Consciousness research has traditionally focused on neural systems and has often treated consciousness as a late evolutionary innovation associated with complex brains. However, accumulating evidence from unicellular organisms, plants, and diverse animal taxa suggests that cognition and consciousness may be more broadly distributed across life. We propose a multidimensional framework of consciousness grounded in the Cellular Basis of Consciousness (CBC) theory and the concepts of the cellular senome and the multicellular N-space Episenome. Six universal dimensions of consciousness are identified and examined across biological scales: state, awareness, memory, attention, perception, and action. We argue that these dimensions are not independent faculties but interdependent manifestations of a unified informational architecture. The senome, conceived as the totality of a cell's sensory experiences and bioelectrical interactions, continuously integrates internal and external information to maintain self-referential awareness and adaptive competence. In multicellular organisms, overlapping individual cellular senomes form a supracellular N-space episenome, a collective informational space-time matrix that coordinates organism-wide cognition and behavior to effect seamless holobionts. Within this framework, organismal consciousness is localized not only to nervous systems but represents the collective cellular interactions of organism-wide informational systems that sense, remember, attend, perceive, and act in manner allowing survival and evolution. In this senomically-based consciousness framework, evolution is viewed as the progressive elaboration of these fundamental dimensions rather than the origin of entirely novel conscious faculties. This multidimensional perspective provides a unified conceptual framework for investigating consciousness across all living systems and offers new directions for empirical and theoretical research into the origins, mechanisms, and evolutionary trajectory of conscious experience.

The genetic code is commonly described as a redundant mapping from nucleotide triplets to amino acids, but this redundancy does not make all nucleotide substitutions equivalent. A physical mutation rate at the DNA level may translate into silent changes, conservative amino-acid substitutions, severe missense events, or nonsense mutations. Here, we develop a communication-theoretic framework to quantify how nucleotide-level errors are transformed into semantic degradation of protein-coding information. Single-nucleotide substitutions were modelled as an error channel with variable transition/transversion structure, and their effects were weighted using a normalized physicochemical distance between amino acids, with nonsense mutations assigned maximal cost. This allowed the definition of codon-level semantic cost, sequence-level degradation, and synonymous-codon protection scores. The framework first confirmed that the standard genetic code reduces expected semantic cost relative to randomized genetic-code baselines. At fixed nucleotide substitution probability, semantic degradation depended strongly on the transition/transversion spectrum, showing that physical error rate and genetic-information degradation are not equivalent quantities. The model was then applied to real coding sequences from the RefSeq Escherichia coli K-12 MG1655 assembly. Gene-level analysis revealed substantial heterogeneity in semantic vulnerability across CDS, driven by codon composition, synonymous-codon choice, nonsense susceptibility, and sequence length. Protein-preserving synonymous null models were used to separate amino-acid-composition effects from codon-choice effects. Relative to uniformly chosen synonymous alternatives, real E. coli CDS showed lower semantic substitution cost, increased silent-error protection, and reduced nonsense vulnerability. Much of this signal was explained by genome-wide codon-usage bias, but a subset of genes remained locally protected or locally vulnerable after controlling for global codon usage. Importantly, high-confidence essential genes were enriched among locally protected CDS and depleted among locally vulnerable CDS. This association remained robust across essentiality definitions, mutation-channel assumptions, multivariate controls, and random-label permutation tests. These results support a layered view of genetic-information robustness: the standard genetic code provides a primary semantic-protection layer, organism-wide codon usage provides a secondary modulation, and gene-specific synonymous choice introduces local heterogeneity associated with functional constraint. The proposed framework links mutation-channel parameters to interpretable measures of semantic degradation and provides a quantitative bridge between molecular error processes, codon usage, and gene essentiality.

We argue that causality is not fundamental but an emergent constraint learned by open biochemical systems as they compress structured environmental regularities. Building on neural-physics and renormalization ideas, we formalize a multiscale learning flow in which biochemical networks reorganize to reduce dissipation and prediction error. From stochastic microdynamics (Langevin, Fokker-Planck, path integrals) we describe how mesoscale causal organization - characterized by modularity, locality, and intervention-stability - can arise as fixed points of a constraint-learning flow β(ϕ) over a finite set of structural order parameters ϕ. We propose operational markers (transfer entropy, integrated information decomposition ΦID, graph entropies) and falsifiable experiments in Physarum, fungal electronics, and gene-regulatory networks that test for entropy collapse, gradient-reversal adaptation, and anticipatory dynamics. Unlike accounts that assume causal structure, we specify when and how it can form. This reframes causality as the macroscopic residue of adaptive coarse-graining rather than a primitive law, linking origins-of-life research, minimal cognition, and causal representation learning. This is a theoretical contribution: it reports no new experimental data, but it states a concrete minimal model and a falsifiable experimental program.

We hypothesize that cells are computational entities whose biochemical mechanisms are specialized in statistical learning. That is, cells learn by extracting regularities from their surroundings, predicting changes in environmental conditions, and reorganizing their biochemical networks for optimizing a risk function (e.g., ATP production), thereby preserving viability under dynamically fluctuating environmental conditions. This is not a metaphor, nor a proposal to apply machine-learning techniques for understanding biological data: it is the claim that cellular functioning is a physical realization of a process of statistical learning. Applying machine-learning methods to study cells is a possible methodological consequence of this hypothesis, not the hypothesis itself.

Here, I analyse the criticisms levelled at the theory (Chirumbolo, S., 2026. A Dynamical Systems Evaluation of the Hypothesis That Genetic Codes Define Domains of Life. Biosystems 268, 105926) that holds that domains of life can be identified through their genetic codes. This analysis concludes that these criticisms are fundamentally flawed. For example, these criticisms consider a model predicting that the domains of life were defined before the emergence of the genetic code. A domain of life is a taxonomic category that, by definition, implies it is composed of cells; indeed, all taxonomic categories are composed of cells. Therefore, it would be concluded that cells arose before the genetic code, which is simply absurd. In fact, the genetic code is a "part" of the cell, and a part cannot originate after its "whole" because the whole must necessarily come into being after all its parts have been completed; otherwise, a paradox would arise. This proves this model to be false. Thus, the hypothesis that the domains of life can effectively be identified by their genetic codes remains valid.

This paper examines the textual and semiotic characteristics of regulatory GCGCs. We hypothesize that the coding mechanisms for structural and regulatory genes differ fundamentally due to their distinct biological functions. Specifically, while the standard GC relies on separate signs, regulatory codes operates on sequence motifs and their combinations as functionally significant elements.. To explore this, we employ the concept of "text as a signal" formulated by Yu. Knorozov and A. Pyatigorsky, alongside distinction between alphabetic and hieroglyphic codes. We demonstrate that regulatory codes function as signal-texts. Their context-dependent operation relies on unique mechanisms because they encode functional operations-such as activation or repression-rather than amino acids or proteins. This functional difference gives rise to a hierarchy of nested codes and coding mechanisms ("languages"), each possessing its own semiotic organization, coding processes, and modes of generating meaning. Ultimately, we emphasize the importance to explicate the principles that govern how these diverse codes interact to realize hereditary information.

This study proposes an internalized morphogenesis model in which a body generates and reorganizes its own form through local token exchange among occupied modules. In contrast to conventional reaction-diffusion or cellular automaton models that update the entire coordinate space, the proposed model confines computation to the body domain itself. Each module generates a unit token quantity, exchanges token quantities only with adjacent modules, and stores accumulated quantities with integer-valued aging labels. A potential value computed from younger and older token populations provides an internal analogue of local activation and longer-range inhibition. Tokens sent from boundary modules toward empty space are not stored externally but dissipate; thus, the body boundary acts as a sink that converts morphology into internal potential gradients. Through the feedback between token dynamics, boundary-mediated dissipation, growth, and degeneration, the morphology evolves toward dynamic equilibria without an external blueprint or global coordinate system. Simulations on a hexagonal lattice showed the emergence of limb-like growth, mesh-like expansion, division-like separation under sensitive parameter conditions, and regeneration-like recovery after artificial amputation. Multi-seed analyses confirmed reproducible qualitative regimes while preserving stochastic morphological diversity. These results suggest that growth, division-like behavior, and regenerative recovery can arise from minimal internal information processing based on local exchange, token aging, and boundary dissipation. The model provides a concise computational framework for studying self-organizing morphogenesis as a body-internal process, rather than as patterning imposed on an external spatial field.

Transcription and translation are fundamentally stochastic processes that drive significant variability in mRNA and protein levels within living cells. In certain cellular environments, these processes occur simultaneously, motivating the Transcription-Translation Coupled (TTC) model. We present a simple model of stochastic gene regulation incorporating TTC that yields bimodality in protein synthesis dynamics. By casting the estimation of protein synthesis times as a first-passage time (FPT) problem, we demonstrate that TTC generates robust bimodal FPT statistics with possible implications for bacterial survival strategies.

The transition from inanimate physicochemical dynamics to autonomous agency represents a fundamental challenge across theoretical biology and cognitive science. While traditional accounts have approached this transition through descriptive frameworks of autopoiesis, anticipatory systems, or the free energy principle, a formal deductive derivation linking computational irreducibility to functional agency has remained elusive. Here, a deductive framework demonstrates that core attributes of biological agency, specifically representational time, forward predictive processing, and self-modeling, emerge as operational necessities for an autonomous agent persisting within a computationally irreducible environment under a defined operational regime of delayed hazards and counterfactual pre-commitment. Grounded in organizational closure and viability theory, this framework establishes three principal deductive results. First, it is demonstrated that purely reactive behavioral strategies fail under observationally aliased environmental states requiring mutually incompatible viability-preserving actions; persisting under delayed viability hazards without lethal trial-and-error mandates the internal synthesis of a tripartite representational time. Second, this representational time is shown to share the operational organization of counterfactual forward prediction. Third, drawing upon closed-loop regulator theory, the Conant-Ashby theorem, and the internal model principle, it is established that evaluating candidate interventions over extended horizons (H > 1) in a coupled system requires internalizing the agent's own generative action dynamics, yielding a functional self-model distinct from simple state-dependent regulation. These formal derivations are subsequently grounded in concrete molecular and cellular phenomena, including cyanobacterial KaiABC timekeeping, synthetic protocellular clocks, microbial adaptive regulation, and bacterial chemotaxis, explicitly demarcating experimentally demonstrated substrates from theoretical extrapolations. Finally, the framework develops a theoretical bridge connecting computational irreducibility to the semiotic hypothesis of organic codes as physical adaptors bridging decoupled biological domains, incorporating the code-mediator-artifact distinction. The analysis concludes that within the specified operational regime, functional self-representation emerges as an inevitable computational consequence of autonomous persistence.

Pedro Ortiz Cabanillas (1933-2011), a Peruvian neurologist and professor at Universidad Nacional Mayor de San Marcos, spent five decades building an Sociobiological Informational Theory: a systematic account of the universe, life, and organized society built around a single, redefined concept -information- understood as a material structure exclusive to living systems. Two works anchor his legacy: El Sistema de la Personalidad (1994), which gives the theory its first systematic exposition, and the Cuadernos de Psicobiología Social (2017), its most complete formulation. Ortiz proposed a five-level architecture of life (genetic, tissue, neural, psychic, and social information) governed by upward and downward determinations and by the primacy of negentropy over entropy. Working in a Lima hospital without internet access, without sustained contact with international science, and publishing almost exclusively in Spanish, Ortiz's theory remained completely unknown outside Peru. This article situates Ortiz's trajectory and theory historically, summarizes its core architecture -a body of thought that survived, barely, at the edge of disappearance- and argues for its relevance to contemporary debates on information in biology and consciousness.

AP1-AP4 define viability-localized production closure, produced boundaries, admissibility-respecting realization, and existence of a globally coherent viable regime. They do not determine whether a repair relation calibrated in one biological context remains adequate after the context changes. This study develops that second-stage question without introducing a new definition of life or a canonical list of categorical operators. The central construction is a context-indexed family of adequate repair surrogates that separates three claims: local surrogate adequacy in each context, reuse of one selected reference relation across contexts, and existence of any shared context-blind relation over the tested context family. A standard overlap-gluing criterion is specialized to make the strongest incompatibility claim explicit. Two quantitative realizations are analyzed. In an Escherichia coli core flux-balance model, three environmental or reaction constraints remain globally feasible while their growth-capacity readouts differ strongly. In a literature-based repressilator model, all 17 tested contexts remain locally reconstructable with high held-out skill, whereas the relation calibrated at the reference condition has a finite threshold-dependent reuse region. The analysis uses strictly disjoint temporal training and evaluation windows, blocked model selection, persistence and state-support controls, and ten-seed sensitivity analysis. A separately fitted state-only relation remains accurate across the complete tested context family, demonstrating that failure of the selected relation does not imply absence of a shared relation. These calculations provide empirical diagnostics of context dependence while deliberately stopping short of claiming full AP1-AP4 validation or a boundary-specific early-warning law.

While the Cohesion-Tension theory explains primary advective sap ascent, standard continuous models often neglect the mechanical regulatory role of bordered pits during secondary transport, such as capillary refilling. To address this, we model the conifer xylem as a network of pressure-actuated elastic microvalves governed by fluid-structure interactions. By coupling Darcy flow with state-dependent hydraulic capacitance and aquaporin-mediated transcellular refilling, we derive a macroscopic, asymmetric Fisher-Kolmogorov reaction-diffusion equation. Analytically, unimpeded fluid recovery propagates as coherent saturation wavefronts bounded by a Kolmogorov-Petrovsky-Piskunov critical velocity, which places the threshold on the same scale as overnight hydraulic recovery, although a front velocity for conifer stems is not at present well constrained empirically. If macroscopic diffusivity or elastic buffering rates drop below this structurally invariant limit, the wavefront collapses, providing a theoretical analog for catastrophic hydraulic failure. Conversely, once a refilling front separates a continuous liquid column from an embolized compartment held near atmospheric pressure by its gas phase, the membrane between them sustains the full head of that column - as tension developed at the column's own top, not as a weight resting upon it - and the microvalves actuate when this differential exceeds the elastic restitution limit of the margo. Crucially, our framework evaluates this topological shift via a piecewise integral: valve closures generate structural septa that physically isolate the lower fluid column and mathematically reset the pressure field for subsequent ascent. This provides a formal mathematical demonstration of hydraulic segmentation-the iterative stacking of mechanically decoupled fluid reservoirs, each of which need only sustain the tension generated over its own height rather than over the whole tree. Furthermore, by deducing a state-dependent capacitance factor to enforce absolute fluid-volume conservation, the closed-valve asymptotic limit yields an exact localized profile that functions as a threshold rather than an attractor: it sets a minimum sequestered volume, 4D/γ, below which an isolated reserve is reabsorbed by the surrounding living tissue. These profiles represent hydraulically sequestered capacitive buffers, mathematically mirroring how trees sequence drought responses to prioritize stem elastic water storage. Because the hydrostatic multiplier is fixed by the specific weight of water and the margo threshold has been measured directly, the predicted spacing of these septa follows with no adjustable parameter, and places segmentation in the range 33-97 m for the Pinaceae, its onset governed by the weakest pits of the population-within the range of heights attained by the tallest conifers. We emphasize that this remains a mechanism permitted by the physics and compatible with the known biology rather than one yet demonstrated: its distinctive signature, a discrete vertical periodicity in the aspirated state of the pit population, has not been looked for. Ultimately, this perspective on vascular fault tolerance offers scalable blueprints for resilient, biomimetic microfluidic technologies.

Subterranean termite tunnels support bidirectional movement between nests and foraging sites, yet the influence of tunnel curvature on traffic performance remains poorly understood. We developed a non-species-specific, individual-based simulation model to examine how total tunnel turning angle, termite number, tunnel width, and wall irregularities affect traffic. A complete factorial design comprising 69,120 simulation runs was evaluated using total flow rate and a composite congestion index. Termite number and tunnel width were the dominant determinants of performance. Increasing termite number increased flow and congestion, whereas wider tunnels reduced congestion. Wall irregularities were weakly associated with flow but strongly associated with increased congestion. Turning angle showed little monotonic association with either response, and including turning-angle terms provided limited improvement in regression-model support. However, curvature-related variation was somewhat greater under high population loading and in narrow tunnels. A turning angle of 90° was the most frequently selected best-tested angle for both flow maximization and congestion minimization, although selected angles were broadly distributed. The two best-tested angles differed in 334 of 384 conditions. Small optimality margins and moderate bootstrap support indicated that many selections were weakly separated and better interpreted as near-optimal ranges than precise optima. Overall, termite number and tunnel width were better-supported determinants, whereas curvature should be interpreted cautiously as a conditional, model-predicted modifier within the tested geometry and behavioral assumptions. The model has not yet been calibrated against empirical termite traffic data, so curvature-related patterns should be regarded as mechanistic simulation predictions rather than species-specific biological optima.