A twenty-first century statistical physics of life (2410.20506v1)

Abstract: The molecular biology revolution of the last seventy five years has transformed our view of living systems. Scientific explanations of biological phenomena are now synonymous with the identification of the genes, proteins, and signaling molecules involved. The hegemony of the molecular paradigm has only become more pronounced as new technologies allow us to make measurements at scale. Combining this wealth of data with new artificial intelligence'' techniques is viewed as the future of biology. Here, we challenge this emergingcommon sense'', laying out a roadmap for developing a theoretical understanding of life. We argue that a twenty-first century theoretical biology must be founded on a new type of statistical physics suited to the living world. Rather than merely constructing statistical models, a statistical theory requires developing ``quantitative abstractions'' for understanding the gene-organism-environment triad. This necessitates overcoming four major challenges that distinguish living matter: (1) living systems are composed of a large number of heterogeneous parts rather than a large number of identical objects; (2) living systems control and manipulate the physical world in a manner that is extremely different from the ways considered in traditional statistical physics; (3) living systems necessarily operate out of equilibrium; (4) living systems are evolved objects with a function, resulting in new types of constraints that must be imposed on probabilistic ensembles. We conclude by discussing a few key themes that we view as promising directions for developing a statistical physics of life: typicality, localized biological control, a linear response of complex systems with non-reciprocal interactions, biological resource allocation, and learning and adaptation in overparameterized systems.

• The paper introduces a new framework that applies statistical physics principles to living systems, capturing heterogeneity and nonequilibrium behavior.
• It critiques conventional molecular biology models and argues that traditional statistical mechanics cannot fully account for the complexity of biological control and evolution.
• The proposed framework leverages concepts like typicality, localized control, and non-reciprocal interactions to enhance predictive models across biological fields.

A New Framework for Theoretical Biology: Statistical Physics of Life

In "Theory is Dead. Long Live Theory! For a 21st Century Statistical Physics of Life," the author provocatively situates the evolution of biological theory in the context of the monumental rise of molecular biology and artificial intelligence. The manuscript critiques the current hegemony of molecular biology and expansive statistical models, questioning their capacity to truly capture the intricacies of biological systems. Emphasizing the need for a novel theoretical approach, it proposes a statistical physics specifically tailored for living systems. This statistical mechanics would deviate from traditional physics, pivoting towards understanding living matter characterized by heterogeneity, nonequilibrium dynamics, and evolutionary adaptation.

The paper underscores four major challenges inherent in the biological domain: (1) the heterogeneity of functional parts that make up living systems; (2) the unprecedented nature of biological control in defying classical statistical mechanics; (3) the inherently nonequilibrium states in which living systems operate; and (4) the evolved functionality common to biological organisms imposing new types of constraints. Each of these factors contributes to the uniqueness of living systems, rendering current molecular paradigms and statistical models insufficient for comprehensive insight.

To address these challenges, the author suggests that a statistical physics of life should incorporate concepts such as typicality under constraints, localized control mechanisms, non-reciprocal interactions within complex systems, resource allocation, and learning in over-parameterized networks. For instance, the concept of typicality is posited as a means to frame complex biological interactions within the constraints imposed by evolutionary history. Similarly, focusing on non-reciprocal interactions acknowledges the inherent non-equilibrium state in biological systems, suggesting that traditional principles of symmetry and reciprocity may not apply.

Crucially, the paper emphasizes that these novel theoretical frameworks should enable abstraction and generalization, distinguishing themselves from contemporary statistical modeling approaches. While algorithms like AI and deep learning have imparted substantial value by performing tasks such as sequence-structure predictions—as evidenced by successes like AlphaFold—they remain limited to interpolation rather than extrapolation. Within the larger framework of theoretical biology, the statistical physics of life aspires to explain and predict biological phenomena across various contexts, emphasizing the potential to unify disparate concepts within biology under a new theoretical lens.

The implications for this line of inquiry are both numerous and significant. Practically, forming a robust statistical physics of life can lead to more predictive models for biological behaviors, potentially revolutionizing fields such as evolutionary biology, ecology, and systems biology. Theoretically, it can foster a deeper understanding of the principles underlying life's complexity, facilitating a shift away from the paradigm of mere data-driven inferences. As the manuscript argues, while the data-rich landscape of modern biology lends itself to advanced modeling techniques, the enduring challenge remains the development of theories that inspire novel insights and unification.

Looking forward, the potential contributions of a statistical physics of life may lie in its ability to provide a shared language and methodology that cuts across the diverse phenomenology of biological systems. It may forge new pathways in describing how living systems evolve, how they coordinate processes at multiple scales, and how they respond to environmental changes in both expected and unforeseen manners. This approach may eventually offer a comprehensive theoretical machinery for biology, capable of tackling the inherent complexity of life in a fundamentally new way.