abundant molecular species. Previously, we have shown how the path mutual information can be calculated for such systems when two molecular components interact directly with one another with no intermediate molecular components being present. In this work, we generalize this approach to biochemical networks involving an arbitrary number of molecular components. We present an efficient Monte Carlo method as well as an analytical approximation to calculate the path mutual information and show how it can be decomposed into a pair of transfer entropies that capture the causal flow of information between two network components. We apply our methodology to study information transfer in a simple three-node feedforward network, as well as a more complex positive feedback system that switches stochastically between two metastable modes.