Emergent phenomena in living systems: a statistical mechanical perspective (2308.16805v1)

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.

• The paper explores how statistical mechanics and concepts like noise-induced transitions illuminate emergent phenomena in living systems, similar to phase transitions in physics.
• It highlights how stochasticity, particularly noise in gene expression and population dynamics, can lead to critical transitions and novel steady states not predicted by deterministic models.
• Applying statistical mechanics to biological networks and biofilms offers insights into regulatory mechanisms and suggests potential applications in biomedical science and synthetic biology.

Emergent Phenomena in Living Systems: A Statistical Mechanical Perspective

The paper "Emergent phenomena in living systems: a statistical mechanical perspective" by Indrani Bose provides an in-depth exploration of the emergent phenomena in biological systems, conceptualized through the lens of statistical mechanics. The discussion centers on understanding how stochastic processes, particularly noise-induced transitions, underpin these phenomena, analogous to the phase transitions observed in physical systems.

Overview

The paper begins with a recognition that living cells and their associated genetic networks function as interaction-rich many-body systems. Within these systems, stochasticity—particularly in processes such as gene expression, cell differentiation, and division—induces fluctuations that lead to emergent phenomena. The stochastic models of gene expression and population genetics are specifically analyzed for transitions induced by noise, which do not manifest in deterministic dynamics.

Key Contributions

1. Noise-Induced Transitions: The emergence of novel steady states due to stochastic dynamics is a focal point. In the Random Switch (RS) model of gene expression, noise transforms protein level distributions from unimodal to bimodal, contrasting with the deterministic steady state characterized by distinct ON and OFF states. The paper identifies critical transitions within this model based on varying noise intensity.
2. Statistical Mechanics of Biological Networks: By drawing parallels between biological transitions and critical phenomena in statistical physics, the paper illustrates how universal concepts of phase transitions can elucidate regulatory mechanisms in cell biology. The mean-field Ising universality class, known for studying equilibrium phase transitions, serves as a mathematical framework for interpreting noise-induced transitions.
3. Critical-Like Phenomena: In biological contexts, especially in population genetics, critical noise intensities lead to transitions mimicking critical points in thermodynamic systems. The paper details how these phenomena exhibit power-law behavior, akin to underlying principles in equilibrium and nonequilibrium critical transitions.
4. Biofilm Dynamics: The paper extends statistical mechanical models to biofilms, specifically examining long-range electrochemical signaling in bacterial communities—described through percolation theory. This model suggests an optimal balance of benefits versus individual cellular costs, achieved at a critical percolation point.

Implications and Future Directions

The insights offered by this paper have both theoretical and practical implications. Understanding stochasticity in gene expression and population dynamics could refine biomedical approaches to disease treatment, such as managing antibiotic resistance or targeting cancerous cell populations. The critical analogies drawn could also aid in the development of synthetic biological networks engineered for desired outcomes.

A promising avenue for future research is the exploration of noise modulation in gene regulation and cell differentiation. The paper hints at potential experimental setups where noise could be synthetically controlled to influence cellular processes, thereby providing direct validation for theoretical models proposed within this framework.

Conclusion

By leveraging the principles of statistical mechanics, this paper offers a comprehensive theoretical framework to analyze biological systems as complex dynamical entities capable of exhibiting critical phenomena. This approach not only broadens the understanding of cellular heterogeneity and phase-like transitions in biology but also strengthens the bridge between theoretical predictions and experimental observations. As research in this field continues to evolve, further exploration into noise-induced transitions in biological systems is anticipated to yield novel insights and applications in bioengineering and synthetic biology.