Most models of prebiotic chemistry emphasize globally distributed or widely acting energy sources, including ultraviolet radiation, lightning, impacts, and energetic particles. However, spatially localized environments capable of repeatedly concentrating reactive chemistry may also have contributed to sustaining nonequilibrium chemical evolution on the early Earth. Here, we propose that Earth's auroral belts may have acted as recurrent and spatially focused environments in which prebiotic reactions repeatedly occurred. Magnetically guided charged particles preferentially enter the atmosphere at high latitudes, depositing energy locally rather than uniformly across the planetary surface. Previous irradiation studies have shown that energetic particles are capable of driving the formation of amino acids, carboxylic acids, and hydrolysable organic precursors under plausible prebiotic conditions. Building upon these previous studies, we explore whether geomagnetic focusing could have spatially organized such particle-driven chemistry into recurrent high-latitude reaction environments. We therefore consider the possible chemical consequences of auroral particle precipitation in an early atmosphere dominated by N2, CO2, and H2O, together with minor reduced gases. In contrast to broadly distributed energy inputs, auroral activity could have repeatedly supplied reactive compounds to restricted polar environments, where subsequent deposition and freeze-thaw concentration may have promoted further chemical evolution. The hypothesis yields several falsifiable predictions that distinguish magnetically localized particle-driven chemistry from ultraviolet- or lightning-dominated scenarios. If valid, planetary magnetic fields may have played an active role in spatially organizing chemical evolution on the early Earth and may also influence chemically favorable environments on other magnetized worlds.
OBJECTIVE:Long non-coding RNAs (lncRNAs) play an irreplaceable role in critical physiological processes such as cell cycle regulation, chromatin remodeling, and tumor suppression, yet traditional wet-lab molecular experiments are expensive and time-consuming. Consequently, there is an urgent need to develop efficient computational prediction models to prioritize disease-related lncRNAs, aiming to discover potential associations and reveal their underlying biological mechanisms. METHODS:A novel computational architecture named TARLHN is proposed. First, a hierarchical path attention encoder models multi-hop biological information transfer pathways over a heterogeneous network, employing a self-organizing dynamic routing mechanism to adaptively adjust semantic structural weights. Secondly, to counteract stochastic network noise and high sparsity, a multi-task equilibrium-driven representation learning strategy utilizing a generator-discriminator game is introduced to achieve structural homeostasis. Finally, an auxiliary edge prediction task captures evolutionary topological patterns to generate robust association scores. RESULTS:Evaluated through a rigorous 10-fold cross-validation protocol with fixed negative sampling pools, TARLHN demonstrated exceptional predictive stability, achieving outstanding performance with an average AUC of 0.9638 and AUPR of 0.9682. Furthermore, extensive case studies confirmed its effectiveness in screening potential lncRNAs associated with breast neoplasms and squamous cell carcinoma, with top-ranked candidates successfully validated by independent literature. CONCLUSIONS:This approach efficiently decodes the complex interactive topologies between lncRNAs and human diseases through topology-aware network representation and equilibrium-driven distribution alignment. It provides more effective theoretical guidance for interpreting lncRNA-mediated cellular control systems and discovering potential therapeutic targets.
Classical thermodynamics has been enormously successful in quantitatively describing macroscopic processes by using its standard, well-known ensemble approach to analyze systems. Yet today, we know from a number of modern developments in physical chemistry, biochemistry, biophysics, and structural and molecular biology that several biochemical processes in enzymes, molecular motors, and other small systems take place in a single-molecule mode, one molecule/ion at a time. How can such single-molecule processes be described and reconciled using thermodynamics within a classical framework? The reader is brought up to date on recent attempts to answer this question (Section 1). The issues and challenges are addressed afresh by a modification of Gibbs’ classical theory of irreversible processes, adapted to single molecules. After discussing the assumptions of the Gibbs framework and equation (Section 2), it is applied to local quantities, and a single molecule thermodynamics of physical processes is developed (Section 3). It is shown, by quantitative calculations, how to apply the results of the theory to membrane transport at a molecular level and to the prototype rotary molecular motor, FOF1-ATP synthase (Section 4). The results are interpreted based on Nath’s two-ion theory of energy coupling and ATP synthesis. The multiple, variegated biological implications arising from the single-molecule thermodynamics are discussed in detail, and guidelines to achieve a true understanding of biochemical processes in vivo are offered (Section 5). The new molecular theory developed here shows that “thermodynamics of a single molecule” is not a contradiction in terms. A companion paper shall attempt a single-molecule thermodynamics description of chemical reactions, e.g. ATP hydrolysis—of overarching importance to living systems.
Ageing is a multifactorial biological process whose comprehensive understanding requires integrative frameworks bridging biology, physics, and complexity sciences. Despite major advances in identifying molecular mechanisms, a unifying physical principle capable of explaining the progressive loss of systemic stability and the emergence of degenerative diseases remains elusive. In this hypothesis paper, we integrate classical ageing biology, including Strehler’s postulates and the Hallmarks of Ageing, with principles from non-equilibrium thermodynamics and complex systems theory. The organism is interpreted as a dissipative structure maintained far from thermodynamic equilibrium, where entropy production is proposed as a candidate physical marker of biological age and may behave as a Lyapunov-like quantity reflecting system stability. Within this framework, ageing is hypothesised as a progressive loss of dynamic robustness that may culminate in a biological phase transition, understood as a transition between dynamical attractors, toward degenerative disease states. Building on this foundation, we propose a fifth postulate that extends classical ageing theory and defines ageing as an emergent property of nonlinear complex systems operating far from equilibrium. This conceptual framework, while requiring further formalization and empirical validation, may provide a bridge linking ageing, longevity, and disease, and suggests new directions for interdisciplinary research.
Tumor cells face chronic genotoxic, metabolic, hypoxic, and immune stress that shapes their evolution. While stress-response molecular pathways are well characterized, cancer biology lacks a predictive framework for how cells select among alternative adaptive strategies and how these selections interact to produce tumor-level behavior. We propose that evolutionary game theory, previously applied to cooperation in cancer, should be extended to position stress adaptation itself as the organizing principle of tumor evolution. In this framework, stress-adaptive strategies constitute frequency-dependent games whose payoffs depend on population composition. We introduce a three-level distinction between cell states (transcriptional snapshots), game states (local configurations of stress and neighbor composition that define the active payoff structure), and cell strategies (conditional behavioral policies mapping game states to fitness-relevant outputs). This perspective explains the maintenance of intratumor heterogeneity through frequency-dependent selection, the reversibility of resistance through bet-hedging dynamics, and therapy resistance as an equilibrium outcome rather than genetic inevitability. Integrating insights from single-cell genomics, spatial profiling, and lineage tracing, we outline testable predictions and experimental approaches for measuring payoff structures. Therapeutically, the framework suggests exploiting adaptive trade-offs, restricting phenotypic plasticity, and reshaping competitive landscapes. Re-framing cancer as an evolving game of stress adaptation provides a unifying structure for predictive oncology.
Unifying physical and biological systems under a common information-theoretic framework requires reconciling two long-standing positions: that information is a universal structural feature of the environment and that it becomes meaningful only when a system registers a difference that alters its state. We propose a substrate-process-agency synthesis in which N-Space denotes a relational substrate of latent distinctions, while Order-Disorder-Reflexive Control (ODC) describes a phase-structured grammar through which bounded systems with memory render such distinctions operational. In this formulation, information becomes causally effective only through partition-mediated interaction that reduces uncertainty. Across domains, this framework predicts a set of falsifiable statistical signatures, including transient coordination during state alignment, non-memoryless persistence, boundary-dependent settling dynamics, and phase-dependent responses to perturbation. We argue, however, that these signatures need not appear identically in every system: some regimes are pulse-driven rather than sustained oscillators, and some support timing-sensitive responses more strongly than residence-time inference. To illustrate this point, we combine a minimal synthetic model, a nonlinear physical example, and an illustrative biological reanalysis of NF-κB signaling dynamics. Together, these analyses support the view that ODC is best interpreted as a phase-structured transition framework rather than a strictly periodic cycle model. The contribution of this work is not to introduce a new physical entity, but to provide a unified, empirically testable grammar for how relational structure, boundary conditions, memory, and timing jointly shape information flow across scales.
Individual differences in risky decision-making may reflect both risk attitude and the consistency with which preferences are expressed. We examined whether complementary behavioral descriptors could identify reproducible participant profiles in 67 healthy adults performing binary choices between safe options and gain-domain risky alternatives with equiprobable outcomes, varying in expected value and outcome variance. After robust trial-level response-time (RT) outlier screening, psychometric choice functions yielded two risk-attitude descriptors-overall risk attitude and context-dependent variability-and two choice-consistency descriptors-psychometric slope consistency and lapse-related stochasticity. RT outliers represented 8.7% of analyzed trials and decreased across blocks. Overall risk attitude and contextual variability were weakly associated, whereas greater contextual variability was consistently related to lower choice consistency. Controlled model-based clustering analyses supported a reproducible two-profile representation. Profile 1 captured participants with greater risk aversion, greater context-dependent variability, lower choice consistency, and higher lapse rates. Profile 2 captured participants with near-neutral risk attitude, low contextual variability, high choice consistency, and low lapse rates. These findings reveal a reproducible organization of individual behavior in a multidimensional risk-consistency space and provide a foundation for subsequent systems-level analyses of profile-dependent neural dynamics.
A unified science of life is extended to the fundamental unit of life - the biological cell. This theoretical study conceives a cell as a machine characterized by dipole strength (Ampere.meter), a constituent of the dipole moment of a cell. We present dipole strength as an individual characteristic of a cell that can help define the Hamiltonian, the total energy of a cell in its collective state, following the notational variant of the classical fields. Hamiltonian space is defined by an ordered pair of potential energy and kinetic energy. A framework for a science of life is presented in Hamiltonian space along with an equations of motion in terms of power. The framework is isomorphic across three levels of organization of life: cell, organism (human individual), and population (human society). A theory of measurement is presented to validate the model of the cell in terms of macroscopic observables such as cell membrane potential (volts) and dipole orientation (degrees), utilizing an experimental design and setup in spectroscopy and cell physiology. Copyright © 2025 IAISAE. Licensee: BioSystems 2025. Distributed under a Creative Attribution NonCommercial License 4.0 (CC BY-NC).
Biological neural systems sustain adaptive cognition under persistent and severe metabolic constraints. This paper develops a multi-scale thermodynamic framework in which those constraints function as organizing principles of neural efficiency and cognitive architecture, addressing four distinct shortcomings of prior accounts. First, I replace the widely cited "2 bits per synapse" convention with a probabilistic model grounded in the Bernoulli statistics of downstream neuron firing, yielding a substantially more empirically defensible efficiency ratio of approximately 3.4 × 106 times the Landauer limit. Second, I close the quantitative gap between per-synapse ATP chemistry and the brain's globally measured 20 W power budget through an explicit multi-scale bridge. Third, sparse coding, predictive processing, and cross-frequency coupling are derived as Lagrangian solutions to a single metabolic optimisation functional rather than merely described as consistent with energetic principles. Fourth, neuromorphic efficiency comparisons are updated and standardised to an energy-per-bit metric for Intel Loihi 2, BrainScaleS-2, and SpiNNaker2, and are extended to two compute-in-memory architectures - the charge-recycling array processor of Karakiewicz et al. (2012) and the RRAM-based NeuRRAM chip of Wan et al. (2022) - both of which approach or exceed biological synaptic efficiency. Each coding strategy generates testable, quantitatively specified predictions that admit principled rejection. The framework positions metabolic pressure not as an engineering detail extrinsic to neuroscience but as a constitutive selective force in the evolutionary shaping of neural computation.
This review treats cancer as a multiscale adaptive system, asks what the framework must predict to be worth adopting, and separates at each scale what the evidence establishes from what is proposed. It is an expert narrative synthesis, not a systematic review, and states the limits of that design. Proton transfer and tautomeric shifts contribute to spontaneous mispairing but do not license claims of directed or non-random mutation: replication timing, three-dimensional chromatin organization, sequence context and known mutagenic processes explain most mutational heterogeneity, leaving any quantum contribution as a residual against that baseline. The Waddington quasi-potential is bounded: outside detailed balance the dynamics are not gradient-derivable and require a probability-flux term. Hysteresis, rate-limited bimodality and return to state after perturbation distinguish an attractor from a transcriptomic cluster. Single-cell karyotype and live-imaging evidence supports whole-genome doubling as an unstable intermediate of heterogeneous origin and context-dependent consequence, not a uniform adaptive strategy. Systems and synthetic biology, virtual cells and digital twins are assessed against benchmarks, not promise. Tissue-scale ecology is reported with the spatial measurements now quantifying it, including evidence that stromal niche construction is not uniformly tumor-supporting. RNA modification is a layer in its own right, showing that the interpretation of a regulatory signal, not its magnitude, is biologically decisive. A dedicated section states the framework's commitments, the observable and evidence at each scale, and what would falsify them, asking what this adds to somatic mutation theory with clonal evolution and plasticity.
The concept of autopoiesis, introduced by Varela and Maturana (1974) to characterize the organizational closure shared by all living systems, has remained an open challenge for rigorous mathematical treatment. This paper traces Varela's formalization program from Spencer-Brown's calculus of distinctions through the Arithmetic of Closure and Form Dynamics, to the emergence of the Eigenform as a fixed-point model of biological identity. We analyze the methodological gap between this deterministic algebraic framework and the stochastic dynamics characteristic of living systems, and identify it as the central open problem motivating contemporary post-Varelian research. As a programmatic proposal rather than a completed formal system, we outline a Categorical-Thermodynamic Calculus of Closure that would integrate lambda-calculus fixed-point operators, topological category theory, and variational free energy minimization into a common setting for autopoietic systems under thermodynamic noise. We position this proposal within the contemporary cartography of autopoiesis, restrict its scope to autopoiesis in its strict organizational sense, and maintain a clear distinction between autopoietic organization and its formal representation. Rather than claiming a finished formalization, we identify the primitives, open problems, and evaluation criteria that such a formalization would require, and discuss the direction this sets for theoretical biology, cognitive science, and the foundations of autonomous systems.
Introduced by Bejan in 1996, the constructal law asserts that a finite-size flow system must evolve into configurations that provide easier access to the currents that flow through it in order to persist in time. This paper synthesizes the application of this principle to biosystems across multiple levels of biological organization, unifying evidence from physiology, animal locomotion, plant biology, microbiology, cellular biology, neurobiology, and social biology within a common framework. Tree-shaped vascular and bronchial networks; Kleiber's 3/4 power law of metabolism; the 1/4 power laws of breathing and heartbeat; the unified mass scaling of running, swimming, and flying; the architecture of root-trunk-canopy systems; the morphology of bacterial colonies and stony corals; the optimal microvasculature of the brain; the stepwise growth of cell clusters; the geometry of social-insect nests; and the design of athletic performance can be placed within a single physical framework. Several of these are derived as theorems of that law, while others are better described as interpretations or analogical extensions of it. Rather than competing with natural selection or the laws of thermodynamics, the constructal law completes them by placing design and evolution within physics as a first principle. The framework offers a deterministic, falsifiable platform for predicting biological architecture and for engineering biomedical flow systems such as vascularized tissue scaffolds and chemotherapeutic protocols.
Photosynthesis is among the most important sources of energy of the biosphere. An important part of photosynthesis is chlorophyll. The chemical formula of chlorophyll is well-known. However, thermodynamic properties of chlorophyll have not been reported. This paper reports enthalpies, entropies and Gibbs energies of molecules and reactions of macrocycle biosynthesis of chlorophyll a and b. The usable energy needed by plants for synthesis of chlorophyll was calculated. Chlorophyll biosynthesis reactions have negative Gibbs energy changes. This means that they are thermodynamically favorable. The negative Gibbs energy change means that the chlorophyll biosynthesis reactions could have appeared early in evolution. Gibbs energy of biosynthesis is the driving force of biosynthesis of chlorophyll that enabled the evolution of cells from the last universal common ancestor (LUCA) to cells that we know today.
The interstitial matrix of multicellular organisms — a polyanionic hydrogel embedded within connective tissue — undergoes volume phase transitions (VPT) between a swollen phase and a collapsed phase, as described by Tanaka (1978) for synthetic gels and extended to biological systems by Verdugo and others. We propose that this phase transition constitutes the mesoscale physical mechanism by which thermodynamic closure occurs in living tissue: the collapsed gel physically isolates embedded cells from the thermal, osmotic, and ionic gradients that drive entropy export, thereby attenuating the dissipative structures and the self-generated temporal order — internal time, in Prigogine's framework — that define the living state.On this account, chronic disease is not primarily a catalogue of molecular lesions but a thermodynamic state: one in which interstitial gel collapse has reduced cellular entropy-export capacity below the threshold required to sustain dissipative structures and internal time. The characteristic clinical features of chronic disease — persistent pain, functional limitation, treatment resistance, progressive deterioration — are the expected signatures of a system whose internal time is attenuating.This framework generates testable predictions concerning the relaxation time spectrum of interstitial gels, entropy-production markers in cells embedded in collapsed versus swollen matrix, and the nonlinear vibrational dynamics observable during effective physical intervention. We acknowledge that mathematical unification of Prigogine's statistical-mechanical formalism with the soft-matter physics of gel phase transition remains to be achieved; the correspondence we identify is structural. Nevertheless, the framework reframes the goal of therapeutic intervention: to restore thermodynamic openness — returning the gel to the swollen phase and thereby restarting the conditions for the living system's temporal self-organisation.
It has been proposed that intelligence is “an emergent property of consciousness … the ability to intentionally solve challenges and adapt to new situations and to an ever-changing environment,” anchored in the far-from-equilibrium (FFE) thermodynamics of intelligent systems (Vitas, Cvjetović and Dobovišek, BioSystems 263, 105776, 2026; hereafter VCD). We argue that this proposal, while moving in a productive direction, omits the specific dynamical regime that distinguishes intelligent organisation from mere dissipative complexity: the teleodynamic regime, in which a system's constraints participate in their own generation through recursive, constraint-on-constraint dynamics — teleodynamic closure — and thereby produce end-directedness from non-intentional substrates. Without that layer, the central operative terms of the definition — “intentionally,” “adapt,” “challenge” — remain undischarged. We identify five lacunae: (i) the FFE invocation is thermodynamic but not yet teleodynamic; (ii) intentionality is presupposed rather than emergentist-derived; (iii) the proposed cognition→consciousness→intelligence ordering is misaligned with the aneural-cognition literature; (iv) “adaptation” without a constraint-generation account is operationally indistinguishable from passive equilibration; (v) the definition lacks any treatment of constraint plasticity. We propose a reconstructed definition: intelligence is the capacity of a teleodynamically organised system to generate, propagate and modify the informational-regulatory constraints of its cognitive subsystem such that the system's own end-directedness is preserved and extended across novel adaptive challenges in a far-from-equilibrium environment. Building on the regulatory tradition (Bich et al., 2016; Bich and Moreno, BioSystems 148, 12-21, 2016), we distinguish life (closure of constitutive constraints), cognition (the informational-regulatory subsystem controlling those constraints), and intelligence (the constraint-plastic mode of that subsystem). The reconstruction is grounded in a timescale-separated dynamical sketch and worked biological cases (chemotaxis and metabolic switching in E. coli). It is substrate-independent yet thermodynamically anchored, consistent with aneural cognition, independent of any prior commitment to consciousness, and operationalisable for artificial intelligence, astrobiology and biological cognition research.
Contextuality is widely regarded as a hallmark of quantum information, yet its structural origin is often obscured by probabilistic or operational formulations. In this work, we show that non-distributive orthomodular structure need not be postulated, but arises canonically as a left adjoint from classical Boolean contexts. We introduce a gluing functor that takes pairs of Boolean algebras and identifies only their minimal and maximal elements via a categorical pushout. The resulting lattice is orthomodular but generically non-distributive. We prove that this construction is left adjoint to a forgetful functor selecting Boolean subalgebras, thereby providing a free but constrained generation of quantum-logical structure from classical contexts. Furthermore, we demonstrate that the failure of this pushout to remain Boolean is equivalent to the absence of global sections in the sheaf-theoretic framework of Abramsky and Brandenburger. This establishes a precise correspondence between contextuality as a sheaf obstruction and non-distributivity as a colimit failure. Our results offer a categorical and lattice-theoretic reconstruction of contextuality that precedes probabilistic notions and clarifies the structural necessity of quantum logic in information-theoretic settings. More broadly, the gluing construction suggests a general principle of contextual organization beyond quantum physics, providing a mathematical framework for interpreting biological systems in which globally coherent dynamics emerge from the interaction of multiple locally classical contexts.
The Caucasus region represents a unique natural laboratory for paleogenetic research due to its complex topography, long-standing role as a migratory corridor and glacial refugium, and exceptional preservation conditions for ancient DNA. This review synthesizes recent genome-wide studies to reconstruct the demographic history shaping the distinctive genetic landscape of modern Caucasus populations. The analysis reveals a deep pattern of continuity, isolation, and periodic admixture. Early genetic differentiation emerged in the Neolithic and Chalcolithic, forming distinct steppe and mountain population clusters. The Bronze Age was a pivotal period marked by large-scale gene flow from the Eurasian Steppe, particularly linked to the Yamnaya expansion, and interactions with Iranian and Anatolian-related groups. Despite these influences, many populations demonstrate remarkable genetic continuity from the Bronze Age to the present day. Significant knowledge gaps persist, particularly for the Paleolithic, Mesolithic, and Neolithic of the North Caucasus, as well as for the Late Medieval and Early Modern periods across the entire region. Addressing these gaps through targeted archaeogenomic studies is crucial for understanding the fine-scale processes that formed the hierarchical structure and high linguistic diversity of Caucasus populations, offering a powerful model for studying human adaptation, interaction, and language-genetics dynamics in a mountainous environment.
