Beyond Physics: Rewriting the Rules of Life

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Author: Denis Avetisyan

A new perspective argues that understanding living systems, particularly neuronal function, demands a departure from conventional physics and the embrace of ‘non-ordinary’ laws.

Cross-disciplinary approaches reveal that finite interaction speeds and inseparable charge and mass are critical to describing biological processes like electrodiffusion and ion channel operation.

Conventional disciplinary approaches struggle to reconcile the complexities of living systems, often leading to internal contradictions in their descriptions. This is addressed in ‘How cross-disciplinary science can describe living matter’, which posits that limitations stem from the inherent constraints of focusing on isolated quantities within individual disciplines. The paper demonstrates that by accounting for finite interaction speeds, the inseparable nature of charge and mass, and the unique thermodynamic conditions of biological electrolytes, a more accurate, cross-disciplinary framework-yielding what might be termed ‘non-ordinary’ laws-can emerge, as exemplified by a successful theory of neuronal operation. Could embracing such an integrated approach unlock a deeper understanding of life’s fundamental principles beyond the boundaries of traditional physics and biology?

The Limits of Equilibrium: A Biological Imperative

Classical thermodynamics, a cornerstone of physics, traditionally assumes systems reach a stable equilibrium – a state of balanced energy and uniform properties. However, living organisms fundamentally defy this expectation; they are dynamic, constantly exchanging energy and matter with their surroundings, and actively maintaining states far from equilibrium. This presents a significant challenge, as the equations designed for closed, static systems struggle to capture the complex, time-dependent processes inherent to biology. The instantaneous interactions presumed in many thermodynamic models fail to account for the crucial roles of reaction rates, diffusion gradients, and the spatial organization of molecules within cells. Consequently, applying these established principles to biological systems often necessitates considerable simplification, potentially obscuring vital details of how life functions and evolves.

The application of classical thermodynamic principles to biological systems frequently demands substantial simplifications, particularly when examining the behavior of ions and the dynamics of cellular membranes. These reductions, while mathematically convenient, often mask the intricate details of these processes; for example, assuming uniform ion concentrations across a membrane ignores the crucial role of localized gradients in signaling and energy production. Similarly, treating the membrane as a simple capacitor neglects its complex protein channels and active transport mechanisms, which are vital for maintaining cellular homeostasis. Consequently, analyses based on these approximations may yield incomplete or even misleading insights into the true energetic and dynamic realities within living cells, highlighting the need for more nuanced and sophisticated modeling approaches.

Traditional physics often treats systems as closed entities tending towards equilibrium, but living organisms defy this paradigm. These biological systems are fundamentally open, constantly exchanging energy and matter with their surroundings and actively working to maintain a dynamic, far-from-equilibrium state. This necessitates a departure from conventional approximations; models built for sealed containers simply cannot capture the intricate interplay of processes defining life. A new theoretical framework is therefore required – one that embraces the inherent openness of biological systems and prioritizes the mechanisms responsible for actively sustaining order amidst constant flux. Such an approach moves beyond merely describing states of balance, instead focusing on the rates of change, energy dissipation, and the proactive strategies organisms employ to persist in a constantly shifting environment.

Ion Transport: The Physics of Signal Propagation

Ion transport within the electrolyte solution supporting neuronal signaling is not a simple diffusion process; it is governed by a combination of collisional and electrostatic forces. Short-range collisions between ions and solvent molecules impede movement, contributing to frictional drag. Simultaneously, long-range electrostatic interactions, arising from the charges of the ions themselves, create attractive or repulsive forces that influence ion trajectories. These interactions are particularly significant given the high charge density within biological fluids and contribute to phenomena like ion pairing and the formation of an ionic atmosphere around each ion. The net effect of these competing forces dictates the effective mobility of ions and, consequently, the speed and efficiency of signal propagation.

The propagation of neuronal signals and maintenance of membrane potential are not instantaneous processes due to the finite speed of ion transport. While often simplified in models, ion movement is governed by interactions with the surrounding viscous electrolyte, limiting their velocity. Calculations utilizing ion energy and fluid viscosity estimate the Stokes-Einstein speed of ions to be approximately 105 m/s. This velocity, though seemingly rapid, introduces a non-negligible delay in signal transmission and necessitates the development of computational models that account for these transit times, moving beyond approximations of instantaneous ionic flow. Accurate simulation of these delays is critical for understanding the precise timing of neuronal events.

Ion transport is significantly impacted by the viscous drag of the surrounding fluid, which limits ion mobility and influences diffusion rates. The Stokes-Einstein relation mathematically describes this relationship, positing that diffusion coefficient [latex]D[/latex] is inversely proportional to both the fluid’s viscosity η and the ion’s hydrodynamic radius [latex]r[/latex]: [latex]D = \frac{k_B T}{6\pi \eta r}[/latex], where [latex]k_B[/latex] is the Boltzmann constant and [latex]T[/latex] is the absolute temperature. Calculations within our model, considering typical neuronal fluid properties and ion sizes, estimate the mechanical energy involved in overcoming viscous drag during these diffusive processes to be approximately 2 x 10-14 Joules per ion movement.

Membrane Dynamics: Beyond Conventional Circuitry

Biological membranes present a departure from traditional electrical circuit analysis due to their intrinsic physical properties. Unlike static conductors, membranes exhibit both elasticity and selective permeability, governed by the lipid bilayer and embedded proteins. This combination creates a dynamic, non-equilibrium environment where ion distributions and membrane potential are actively maintained. Consequently, applying standard electrical theory – which typically assumes fixed geometries and homogeneous materials – can yield inaccurate predictions. The selective permeability introduces a resistance dependent on ion type and concentration, while membrane elasticity influences capacitance and introduces mechanical effects not accounted for in conventional models. Therefore, a framework of ‘non-ordinary laws’ – incorporating these membrane-specific characteristics – is necessary for accurately describing and predicting electrical phenomena in living systems, such as nerve impulse propagation and cellular signaling.

The Nernst-Planck Relation, commonly used to describe ion flux across biological membranes, fundamentally assumes an electrochemical driving force determined by both concentration and electrical gradients. However, its accurate application requires detailed consideration of membrane-specific properties such as permeability, surface charge, and the presence of ion channels. The relation, expressed as [latex]J = -D\frac{dC}{dx} – \frac{zFCJ}{RT}[/latex], where J is the flux, D the diffusion coefficient, C the concentration, z the valence, F Faraday’s constant, R the gas constant, and T temperature, simplifies a complex reality. Dynamic changes in ion concentrations, induced by gating currents or metabolic activity, can significantly alter the driving force, and membrane capacitance influences the transient behavior of ion fluxes. Furthermore, the relation assumes a homogeneous membrane environment, which is often not the case due to lipid rafts and protein distributions.

Current models of action potential propagation may be enhanced by considering the characteristics of soliton-like waves, where the elasticity of the cell membrane contributes to self-reinforcing signal transmission along the axon. Calculations performed indicate pressure changes during an action potential are approximately [latex]2×10^3 Nm^{-2}[/latex]. This calculated pressure change correlates with measured temperature variations of 80 μK observed in experimental setups designed to study soliton behavior, suggesting a potential mechanistic link between membrane elasticity, soliton dynamics, and the physiological processes underlying neuronal signaling.

A Convergence of Disciplines: The Future of Biophysics

Accurate modeling of biological systems demands a deliberate integration of physics, chemistry, and biology, recognizing that life’s complexity transcends the boundaries of any single discipline. Traditional biophysical approaches, while valuable, often simplify living organisms as static physical machines, overlooking the crucial roles of chemical reactions, molecular interactions, and dynamic self-organization. A cross-disciplinary framework allows researchers to build more holistic models, accounting for the interplay between physical forces, chemical kinetics, and biological processes – from protein folding and cellular signaling to ecological interactions. This collaborative approach enables the development of predictive tools capable of simulating biological behavior with greater fidelity, ultimately accelerating breakthroughs in areas like drug discovery, personalized medicine, and synthetic biology.

Living systems defy simple mechanistic description; they are not simply elaborate arrangements of parts operating under fixed physical laws. Instead, biological entities exhibit self-organization, a process where patterns emerge spontaneously from local interactions without central control. This inherent dynamism gives rise to emergent properties – characteristics that cannot be predicted by examining the individual components in isolation. Consider a flock of birds; the coordinated, swirling movements are not dictated by a leader, but arise from each bird reacting to its neighbors. Similarly, within cells, complex behaviors like oscillations and pattern formation emerge from the interplay of molecules, demonstrating that life’s complexity stems not just from its components, but from how those components interact and self-organize, necessitating a shift in perspective for comprehensive biophysical modeling.

The pursuit of biological understanding increasingly demands a departure from strictly reductionist physical models. Traditional physics, while foundational, often struggles to encapsulate the inherent complexity and dynamism of living systems – systems defined by non-equilibrium thermodynamics, stochastic processes, and intricate feedback loops. By embracing approaches that acknowledge biology’s unique characteristics, researchers can move beyond simply describing what happens to elucidating how and why. This shift facilitates the development of novel technologies, from biomimicry inspired materials to targeted drug delivery systems, and promises breakthroughs in areas like regenerative medicine and synthetic biology. Ultimately, a more holistic framework, informed by physics but not constrained by it, is essential for unraveling life’s fundamental processes and addressing pressing biological challenges.

The pursuit of understanding living matter, as detailed in this work, demands a departure from solely relying on established physical laws. It necessitates acknowledging the inherent complexities arising from finite interaction speeds and the inseparable nature of charge and mass within biological systems. This approach echoes Albert Einstein’s sentiment: “The important thing is not to stop questioning.” The article demonstrates that accepting limitations of traditional models-and continually questioning their applicability to non-equilibrium systems-is crucial for formulating ‘non-ordinary’ laws that accurately describe neuronal operation. The elegance lies not in forcing biology into existing frameworks, but in allowing new principles to emerge from rigorous, cross-disciplinary investigation.

Beyond the Standard Model of Life

The proposition that biological systems operate under ‘non-ordinary’ laws, while perhaps jarring to the classically trained physicist, is not entirely unexpected. The insistence on treating living matter solely through the lens of established physics has consistently yielded approximations, not explanations. The core challenge remains the reconciliation of fundamentally thermodynamic processes with the observed complexities of neuronal operation and, by extension, all living systems. A truly predictive model demands a rigorous treatment of finite interaction speeds – the abandonment of instantaneous action at a distance – and the inextricable coupling of charge and mass within biological contexts.

Future work must prioritize the development of mathematical frameworks capable of capturing these subtleties. Empirical validation, while necessary, is insufficient; elegant algorithms, demonstrably correct through formal proof, are the ultimate goal. The current reliance on descriptive models – ‘it works because it works’ – is intellectually unsatisfying. A particularly fruitful avenue lies in exploring the implications of electrodiffusion beyond the simplistic assumptions of homogeneous media. Biological systems are, by their nature, exquisitely heterogeneous, and any theory ignoring this reality courts irrelevance.

Ultimately, the pursuit is not merely to describe life, but to understand its underlying principles with the same mathematical purity as one would demand of any other physical phenomenon. The elimination of redundancy, the minimization of abstraction leaks – these are not merely aesthetic concerns, but essential steps toward a truly predictive and elegant theory of life.

Original article: https://arxiv.org/pdf/2602.11370.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-15 07:45