Cascade of transitions in molecular information theory

TL;DR

This paper models biological adaptation as an information-theoretic optimization problem, revealing a cascade of transitions in response complexity as the cost of information decreases, with implications for understanding biomolecular networks.

Contribution

It introduces a novel framework linking stochastic thermodynamics and rate distortion theory to analyze adaptive responses in biological systems, identifying phase transitions in response strategies.

Findings

Identification of a sequence of response transitions as information cost decreases

Derivation of formal equations for transition points and exact solutions for special cases

Detailed analysis of the first coding transition and its asymptotic behaviors

Abstract

Biological organisms are open, adaptve systems that can respond to changes in environment in specific ways. Adaptation and response can be posed as an optimization problem, with a tradeoff between the benefit obtained from a response and the cost of producing environment-specific responses. Using recent results in stochastic thermodynamics, we formulate the cost as the mutual information between the environment and the stochastic response. The problem of designing an optimally performing network now reduces to a problem in rate distortion theory -- a branch of information theory that deals with lossy data compression. We find that as the cost of unit information goes down, the system undergoes a sequence of transitions, corresponding to the recruitment of an increasing number of responses, thus improving response specificity as well as the net payoff. We derive formal equations for the transition points and exactly solve them for special cases. The first transition point, also called the {\it coding transition}, demarcates the boundary between a passive response and an active decision-making by the system. We study this transition point in detail, and derive three classes of asymptotic behavior, corresponding to the three limiting distributions of the statistics of extreme values. Our work points to the necessity of a union between information theory and the theory of adaptive biomolecular networks, in particular metabolic networks.