Emergent phenomena in living systems: a statistical mechanical perspective

TL;DR

This paper reviews how statistical mechanics explains emergent phenomena in living systems, focusing on noise-induced phase transitions in biological networks like gene expression and tissue homeostasis.

Contribution

It highlights the role of stochastic models in understanding biological phase transitions and connects theoretical physics concepts with biological observations.

Findings

Noise-induced transitions can resemble physical phase transitions.

Some biological phenomena exhibit critical-point behavior similar to the Ising universality class.

Statistical mechanics bridges models and experimental data in biology.

Abstract

A natural phenomenon occurring in a living system is an outcome of the dynamics of the specific biological network underlying the phenomenon. The collective dynamics have both deterministic and stochastic components. The stochastic nature of the key processes like gene expression and cell differentiation give rise to fluctuations (noise) in the levels of the biomolecules and this combined with nonlinear interactions give rise to a number of emergent phenomena. In this review, we describe and discuss some of these phenomena which have the character of phase transitions in physical systems. We specifically focus on noise-induced transitions in a stochastic model of gene expression and in a population genetics model which have no analogs when the dynamics are solely deterministic in nature. Some of these transitions exhibit critical-point phenomena belonging to the mean-field Ising universality class of equilibrium phase transitions. A number of other examples, ranging from biofilms to homeostasis in adult tissues, are also discussed which exhibit behavior similar to critical phenomena in equilibrium and nonequilbrium phase transitions. The examples illustrate how the subject of statistical mechanics provides a bridge between theoretical models and experimental observations.