How and why do complex chemical and biological systems self-organize into ordered states far from thermodynamic equilibrium? Despite advances in thermodynamics, kinetics, and information theory, a unifying principle that links organization and efficiency across scales has remained elusive. If systems are open, the endpoints of the trajectories are restricted to the source and sink. We propose a stochastic-dissipative least-action triad framework in which (i) a path-ensemble weighting biases trajectories by their action cost, (ii) feedback processes sharpen this distribution, and (iii) the ensemble approaches a least-average-action attractor in steady state; otherwise it continues to decrease. We define a parametric cross-scale metric-Average Action Efficiency (AAE), the number of events over total action for them-and show that, under reinforcing feedback, identities derived from the exponential-family path measure imply decreasing average action and monotonically rising AAE. Our formulation could help bridge in its future extensions quantum, classical, and biological regimes while remaining computationally tractable because its empirical version relies on aggregate energetic and timing data rather than enumerating individual trajectories. At a non-equilibrium steady state, AAE reaches a local maximum consistent with the conditions and limitations of the current formulation. We connect AAE to thermodynamic and informational measures. A companion article (Part II) details empirical estimation strategies and applications.

Consciousness requires explanation for how biological architectures reliably generate it. This paper introduces Value Saturation, advancing an identity claim: phenomenal consciousness IS explicit recursive self-modeling saturated by homeostatic significance under perspectival entrapment. Building on the Reaction to Reflection framework, the theory distinguishes sentience (implicit recursion under survival stakes, Level 2) from subjective consciousness (explicit recursion where self-models become manipulable, Levels 4-5). Three integrated components prove necessary: interoceptive binding, homeostatic saturation, and perspectival entrapment. Testable predictions include developmental progression from birth sentience to subjective consciousness around ages 3-5, awareness-manipulation asymmetry, and clinical dissociations producing aberrant rather than absent phenomenology. Converging evidence from prediction error processing, homeostatic feelings, metacognitive hierarchies, and biological computing's thermodynamic advantages supports these requirements. The framework specifies falsification criteria and transforms consciousness into an empirically tractable investigation of organizational transitions in biological systems under thermodynamic constraints.

Selection pressure, determining which individuals survive and reproduce, is traditionally regarded as an exogenous property of the environment. However, growing evidence from the Extended Evolutionary Synthesis (EES) suggests that the strength of selection can, to some extent, be modulated by the organisms themselves. In this paper, we propose a minimal game-theoretical model that formalizes the concept of endogenous selection pressure. In the model, individuals collectively determine the intensity of selection through their actions: each agent "votes" to either intensify or relax selection depending on its relative fitness rank. The aggregate outcome of these decisions defines the number of reproducers, k, which is inversely related to the overall strength of selection. We analytically derive all pure strategy Nash equilibria of the game and examine their properties. By internalizing selection as a population-level emergent variable, the framework provides a tractable formalization of key principles from the EES and offers a foundation for exploring evolutionary dynamics that arise under the presence of endogenous selection pressure.

Understanding complex molecular interactions in biological systems requires both precise simulation and effective visualization of dynamic processes. Traditional tools have often presented static network diagrams and segregated simulation results, making it challenging to concurrently interpret network structure and temporal dynamics. In this study, we introduce SBDyNetVis, a novel Python-based library that transforms network and time-course data into interactive JavaScript visualizations using Cytoscape.js. SBDyNetVis integrates multiple network layout algorithms with animated representations of nodes and edges, allowing users to intuitively explore reaction kinetics, flux equations, and dynamic changes all on a single platform. We demonstrate the utility of SBDyNetVis by successfully converting 913 models from BioModels and by applying the tool in a detailed case study of the IKK-NFκB-IκB reaction. Its web-based interface promotes seamless data sharing and collaboration among researchers with varying levels of technical expertise. By bridging the gap between static network representation and dynamic simulation visualization, SBDyNetVis paves the way for deeper insights into cellular processes and holds significant potential for advancing systems biology research. SBDyNetVis is available at https://github.com/kntrinoue/SBDyNetVis.

One way to investigate the origins of cumulative culture is by examining the increasing complexity of lithic technologies in the hominin lineage. This study presents a simplified model of cultural transmission in Homo erectus and Homo heidelbergensis, focusing on the emergence of cumulative culture in relation to Oldowan, Acheulean, and the transition to Mode 3 technologies. Acheulean toolmaking likely required high-fidelity transmission, possibly supported by early forms of teaching. However, the remarkable technological conservatism of the Acheulean record contrasts with the rapid accumulation of innovations in modern humans. Our model aims to shed light on this circumstance by analysing the roles played by imitation and early forms of teaching. Each lithic technology is classified into three behavioural levels (0-2), representing increasing complexity and adaptive value, which can be acquired through individual learning or social transmission. We introduce two genotypes: the Imitator, which enables basic social learning, and the Assessor, which adds evaluative teaching between parents and offspring. We analyse the cultural dynamics generated by each genotype in pure populations. Our results suggest that incremental cumulative culture depends critically on offspring reliably replicating the most complex behaviours of their parents. This dynamic favours the evolution of the Assessor genotype, as it enhances parent-offspring behavioural resemblance. Nonetheless, when replication fidelity falls short of establishing the highest level of complexity behaviours (Level 2) predominant, the Assessor genotype may inadvertently reinforce cultural stasis instead of driving cumulative cultural evolution, ultimately hindering the transition from Level 1 to Level 2 behaviours via individual learning.

No consensus exists on how to define cognition. One source of contention is the attribution of cognition to aneural organisms. The claim that aneural organisms are cognitive stems from existing definitions in the cognitive literature and from the recognition that these organisms possess similar processes to those found in cognitive animals. The most conspicuous feature of cognition is that it is a collection of processes: perception, memory, decision-making, problem-solving, and so on. It has been shown that aneural groups, from bacteria to single-celled eukaryotes to plants and fungi, possess a rich machinery to implement these processes. Despite that, many researchers still dispute the idea of aneural cognition. They claim that their processes are too limited, that finding a bunch of processes in aneural organism does not suffice to make them cognitive, or that cognition needs specific requirements, which often involve high-level processes. We challenge these criticisms through an analysis of E. coli. We gather evidence for the existence of a rich cognitive repertoire in this organism, showcasing how a simple bacterium is capable of realizing multiple components of cognition. E. coli has an extensive molecular machinery that implements the various components deemed necessary for cognition. By analyzing E. coli, we can also capture an essential aspect of cognition's nature: cognition is a global process that emerges through the interdependent orchestration of its components, which enables an organism to grasp aspects of its world. As such, it represents a fundamental biological process for every cellular-based organism.

Tumor heterogeneity results from the continuous accumulation of mutations in cancer cells. High phenotypic diversity in cancer can reduce the efficacy of precisely targeted adaptive immune responses. To investigate the conditions under which tumor heterogeneity is sufficient for immune evasion, we developed an agent-based model at the level of individual cells. In this model, cancer phenotypes are represented as vectors, with mutation as a random walk on the corresponding phenotypic space. We evaluated various immune activation strategies by modulating the affinity threshold for activation and assessed their impact on tumor clearance rates. First, the model exhibited oscillatory behavior consistent with empirical observation, that we hypothesized was driven by negative frequency dependent selection. Further supporting this mechanism, the variance of cancer phenotypes consistently increases as the cancer population collapses. Finally, we identified conditions favoring either narrow or broad immune activation profiles, in accordance with theoretical predictions. These findings suggest potential strategies for optimizing immunotherapies, such as adoptive cell therapy, by tailoring the antigenic repertoire used to prime immune cells according to the adaptive dynamics of the target tumor population.

Nikolai Konstantinovich Koltsov (1872-1940) lived and worked in an epoch when biology was rapidly transitioning from a descriptive to an experimental science. Koltsov foresaw and, to some extent, predetermined the development of the most important areas of modern biology: evolutionary genetics and synthetic theory of evolution, mutagenesis, human genetics, cellular and molecular biology, developmental genetics, developmental biology, and epigenetics. He had visionary ideas about "template synthesis", which underlies the process of copying genes and the possible involvement of methylation in the modification of molecules of biological inheritance. For political reasons, the development of his scientific school in the USSR was terminated, and his name was forgotten for several decades. The scientific legacy of Nikolai Koltsov remains relevant in our days.

Nullomers - sequences entirely absent from a given genome - exhibit unexpected fractal structures when visualized using Chaos Game Representation (CGR). Unlike nullomers, rare sequences are defined as those that occur infrequently within the genome. Both nullomers and rare sequences conform to Generalised Chargaff's Second Parity rule at rates that far exceed random expectations. Beginning with nullomer analysis in Homo sapiens, we identified similar fractal patterns among rare sequences with low genomic frequency. We observe a continuous transition in various organisational properties, such as fractal geometry, CpG content, and Hamming distance between consensus sequences as a function of sequence frequency. In addition, our results reveal a fine-grained interpretation of Chargaff's rule: sequences exhibit frequency-dependent distributional characteristics. Rare sequences, in particular, display distinctive structural order that differentiates them from more abundant sequences, offering new insights into the underlying architecture of the genome as well as its informational structure. Moreover, these architectural and structural distinctions reinforce the perspective that information and meaning are encoded and managed independently within the genomic language.

Noncoding RNAs play a crucial role in many biological processes ranging from gene expression regulation, genome defence to epigenetic inheritance. In this paper, a mathematical model with time delay is proposed to characterize gene regulation mediated by two small noncoding RNAs acting at transcriptional level and post-transcriptional level. Through bifurcation analysis of the model without time delay, complex dynamics such as monostability, bistability, excitability and oscillation are exhibited with changing parameters. Then, introducing the time delay in the monostable, bistable, excitable and oscillatory regions generates a variety of bifurcations, accompanied with intermittent oscillations, oscillations with discontinuously changing amplitudes and periods, and chaotic dynamics by quasi-periodic route or period-doubling cascade route. The abundant dynamical mechanism on bistable switches or oscillators accounts for many functional features of sncRNAs. As an application, the presented model can be used to qualitatively simulate experimental findings of long-term memory formation. The existence of bistability depending on appropriate time delay is critical for the sustained high state in the system, and the time delay may affect the number of repeated pulses of stimulation required for the establishment of long-term memory.

This article investigates the ontological and epistemological status of viruses as liminal entities at the threshold between life and non-life. Building on recent developments in biosemiotics, theoretical biology, and origin-of-life studies, the paper argues that viruses challenge classical definitions of life by exhibiting context-dependent agency and evolutionary functions despite lacking cellular autonomy. Through a transdisciplinary methodology that integrates semiotic theory, material ecosemiotics, and systems biology, the study analyzes viral quasispecies dynamics, host-parasite coevolution, and the semiotic implications of viral replication. The central hypothesis proposes that life should not be defined as a fixed property but as an emergent, processual, and relational condition grounded in semiotic interactions. The article develops a theoretical model of "resonant semiosis" to account for the active role of liminal entities in generating biological meaning and transformation. By reframing viruses as heuristic figures for understanding the fluid boundaries of life, this work contributes to a more inclusive and dynamic conceptualization of biological individuation within the biosemiotic paradigm.

Cells and tissues are composed from atoms of chemical elements, some of which have two kinds of stable isotopes, magnetic and non-nonmagnetic ones. Not long ago, magnetic isotope effects (MIE) were discovered in living nature, in part, in the experiments with myosin, the important molecular motor of bioenergetics. In this review, the effects of different isotopes of magnesium and zinc on the enzymatic activity of myosin, namely - subfragment-1 of myosin, are presented. The rate of the ATP hydrolysis with the magnetic Mg as the enzyme cofactor is twice higher than the rates of the same reactions with the nonmagnetic Mg or Mg. The similar effect of the nuclear spin catalysis was detected in the enzymatic ATP hydrolysis driven by mitochondrial H-ATPase. Furthermore, MIE has been discovered in the experiments with different stable isotopes of zinc. While Zn performs the cofactor function less efficiently, than Mg, it was found that the rate of the enzymatic ATP hydrolysis with magnetic Zn is 40-50 percents higher as compared to the nonmagnetic Zn or Zn. As the kinetics phenomenon, MIE unambiguously indicates that, in the chemo-mechanical process driven by the biomolecular motor, there is a limiting step, and this "bottle-neck" is accelerated by the nuclear spin of the magnetic isotopes of Mg or Zn. Although detailed mechanisms of the discovered effects require further investigations, the findings of the nuclear spin catalysis in the ATP-dependent molecular motors of bioenergetics principally manifest the potentialities of the stable magnetic isotopes in the research aimed at creating the unified theory of ATP synthesis/hydrolysis.

The emergence of biology from planetary chemistry remains one of the central open questions in planetary physics, chemistry and biochemistry. In this study, we address an important aspect of this problem: whether abiogenesis is a statistically regular process under broadly defined planetary conditions, or a unique, highly contingent event. To examine it, we introduce a conceptual framework of regularity-relevant classes of adaptive planetary physico-chemical systems: the adaptability ladder model, that formalizes the progression from chemically rich but unorganized environments to stable biochemical systems. The model provides a structured approach to the regularity question, examining potential constrains and bottlenecks in specific adaptive classes and formulate formal statements of the regularity hypothesis: specialized for individual classes and strong (or general) across the full adaptive spectrum. We then assess their differential detectability across observation channels: laboratory simulation, in situ exploration, and remote sensing. Our analysis reveals a strong complementarity between detection channels and the regularity classes, with fragile proto-biotic states accessible primarily through laboratory studies. We observe that under the regularity hypothesis, such early adaptive states should emerge reproducibly under appropriate simulated planetary conditions. This result establishes a direct and testable link between laboratory experimentation and the general question of the regularity of biological emergence, positioning lab-based studies as central to future progress. The framework extends conventional definitions of planetary habitability, traditionally focused on terrestrial-class physical and chemical conditions by incorporating informational aspects, specifically compositional and interactive diversity. This broader perspective informs the strategic prioritization of empirical search efforts, bridging theoretical insights with observable planetary conditions.

In this article, we propose to use the formalism of quantum mechanics to describe and explain the so-called "abnormal" behaviour of agents in certain decision or choice contexts. The basic idea is to postulate that the preferences of these agents are indeterminate (in the quantum sense of the term) before the choice is made or the decision is taken. An agent's state before the decision is represented by a superposition of potential preferences. The decision is assimilated to a measure of the agent's state and leads to a projection of the state onto one of the particular preferences. We therefore consider that uncertainty about preferences is not linked to incomplete information but to essential indeterminacy. We explore the consequences of these hypotheses on the usual concepts of decision theory and apply the formalism to the problem of the so-called "framing" effect.

The chemiosmotic model of oxidative phosphorylation (oxphos) proposed by Peter Mitchell in 1961 was revisited and its basic mechanistic assumptions that oxphos is driven by protonmotive force either across or within the mitochondrial inner membrane were re-evaluated in light of recent findings. Available evidence strongly suggests that non-chemiosmotic mechanisms such as the conformon or detailed conformational change-mediated mechanisms, including Nath's torsional mechanism of energy transduction and ATP synthesis, are needed to explain the phenomenon of oxphos in enzymologically and quantum-mechanically realistic terms. An in-depth analysis of oxphos, taking into account recent findings reveal the logical errors or fallacies in dismissing conformational change-based models in favor of the chemiosmotic theory, and suggest the principal mechanistic events underlying chemomechanical coupling in bioenergetic processes.in general.

This paper proposes a new kinematic linkage model for the actin-myosin contraction mechanism. For studying the mechanical movements, the structures of myosin and actin are first modeled using rigid links and kinematic pairs. A kinematic mechanism has been proposed taking into consideration the experimentally observed changes in the angles of the links. The model has been validated with the experimentally available stroke length of the actin during the power-stroke. The present work also revisits the classical swinging lever arm model (SLAM) and for the first time, proposes kinematic linkage model for SLAM. The prominent contribution of the paper is the development of a planar kinematic linkage model based on Nath's Rotation-Twist-Tilt (RTT)/Rotation-Uncoiling-Tilt (RUT) model. The value of torsional spring stiffness of 534 pN-nm/rad has been estimated based on the RTT/RUT model for the first time. It is shown that the proposed kinematic model validates the experimentally measured, 5.3 nm stroke length of the actin filament, reported in literature.

From a biological standpoint, the consciousness of a living organism equipped with a complex nervous system may be considered the result of the complex structure and functioning of its brain. Subsequently, the neurobiological bases of consciousness should be traced back to the systemic nature of the macroscale neural circuitries of the brain, which are, in turn, the basic outcomes of certain fundamental microscale neuronal processes whose spatiotemporal action gives rise to a much more complex systemic process called neural plasticity. It is closely related to brain evolution from both phylogenetic and ontogenetic perspectives. It is the primary neurobiological process that links the brain with internal and external environments and the related experiences. Since consciousness is a theoretical construct introduced mainly to account for the (ontogenetically evolving) integrative subject-object intermediation, it might be possible hypothesize the existence of a relationship between consciousness and neural plasticity, at least from a biological standpoint. In these terms, the phylogenetic development of human consciousness, when human lineage started after the split from that of nonhuman primates, might be partially inferred by comparing the (ontogenetic) development of human and nonhuman primate brains, using an extended meaning of the concept of neural plasticity as the main neurobiological process of reference. What seems to emerge from this comparison is the evolutionary occurrence, during anthropogenesis, of distinguishing (transcriptional) heterochronic phenomena concerning neurodevelopmental processes.

The constant improvement in molecular biology techniques, as well as the continuous advances in optical microscopy, has generated an unmet demand for new tools for the analysis and processing of digital images. In this paper, we present and validate a new computational approach for quantitative analysis of mRNA and protein spatial profiles extracted from transverse-plane images of Drosophila melanogaster embryos. These images are used to collect fluorescence signal intensities from protein and RNA staining at the early developmental stages. Six main image processing tasks are addressed: image preprocessing, nuclei segmentation, cytoplasm detection (apical and basal), protein/RNA quantification, detection of the dorsoventral axis, and extraction of expression profiles. This approach enables the simultaneous determination of nuclear and cytoplasmic (both basal and apical) protein and/or RNA expression. In addition, it facilitates data extraction at an unprecedent level, with a potential significant impact to understand the establishment of morphogen gradients, gene regulation mechanisms, and cell signaling in eukaryotic organism. To our knowledge, this is the first of approach this kind. Our results show that the method is fully automated and robust to a variety of confocal image settings.

Biological teleonomy is a concept that reflects the goal-directedness and internal purposiveness characteristics in living systems. It is manifested in the way that the structures, functions, and behaviors of organisms are organized and evolved to achieve specific goals or maintain specific states. Teleonomy is a fundamental attribute of life, and as a biological principle, it stems from the special material structure of living systems. This paper uses the stable complex (evolution) encoding (SCE) model to explain the origin and mechanism of teleonomy. The SCE model represents biological functions as attractors and non-equilibrium dynamic stability structures, revealing how biological systems achieve the autonomous characteristics of self-selection and self-evolution through thermodynamic and kinetic encoding. This autonomy is the core manifestation of teleonomy and the fundamental reason for its self-sustaining and self-stabilizing characteristics. Since the origin of life, its structure has been goal-directed and has internal teleonomy. The structure at the origin of life established specific biological rules. As described by the SCE model, all functional structures are integrated into the system by forming encoded stable complexes. Therefore, life not only follows the laws of physics and chemistry but also has unique biological rules. Teleonomy emphasizes the self-leading and self-adapting aspects of biological evolution, achieving self-organization and self-construction in a way that coordinates with the environment.

Marine Growth Prevention Systems (MGPS) are vital for maintaining vessel efficiency, reducing biofouling, and ensuring sustainable operation under variable marine conditions. However, their performance is often challenged by complex interdependencies among design, thermodynamic, and operational factors. In this study, a fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) approach is employed to systematically evaluate and prioritize fifteen critical factors affecting MGPS effectiveness. The analysis integrates Constructal Law principles and bio-inspired design concepts to capture the dynamic interplay between entropy generation, copper ion distribution, electrode configuration, and structural adaptability. Expert evaluations from oceangoing chief engineers with academic and operational experience were used to construct the causal model. The results show that Structural Topology Efficiency (C13) and Evolutionary Robustness under Changing Conditions (C15) exhibited the highest causal influence (rᵢ - cⱼ > 0.35), whereas Biological Antifouling Analogy Consistency (C10) was found to be the most dependent factor (rᵢ - cⱼ < -0.28). Incorporating Constructal alignment principles reduced estimated entropy generation suggesting that thermodynamically optimized configurations can substantially enhance antifouling performance and energy efficiency. The cause-effect diagram highlights that adaptive topology and control responsiveness act as dominant design drivers, shaping electrochemical and flow-related outcomes. This study contributes to the literature by bridging fuzzy multi-criteria decision-making with thermodynamic and bio-organizational perspectives, offering both theoretical insights and practical design guidance. Furthermore, the findings open new research avenues toward integrating Constructal Law optimization with AI-based predictive control, CFD-multi-physics coupling, and self-healing MGPS architectures for next-generation sustainable marine systems.