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Kunihiko Kaneko

Publications and source records attributed to Kunihiko Kaneko.

At least 19 recordsLinked to original sources

Global geometry of the genotype-phenotype map illuminates a trade-off between penetrance and mutational adaptability

Evolution in changing environments requires both reliable expression of the currently favored phenotype, as quantified by penetrance, and the capacity to reach alternative phenotypes through mutation. Previous studies suggest that high penetrance may restrict such mutational access. However, because these studies focus on evolved genotypes and local mutational neighborhoods, they cannot determine whether this local constraint limits mutational adaptability under environmental change. Such adaptability depends on a genotype's position relative to high-fitness regions for other environments. Addressing this question requires reconstructing the full probability distribution over phenotypes for every genotype and the resulting environment-specific fitness landscapes across genotype space. Such reconstruction is generally infeasible because genotype and phenotype spaces grow combinatorially. Here, an abstract model of stochastic genotype-phenotype mapping, inspired by interacting spins in statistical physics, permits exhaustive reconstruction of the map. We find that high-penetrance genotypes tend to occupy the interior of environment-specific high-fitness regions and are mutationally robust, whereas lower-penetrance genotypes tend to lie near their boundaries and have greater mutational access to high-fitness regions for alternative environments. This global geometry generates a trade-off between penetrance and mutational adaptability. In evolutionary simulations, stronger phenotypic noise in a fixed environment increases the selective advantage of reliable expression, thereby favoring high penetrance and mutational robustness. Frequent environmental change instead favors mutational accessibility at the expense of penetrance. Thus, penetrance and adaptability are opposing consequences of the same global geometry, with environmental conditions determining their evolutionary balance.

q-bio.PE↗

Self-Organized Dynamic Reactors: Product-Condensate Feedback Accelerates Reactions

Condensates can accelerate reactions by concentrating reactants, yet product accumulation can eliminate this advantage. We show that product-induced suppression of condensation generates rotating, deforming, and propagating condensate domains through spontaneous symmetry breaking. Their reorganization shifts reaction sites away from accumulated products while retaining reactant enrichment, yielding rates above those of static condensates and homogeneous states. Reaction flows can thus self-organize condensates into dynamic reactors, with implications for biological and synthetic systems.

physics.bio-ph↗

Noise-Driven Differentiation via Gene Frustration and Epigenetic Fixation

Gene expression in cells is stochastic, yet differentiation can display reproducible timing and stable fate commitment. We develop an analytical theory for a previously identified mechanism in which weakly stable intermediate, or frustrated, gene-expression states are perturbed by stochastic fluctuations and subsequently fixed by slow epigenetic feedback. By eliminating the fast expression dynamics, we show that the differentiation of the slow epigenetic variable is driven by the noise of gene-expression, which can be amplified by regulatory interactions. We derive the logarithmic dependence of onset time for differentiation upon the effective noise intensity, and the input-dependent probability of reaching either fate. We further construct a Waddington-inspired time-dependent probability landscape that visualizes population branching and progressive fate fixation.

physics.bio-ph↗

A loser in both environments can survive by switching between them

How can a species persist in an environment where it is always outcompeted? Using a minimal predator-prey model with environment-dependent parameters, we show that a predator driven to extinction in each of two static environments can survive indefinitely once the environment alternates between them fast enough. We derive the critical switching rate above which persistence occurs, and show that random (Poisson) switching needs to be faster than periodic switching in order to offset prolonged spells in the unfavorable environment. We then generalize the mechanism to any two-species system, and can predict persistence solely based on the sign of a single ``switching rescue function" assembled from the two boundary vector fields. This general result has broad reaching consequences: for instance, when applied to a standard model of viral dynamics, it predicts that two drugs which each clear a pathogen on their own can fail when alternated, giving a non-resistance-based explanation for the failure of drug-cycling strategies. Our results demonstrate that the tempo of environment change, as opposed to the environments themselves, can lead to species survival.

q-bio.PE↗

Self-Organized Bioelectricity via Collective Pump Alignment: Toward a Physical Origin of Chemiosmosis

Directional ion transport across membranes maintains living systems in nonequilibrium, which underlies chemiosmotic energy conversion. However, the physical origin of collectively organized ion transport in primitive cellular systems remains unclear. Here, we propose a minimal model in which ion pumps collectively align through feedback between ion transport and electrostatic interactions. In the model, directional ion transport generates a membrane potential, while the resulting electrochemical potential biases pump orientation, leading to self-organized collective alignment. Numerical simulations and mean-field analysis reveal a nonequilibrium transition from a disordered state without net transport to a pump-alignment state with sustained membrane potentials. The critical behavior is consistent with the mean-field Ising universality class; however, the effective field is generated self-consistently by nonequilibrium ion transport. We further show that protocell asymmetry can bias the polarity of the membrane potential. These results provide a generic self-organizing mechanism for the emergence of bioelectricity and a physical route toward chemiosmotic coupling in protocells.

physics.bio-ph↗

Delayed control driven oscillations in plant roots

Arabidopsis roots show oscillatory growth patterns on homogeneous agar surfaces, whereas other plants, such as maize, do not. Although several explanations have been proposed, a simple and general model that makes testable predictions across species has been lacking. Roots sense gravity and correct their growth direction towards the vertical. Motivated by recent evidence for a time delay in this gravitropic correction, we develop a minimal nonlinear model based on the delay hypothesis that predicts whether a root oscillates or grows vertically downwards. The model identifies a fourfold relation between the delay and time period, robust across different response functions. Analysing images of Arabidopsis, we find that the mode of the oscillatory arc length is not significantly different between inclined and vertical growth conditions. The quantitative agreement between the experimentally measured oscillatory arc length and the arc length estimated from estimated root growth speed and response delay supports this fourfold delay-period rule for delay-driven root oscillations. The simplicity of our model allows for a direct comparison with data from diverse plant species.

physics.bio-ph↗

Complex versus Complicated Systems Biology, Universality versus Detailed Modelling

Biological systems are generally complicated and/or complex. In the former approach, one sets up a model with a large number of parameters to describe the system in detail. The latter approach focuses on understanding the universal aspects of biological systems. In this case, an appropriate simple model represents a universality class. The extraction of universal properties is supported by evolutionary robustness and the reduction of dimensionality in high-dimensional states. Integrating the data-driven omics approach with the universality approach is an important step in systems biology.

physics.bio-ph↗

Cross-feeding yields high-dimensional chaos and coexistence of species beyond exclusion principle

Species interactions through cross-feeding via leakage and uptake of chemicals are important in microbial communities, and play an essential role in the coexistence of diverse species. Here, we study a simple dynamical model of a microbial community in which species interact by competing for the uptake of common metabolites that are leaked by other species. The model includes coupled dynamics of species populations and chemical concentrations in the medium, allowing for a variety of uptake and leakage networks among species. Depending on the structure of these networks, the system exhibits different attractors, including fixed points, limit cycles, low-dimensional chaos, and high-dimensional chaos. In the fixed-point and limit-cycle cases, the number of coexisting species is bounded by the number of exchangeable chemicals, consistent with the well-known competitive exclusion principle. In contrast, in the low-dimensional chaotic regime, the number of coexisting species exhibits noticeable but limited excess over this limit. Remarkably, in the high-dimensional chaotic regime, a much larger number of species beyond this limit coexist persistently over time. In this case, the rank-abundance distribution is broader than exponential, as often observed in real ecosystems. The population dynamics displays intermittent switching among quasi-stationary states, while the chemical dynamics explore most of the high dimensions. We find that such high-dimensional chaos is ubiquitous when the number of uptake chemicals is moderately larger than the number of leaked chemicals. Our results identify high-dimensional chaos with intermittent switching as a generic dynamical mechanism that stabilizes coexistence in interacting systems. We discuss its relevance to sustaining diverse microbial communities with leak-uptake cross-feeding.

physics.bio-ph↗

Evolution of robust cell differentiation under epigenetic feedback

In multi-cellular organisms, cells differentiate into multiple types as they divide. States of these cell types, as well as their numbers, are known to be robust to external perturbations; as conceptualized by Waddington's epigenetic landscape where cells embed themselves in valleys corresponding to final cell types. How is such robustness achieved by developmental dynamics and evolution? To address this question, we consider a model of cells with gene expression dynamics and epigenetic feedback, governed by a gene regulation network. By evolving the network to achieve more cell types, we identified three major differentiation processes exhibiting different properties regarding their variance, attractors, stability, and robustness. The first of these, type A, exhibits chaos and long-lived oscillatory dynamics that slowly transition until reaching a steady state. The second, type B, follows a channeled annealing process where the epigenetic changes in combination with noise shift the cells towards varying final cell states that increase the stability. Lastly, type C exhibits a quenching process where cell fate is quickly decided by falling into pre-existing fixed points while cell trajectories are separated through periodic attractors or saddle points. We find types A and B to correspond well with Waddington's landscape while being robust. Finally, the dynamics of type B demonstrate a differentiation process that uses a directed shifting of fixed points, visualized through the dimensional reduction of gene-expression states. Correspondence with the experimental data of gene expression variance through differentiation is also discussed.

physics.bio-ph↗

A Self-Organized Tower of Babel: Diversification through Competition

We introduce a minimal evolutionary model to show how local cooperation and global competition can create a transition to the diversity of communities such as linguistic groups. By using a lattice model with high-dimensional state agents and evolution under a fitness that depends on an agent's local neighborhood and global dissimilarity, clusters of diverse communities with different fitness are organized by equalizing the finesses on the boundaries, where their numbers and sizes are robust to parameters. We observe successive transitions over quasi-stationary states, as triggered by the emergence of new communities on the boundaries. Our abstract framework provides a simple mechanism for the diversification of culture.

physics.soc-ph↗

Generalising the Central Dogma as a cross-hierarchical principle of biology

The Central Dogma of molecular biology, as originally proposed by Crick, asserts that information passed into protein cannot flow back out. This principle has been interpreted as underpinning modern understandings of heredity and evolution, implying the unidirectionality of information flow from nucleic acids to proteins. Here, we propose a generalisation of the Central Dogma as a division of labour between the transmission and expression of information: the transmitter (nucleic acids) perpetuates information across generations, whereas the expressor (protein) enacts this information to facilitate the transmitter's function without itself perpetuating information. We argue that this generalisation offers two benefits. First, it provides a unifying perspective for comparing the Central Dogma to analogous divisions of labour observed at vastly different biological scales, including multicellular organisms, eukaryotic cells, organelles, and bacteria. Second, it offers a theoretical framework to explain the Central Dogma as an outcome of evolution. Specifically, we review a mathematical model suggesting that the Central Dogma originates through spontaneous symmetry breaking driven by evolutionary conflicts between different levels of selection. By reframing the Central Dogma as an informational relationship between components of a system, this generalisation underscores its broader relevance across the biological hierarchy and sheds light on its evolutionary origin.

q-bio.PE↗

Anonymous monitoring enables turn-taking and sustainablity in collective resource governance: Multi-player evolutionary dynamical-systems game

Sustainable resource use in large societies requires social institutions that specify acceptable behavior and punish violators. Because mutual monitoring becomes prohibitively costly as populations grow, we examine whether sustainability can be maintained when only anonymized information is available. Using the evolutionary dynamical-systems game framework, we model the common-pool resource management game. In the model, each player's harvesting decisions shape the resource dynamics and depend on the resource's state, the player's wealth, and the group average wealth. Strategies are encoded as two-parameter decision-making functions that mutate across generations. Evolutionary simulations reveal that players self-organize into clusters that alternate harvesting turns: individuals within a cluster harvest synchronously, while the clusters themselves take turns. The emergent institutional rule is strikingly simple: "wait when rich, harvest when below average." While the majority cluster tends to exploit the minority, moderate diversity in decision parameters of strategies allows "turn-taking of turns" between the majority and minority roles, improving efficiency, equity, and resistance to selfish mutants. We quantify the difficulty of managing institutions as population size increases. When group size is fixed, the minimum number of groups required for cooperation grows exponentially with group size. If, however, groups enlarge gradually, the scaling transitions to a power law, indicating that institutions remain stable when they are first built in small populations and subsequently adapted to larger ones. Our findings provide a theoretical basis for the self-organization of institutions in large societies, illuminating how anonymized information can coordinate behavior and how institutional success depends on its developmental trajectory.

physics.soc-ph↗

Enzyme as Maxwell's Demon: Steady-state Deviation from Chemical Equilibrium by Enhanced Enzyme Diffusion

Enhanced enzyme diffusion (EED), in which the diffusion coefficient of an enzyme transiently increases during catalysis, has been extensively reported experimentally. We numerically and analytically demonstrate that such enzymes can act as Maxwell's demons. They use their enhanced diffusion as a memory of the previous catalytic reaction, to gain information and drive steady-state chemical concentrations away from chemical equilibrium. Our theoretical analysis identifies the conditions for this process, highlighting the functional role of EED and its relevance to cellular systems.

physics.bio-ph↗

Self-organized institutions in evolutionary dynamical-systems game

Social institutions are systems of shared norms and rules that regulate people's behaviors, often emerging without external enforcement. They provide criteria to distinguish cooperation from defection and establish rules to sustain cooperation, shaped through long-term trial and error. While principles for successful institutions have been proposed, the mechanisms underlying their emergence remain poorly understood. Here, we introduce the evolutionary dynamical-systems game, a framework that couples game actions with environmental dynamics and explores the evolution of cognitive frameworks for decision-making. We analyze a minimal model of common-pool resource management, where resources grow naturally and are harvested. Players use decision-making functions to determine whether to harvest at each step, based on environmental and peer monitoring. As these functions evolve, players detect selfish harvesting and punish it by degrading the environment through harvesting. This process leads to the self-organization of norms that classify harvesting actions as cooperative, defective, or punitive. The emergent norms for ``cooperativeness'' and rules of punishment serve as institutions. The environmental and players' states converge to distinct modes characterized by limit-cycles, representing temporal regularities in socio-ecological systems. These modes remain stable despite slight variations in decision-making, illustrating the stability of institutions. The evolutionary robustness of decision-making functions serves as a measure of the evolutionary favorability of institutions, highlighting the role of plasticity in responding to diverse opponents. This work introduces foundational concepts in evolutionary dynamical-systems games and elucidates the mechanisms underlying the self-organization of institutions by modeling the interplay between ecological dynamics and human decision-making.

physics.soc-ph↗

Stability Control of Metastable States as a Unified Mechanism for Flexible Temporal Modulation in Cognitive Processing

Flexible modulation of temporal dynamics in neural sequences underlies many cognitive processes. For instance, we can adaptively change the speed of motor sequences and speech. While such flexibility is influenced by various factors such as attention and context, the common neural mechanisms responsible for this modulation remain poorly understood. We developed a biologically plausible neural network model that incorporates neurons with multiple timescales and Hebbian learning rules. This model is capable of generating simple sequential patterns as well as performing delayed match-to-sample (DMS) tasks that require the retention of stimulus identity. Fast neural dynamics establish metastable states, while slow neural dynamics maintain task-relevant information and modulate the stability of these states to enable temporal processing. We systematically analyzed how factors such as neuronal gain, external input strength (contextual cues), and task difficulty influence the temporal properties of neural activity sequences - specifically, dwell time within patterns and transition times between successive patterns. We found that these factors flexibly modulate the stability of metastable states. Our findings provide a unified mechanism for understanding various forms of temporal modulation and suggest a novel computational role for neural timescale diversity in dynamically adapting cognitive performance to changing environmental demands.

q-bio.NC↗

Modelling Soil as a Living System: Feedback between Microbial Activity and Spatial Structure

Soil is a complex, dynamic material, with physical properties that depend on its biological content. We propose a cellular automaton model for self-organizing soil structure, where soil aggregates and serves as food for microbial species. These, in turn, produce nutrients that facilitate self-amplification, establishing a cyclical dynamic of consumption and regeneration. Our model explores the spatial interactions between these components and their role in sustaining a balanced ecosystem. The main results demonstrate that (1) spatial structure supports a stable living state, preventing population collapse or uncontrolled growth; (2) the spatial model allows for the coexistence of parasitic species, which exploit parts of the system without driving it to extinction; and (3) optimal growth conditions for microbes are associated to diverse length scales in the soil structure, suggesting that heterogeneity is key to ecosystem resilience. These findings highlight the importance of spatio-temporal dynamics of life in soil ecology.

physics.bio-ph↗

Dimensional reduction and adaptation-development-evolution relation in evolved biological systems

Life systems are complex and hierarchical, with diverse components at different scales, yet they sustain themselves, grow, and evolve over time. How can a theory of such complex biological states be developed? Here we note that for a hierarchical biological system to be robust, it must achieve consistency between micro-scale (e.g. molecular) and macro-scale (e.g. cellular) phenomena. This allows for a universal theory of adaptive change in cells based on biological robustness and consistency between cellular growth and molecular replication. Here, we show how adaptive changes in high-dimensional phenotypes (biological states) are constrained to low-dimensional space, leading to the derivation of a macroscopic law for cellular states. The theory is then extended to evolution, leading to proportionality between evolutionary and environmental responses, as well as proportionality between phenotypic variances due to noise and due to genetic changes. The universality of the results across several models and experiments is demonstrated. Then, by further extending the theory of evolutionary dimensional reduction to multicellular systems, the relationship between multicellular development and evolution, in particular the developmental hourglass, is demonstrated. Finally, the possibility of collapse of dimensional reduction under nutrient limitation is discussed.

physics.bio-ph↗

Fluctuation-learning relationship in neural networks

Learning in neural systems occurs through change in synaptic connectivity that is driven by neural activity. Learning performance is influenced by both neural activity and the task to be learned. Experimental studies suggest a link between learning speed and variability in neural activity before learning. However, the theoretical basis of this relationship has remained unclear. In this work, using principles from the fluctuation-response relation in statistical physics, we derive two formulae that connect neural activity with learning speed. The first formula shows that learning speed is proportional to the variance of spontaneous neural activity and the neural response to input. The second formula, for small input, indicates that speed is proportional to the variances of spontaneous activity in both target and input directions. These formulae apply to various learning tasks governed by Hebbian or generalized learning rules. Numerical simulations confirm that these formulae are valid beyond their theoretical assumptions, even in cases where synaptic connectivity undergoes large changes. Our theory predicts that learning speed increases with the gain of neuronal activation functions and the number of pre-embedded memories, as both enhance the variance of spontaneous neural fluctuations. Additionally, the formulae reveal which input/output relationships are easier to learn, aligning with experimental data. Thus, our results provide a theoretical foundation for the quantitative relationship between pre-learning neural activity fluctuations and learning speed, offering insights into a range of empirical observations.

cond-mat.dis-nn↗