Complete integrability of information processing by biochemical reactions

TL;DR

This paper introduces a new integrable hydrodynamic PDE framework for modeling finite-size biochemical reaction systems, providing explicit solutions and explaining complex phenomena like noise-induced cooperativity and stochastic bistability.

Contribution

It reformulates biochemical reaction kinetics using integrable hydrodynamic PDEs, enabling explicit finite-size solutions and advancing understanding of collective behaviors.

Findings

Explicit finite-size solutions for biochemical reactions

Explanation of noise-induced cooperativity and bistability

Validation against experimental data

Abstract

Statistical mechanics provides an effective framework to investigate information processing in biochemical reactions. Within such framework far-reaching analogies are established among (anti-) cooperative collective behaviors in chemical kinetics, (anti-)ferromagnetic spin models in statistical mechanics and operational amplifiers/flip-flops in cybernetics. The underlying modeling -- based on spin systems -- has been proved to be accurate for a wide class of systems matching classical (e.g. Michaelis--Menten, Hill, Adair) scenarios in the infinite-size approximation. However, the current research in biochemical information processing has been focusing on systems involving a relatively small number of units, where this approximation is no longer valid. Here we show that the whole statistical mechanical description of reaction kinetics can be re-formulated via a mechanical analogy -- based on completely integrable hydrodynamic-type systems of PDEs -- which provides explicit finite-size solutions, matching recently investigated phenomena (e.g. noise-induced cooperativity, stochastic bi-stability, quorum sensing). The resulting picture, successfully tested against a broad spectrum of data, constitutes a neat rationale for a numerically effective and theoretically consistent description of collective behaviors in biochemical reactions.