State- versus Reaction-Based Information Processing in Biochemical Networks

TL;DR

This paper compares reaction-based and state-based descriptions of biochemical trajectories, showing that reaction-based approaches better capture information transfer, especially in large systems, with implications for understanding cellular sensing mechanisms.

Contribution

It introduces a reaction-specific formulation of the Linear-Noise Approximation that accurately reflects mutual information in biochemical networks.

Findings

Reaction-based trajectories capture more information than state-based ones.

The reaction-specific LNA aligns with exact Markov jump process results.

Implications for cellular sensing and information transfer models.

Abstract

Trajectory mutual information is frequently used to quantify information transfer in biochemical systems. Tractable solutions of the trajectory mutual information can be obtained via the widely used Linear-Noise Approximation (LNA) using Gaussian channel theory. This approach is expected to be accurate for sufficiently large systems. However, recent observations show that there are cases, where the mutual information obtained this way differs qualitatively from results derived using an exact Markov jump process formalism, and that the differences persist even for large systems. In this letter, we show that these differences can be explained by introducing the notion of reaction- versus state-based descriptions of trajectories. In chemical systems, the information is encoded in the sequence of reaction events, and the reaction-based trajectories of Markov jump processes capture this information. In contrast, the commonly used form of the LNA uses a state (concentration) based description of trajectories, which contains, in general, less information than a reaction-based description. Here, we show that an alternative formulation of the LNA that retains the reaction-specific information of trajectories can accurately describe the trajectory mutual information for large systems. We illustrate the consequences of different trajectory descriptions for two common cellular reaction motifs and discuss the connection with Berg-Purcell and Maximum-Likelihood sensing.