Ribozymes can be engineered to function as molecular switches that exhibit different Boolean logic functions. Here, we describe two novel algorithmic pipelines for the design of YES and NOT Boolean-function oligonucleotide-sensing allosteric ribozymes bearing a predefined oligonucleotide-binding site sequence, capable of targeting known sequence RNA molecules. The algorithms generate allosteric riboswitches based on the extended hammerhead ribozyme by computing RNA thermodynamic properties and using random search. A computationally designed YES Boolean logic allosteric ribozyme was synthesized and tested in vitro. The tested ribozyme exhibits at least 1000-fold activation, demonstrating high ligand specificity and function. These algorithmic approaches provide a rapid and inexpensive way to create allosteric ribozymes targeting a known RNA sequence for constructing gene control elements and nucleic acid detection systems.

This complements my article last year, which proposed two coherent definitions of the life process, with an operationalization of one of them, which gives definition of living object and arose from Abel's article this year. The second, complementary topic is the problem of progress in biological evolution. All these topics are connected by the "purposeful information theory," which offers an objectively defined purpose and purposeful information, linked to causality and distinguishing such a concept of "purpose" from subjective intention. Both my and Abel's definitions of life assume that the purposefulness observed in living objects distinguishes them from non-living ones. Contrary to appearances, these are not competing definitions; Abel's approach, however, has a much more modestly developed foundation. It has allowed for the inclusion of even a "biological" definition based on metabolism into a consensus. The presence of progress in biological evolution is an extremely controversial issue. This stems from the lack of a broader theory that would sufficiently precisely define the various observed forms of progress and explore their properties and interrelationships. The theory of purposeful information, combined with the derivation of the basic features of the life process from its definition, allows for such analysis, definitions and conclusions that are clearly deeper than those previously presented in the literature.

The emergence of the first membranes requires more than the synthesis of amphiphiles; it requires a mechanism that stabilizes bilayer-forming geometries within chemically diverse prebiotic mixtures. We present a minimal thermodynamic model coupling amphiphile topology to self-assembly, incorporating packing, curvature, and steric contributions. Changes in temperature alter the balance between ordering and disorder within amphiphile aggregates, thereby modifying the relative stability of lamellar, micellar, and dispersed states. The model identifies a bounded temperature regime in which geometries compatible with ordered packing preferentially stabilize lamellar assemblies, while geometrically incompatible isomers partition into micellar or nonlamellar phases. In this framework, temperature acts as a physical filter on assembly stability, providing a pre-metabolic mechanism for membrane selection and suggesting that cooler environments may have offered favorable stability windows for early membrane persistence, and therefore may have been first in the origin of life in the Lipids First hypothesis.

Certain rough set lattices induced by binary relations form almost disjoint unions of Boolean algebras, a subclass of orthomodular lattices that captures quantum-like structure. In empirical data analysis, however, rough set lattices rarely realize this ideal form exactly. To quantify approximate similarity to such structures, we introduce numerical indices for rough set lattices. Simulation experiments based on controlled perturbations of binary relations show that the proposed indices distinguish structured perturbations from density-matched random baselines and remain informative under substantial perturbation. We further apply the framework to rough set lattices derived from machine-learning confusion matrices. In the examples studied, ordinary classification outcomes do not exhibit the characteristic signatures of almost disjoint unions of Boolean algebras, whereas a superposition-based setting does. The proposed approach provides a quantitative methodology for detecting latent lattice-theoretic organization in complex and intelligent systems, with potential relevance to cognitive and other self-organizing information systems.

Collective migration of epithelial monolayers emerges from the interplay between mechanical interactions and biochemical signalling on the cellular scale. Here, we provide a phenomenological mechanobiological framework with constitutive interpretation linking microscopic orientational interactions to tissue-scale mechanics. By distinguishing reversible from irreversible head-on and glancing interactions, we find that reversible interactions store orientational mechanical energy while preserving the collision angle between cells before and after contact, whereas irreversible interactions dissipate energy and alter the collision angle. Quantifying the storage and dissipation of orientational energy is crucial, as it governs how collective migration and mechanical feedback emerge in epithelial monolayers, ultimately influencing density-dependent outcomes such as cell jamming and live cell extrusion. These interactions modulate cell elasticity, contractility, and adhesion, thereby shaping the surface properties and effective viscoelastic response of the monolayer and influencing the efficiency of collective cell migration. Increasing cell packing density shifts the balance from energy storage to dissipation, inducing density-dependent changes in migration mechanisms, epithelial surface tension, and viscoelasticity. We quantify these effects using orientational potentials, an effective second virial coefficient, and dimensionless ratios capturing the relative fractions of energy stored or dissipated by orientational interactions. While these contributions are partial at intermediate densities (above the confluent cell packing density and below the jamming packing density), they become dominant near jamming. This framework provides a mechanobiological perspective linking cell-scale interaction dynamics and orientation changes to emergent tissue-scale rheological behavior. It suggests how collision-induced orientational energy may contribute to collective migration mechanics and how density-dependent interaction regimes may influence the epithelial viscoelastic response under varying cell packing conditions.

Kauffman's work-constraint (W-C) cycle captures the logical interdependence of work and constraint production in living systems, but leaves a fundamental dimension unexplored: when should each phase occur, and for how long? We argue that the relative duration of the work and constraint phases constitutes a temporal constraint that any autonomous agent must satisfy. Framing this as an optimal stopping problem, we show that the work phase should occupy 1/e ≈ 37% of the total cycle, which is the point at which diminishing returns on continued energy extraction are optimally balanced against the need for constraint reconstruction. We apply this framework to the eukaryotic cell cycle, treating G1 as the work phase and S/G2/M as the constraint phase, separated by the restriction point (R/START). Empirical observations from budding yeast and immortalized human cancer cells, which are systems that proliferate autonomously under steady-state conditions, show G1 durations of approximately 35-40% of the cell cycle, consistent with the 1/e prediction. Because cells lack explicit clocks, we propose that stochastic gene expression fluctuations may provide an implicit, representation-free mechanism for tracking elapsed time, and outline the empirical conditions needed to test this hypothesis. Our analysis suggests that temporal allocation deserves to be treated as a primary dimension of autonomous agency alongside the structural and thermodynamic dimensions already recognized.

Movement cascades across scales. A reach for a cup enlists ion channels opening on microsecond timescales, motor units summating on millisecond timescales, postural adjustments stabilizing the body over hundreds of milliseconds, and habits of coordination laid down over years-all operating simultaneously, all in register. The cascade exists; the striking fact is that it coheres: a hand reliably arrives at a cup despite being assembled from processes separated by ten orders of magnitude in space and time. A cascade that coheres does not do so by accident. For a limb to reach a cup, activity at molecular, cellular, tissue, and whole-body scales must remain in register despite operating at wildly different rates and magnitudes. Such coherence is fully consistent with physical law, but it is not a generic outcome of it: physics permits this cross-scale coupling without making it the default, and the specific organization that achieves it is hard-won. Evolution, we argue, has been in the business of building bridges-mechanisms whose function is to couple scales to one another, rather than to produce behavior at any single scale, so that perturbations at one level reach, and are answered at, levels far removed in space and time. Here, we sketch the contours of such a search. We argue that bridging mechanisms are real, discoverable, and distinct from the processes they couple; that evolution has repeatedly discovered a small family of cross-scale architectures (criticality, multifractal cascades, mechanotransductive tensegrity, oscillatory coupling, and motor synergies); and that an empirical program organized around these bridges can transform a philosophical puzzle into a tractable research agenda. The localization of action may be intractable, but the linkage of scales is not. Finding those linkages is, we suggest, the central empirical project that a multiscale science of movement now confronts.

Cultures of neurons grown on multi-electrode arrays have become a common experimental preparation for investigating developing neural networks. Experiment and simulation have shown that these developing networks eventually exhibit bursting behavior in which the entire culture participates for short periods of time, with inter-burst intervals in which the network is comparatively quiescent. This paper extends previous simulation results by examining the spatiotemporal patterns of such bursting. We show that these bursts originate at a small number of network locations and propagate as waves of activity. We demonstrate that this type of activity does not require fine tuning of neuron or network parameters. We also examine how this activity changes during development and the dependence of such activity and its triggering on both local and global network properties.

This manuscript discusses how recent models can clarify the emergence of life-like organization, focusing on the relationship between Carr's agent-based protocell simulations and the Prigoginian Informational Dissipation (PID) framework. Carr shows that motif-metabolite "semantic" correlations can accumulate before genetic replication, but only above an inheritance-fidelity threshold, and that mutual information may increase without fitness gains when correlations mainly reflect retained environmental/positional structure. We recast these results in a minimal "write-erase" dynamical form and introduce a PID-style symmetry-breaking closure in which informational constraints emerge only after a transition from a symmetric, non informative regime. We then propose an explicit bridge: PID-generated constraints can modulate effective retention and correlation lifetimes, yielding falsifiable predictions about when correlations persist at low inheritance fidelity. Finally, using CASP historic performance as an empirical analogue of overcoming combinatorial macromolecular structure search, we show that a regime-shift signature is better supported than a purely smooth trajectory.

This paper studies cooperation in spatial evolutionary games by combining local memory-based reputation with heterogeneous Fermi learning. Agents are placed on a two-dimensional lattice and repeatedly play the Prisoner's Dilemma with their neighbors. Each agent records the recent cooperative behavior of neighboring agents within a finite memory window and uses this information to form a local reputation assessment. This reputation then biases the choice of reference neighbors for imitation. Strategy adoption is modeled through a Fermi-type update rule with agent-specific learning sensitivity, allowing agents to differ in how strongly they respond to payoff differences. Numerical experiments show that reputation-biased reference selection improves cooperation by increasing the influence of historically cooperative neighbors. When combined with heterogeneous learning sensitivity, the mechanism produces more stable cooperative clusters within the tested parameter ranges and delays the decline of cooperation under stronger temptation to defect. Additional comparisons with baseline models, sensitivity analyses, structural-disorder experiments, and system-size checks are used to examine the stability of these findings. The results suggest that cooperation in structured populations depends not only on local interaction structure, but also on how agents use past behavior to select social references and respond to payoff differences.

Biophotonic emission has long been considered a weak byproduct of metabolic activity, but growing evidence suggests that it may exhibit non-classical properties indicative of underlying coherence. Building on the pioneering work of Fritz-Albert Popp, this study revisits the concept of coherence in ultra-weak photon emissions (UPEs) and proposes the term photonic biofield as a refined heuristic framework to (i) describe the spatiotemporal organization of biophotonic emissions in living systems; and (ii) elucidate the dual capacity of the biophoton as an information carrier and an energy transduction vector. Advances in the theory of open quantum systems (Fano-Feshbach formalism) and in tryptophan superradiance in microtubules provide a mechanistic basis for the resonant amplification of ultra-weak signals. Differential emission patterns between normal and tumor cells are characterized by spectral ratios (610-630/395-455 nm) correlated with tumor volume (R[2] = 0.947), as well as by loss of complexity revealed through diffusion entropy analysis. These findings are consistent with Popp's diagnostic intuitions. Collectively, these converging lines of evidence support the view that coherent photonic biofields represent a physically plausible and experimentally accessible layer of biological organization, with potential implications for non-invasive diagnostics and future avenues in energy-based therapeutic research.

This study investigates recovery dynamics in elementary cellular automata (ECA) following localized perturbations. Using all 256 Wolfram rules, we introduce controlled disruptions ("injuries") at specified pattern growth steps and quantify divergence between perturbed and unperturbed trajectories using Boolean XOR difference mapping. Based on this framework, we define a restoration coefficient R, computed from the normalized Hamming distance over a post-perturbation observation window. Across 100 trials per rule, recovery behavior varies systematically with dynamical class. Class I-II rules exhibit high restoration (R≈1), rapidly returning to baseline trajectories, while Class III rules exhibit persistent divergence (R≈0). Class IV rules display intermediate and phase-dependent behavior, with recovery strongly influenced by perturbation timing and structural maturity. In particular, rules such as Rule 110 show increased resilience, where localized structures constrain divergence. These results demonstrate that recovery properties are not uniformly distributed across rule space but instead cluster by dynamical regime and perturbation conditions. The restoration coefficient provides a quantitative framework for comparing robustness across cellular automata. We discuss how these computational findings offer an abstract perspective on pattern maintenance in biological systems during regeneration, while emphasizing the limits of direct mechanistic interpretation.

The universal conservation of the Proton-Motive Force (PMF) indicates that the chemiosmotic coupling was a primary requisite for abiogenesis. However, the mechanism governing Kinetic State Selection (KSS)-the process by which specific metabolic pathways were identified within high-dimensional prebiotic chemical spaces-remains a fundamental challenge in evolutionary biology. We introduce the Quantum-to-Classical Transducer (QCT) framework, positioning the PMF as the outcome of a stepwise physical scale-transduction cascade at mineral-organic interfaces. Driven by continuous non-equilibrium electrostatic potentials in the Hadean environment, semi-conductive mineral clusters facilitated electron transfer governed by non-adiabatic quantum kinetics. The transition from coherent sub-atomic tunneling to stable ionic gradients utilizes an Environmentally Assisted Quantum Transport, where aqueous thermal decoherence in metal nanopores forces rapid wave-function localization into thermodynamically favorable redox states. We mathematically estimate that nuclear tunneling provided a significant rate enhancement (10[13] s[-1]), enabling the sampling of dissipative pathways characterized by high reorganization energies that are kinetically inhibited in classical regimes. The discrete quantum of energy released during this subatomic collapse (0.1-0.5 eV) physically translocates a classical proton. The 1836:1 intrinsic mass anisotropy physically prevents the translocated proton from undergoing reverse tunneling across mineral-organic interface and transforms the high-frequency bidirectional electron flux into a stable unidirectional macroscopic chemical gradient. This physical rectification mathematically maximizes Onsager cross-coupling coefficients to achieve Dynamic Kinetic Stability. By providing deterministic physical resolution to the statistical improbabilities identified in stochastic prebiotic models, the QCT defines the PMF as a pre-biotic filter, favoring metabolic routes with optimal dissipative efficiency.

Binarization of gene expression data is a critical prerequisite for the synthesis of Boolean gene regulatory network (GRN) models from omics datasets. In practice, thresholding methods remain the dominant approach, yet they oversimplify the underlying biology by ignoring gene-specific functional roles and failing on sparse or single-snapshot data. To overcome these limitations, we propose Bi4Back, a novel regulation-based binarization algorithm that combines thresholding with iterative forward and backward Boolean propagation guided by a known signed regulatory graph-supplied as a required input rather than inferred-and corrects inconsistencies through a dedicated detection step. Bi4Back thus infers the binary states of genes given a network, not the network itself. The algorithm operates on as few as a single steady-state measurement and infers missing or uncertain binary states in a biologically consistent manner. Validation against ODE simulations of artificial and established Boolean GRNs spanning stable equilibria, oscillatory regimes, and continuous time-series trajectories shows exact agreement with the threshold-defined ground truth on stable artificial networks, near-exact agreement (dissimilarity distance down to d=1/11, reaching exact agreement at fully converged snapshots) on established models that converge to equilibrium, and graceful, phase- and topology-dependent degradation under oscillatory dynamics. The algorithm exhibits good robustness to up to ±50% multiplicative measurement noise, with dissimilarity distances remaining close to or better than the noise-free baseline, while performance under missing data depends critically on the topological centrality of unmeasured genes-a finding that yields a clear experimental design principle: measurement completeness for hub genes outweighs precision for peripheral ones. Robustness testing over 100,000 parameter-randomized simulations confirms reliability across diverse biological conditions. Scalability analysis across networks of 10 to 100 genes confirms computation times under 2 seconds throughout. Implementations in R and Mathematica are publicly available.

Gene regulatory network (GRN) benchmarks are typically interpreted as if curated references were complete, yet they are not. We formalize observed-metric ceilings under partial positive labels and reanalyze existing benchmark outputs across 15 methods and 5 references. The methods include six edge sets derived from a single-cell foundation model (scGPT) via attention and gradient attribution probes, evaluated alongside classical statistical and tree-based inferers and a random control. Under a missing-at-random (MAR) label model, we derive explicit ceilings for observed F1 and AUPR, propagate coverage uncertainty via Beta posteriors, and stress-test assumption violations. Crucially, because real curated references concentrate on extensively studied regulators, we replace stylized non-MAR tests with study-biased missingness grounded in two annotation databases: transcription factors assayed by ChIP-seq (CistromeDB/ChIP-Atlas) and genes carrying Reactome pathway annotations. Across 39 AUPR-evaluable rows, the best normalized F1 and AUPR ratios are 0.137 and 0.014, median normalized AUPR is 5.56×10-5, and only 3 of 39 rows exceed the observed random baseline; the foundation-model probes occupy the top of the ranking but still operate far below the observable ceiling. Restricting ground-truth edges to pathway-annotated genes leaves only ∼ 60% of edges observable and, at the empirically measured coupling between model scores and studied status, inflates observed AUPR by +0.040 above the MAR ceiling-intermediate between MAR (∼ 0) and the stylized worst case (+0.151). Missing labels therefore explain only part of the performance gap; substantial model-to-biology mismatch persists after ceiling correction, and evaluation claims should be narrowed accordingly.