arXiv subjects

Kunihiko Kaneko

Publications and source records attributed to Kunihiko Kaneko.

At least 37 records · Page 2Linked to original sources

arXiv2024

Dynamical Theory for Adaptive Systems

The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former's dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin-Siggia-Rose-De Dominicis-Janssen (MSRDJ) to analyse nonequilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

q-bio.PE↗

arXiv2024

Formation of human kinship structures depending on population size and cultural mutation rate

How does social complexity depend on population size and cultural transmission? Kinship structures in traditional societies provide a fundamental illustration, where cultural rules between clans determine people's marriage possibilities. Here we propose a simple model of kinship interactions that considers kin and in-law cooperation and sexual rivalry. In this model, multiple societies compete. Societies consist of multiple families with different cultural traits and mating preferences. These values determine interactions and hence the growth rate of families and are transmitted to offspring with mutations. Through a multilevel evolutionary simulation, family traits and preferences are grouped into multiple clans with inter-clan mating preferences. It illustrates the emergence of kinship structures as the spontaneous formation of interdependent cultural associations. Emergent kinship structures are characterized by the cycle length of marriage exchange and the number of cycles in society. We numerically and analytically clarify their parameter dependence. The relative importance of cooperation versus rivalry determines whether attraction or repulsion exists between families. Different structures evolve as locally stable attractors. The probabilities of formation and collapse of complex structures depend on the number of families and the mutation rate, showing characteristic scaling relationships. It is now possible to explore macroscopic kinship structures based on microscopic interactions, together with their environmental dependence and the historical causality of their evolution. We propose the basic causal mechanism of the formation of typical human social structures by referring to ethnographic observations and concepts from statistical physics and multilevel evolution. Such interdisciplinary collaboration will unveil universal features in human societies.

physics.soc-ph↗

arXiv2024

Emergence of economic and social disparities through competitive gift-giving

Several tiers of social organization with varying economic and social disparities have been observed. However, a quantitative characterization of the types and the causal mechanisms for the transitions have hardly been explained. While anthropologists have emphasized that gift exchange, rather than market exchange, prevails in traditional societies and shapes social relations, few mathematical studies have explored its consequences for social organizations. In this study, we present a simple model of competitive gift-giving that describes how gifts bring goods to the recipient and honor to the donor, and simulate social change. Numerical simulations and an analysis of the corresponding mean-field theory demonstrate the transitions between the following four phases with different distribution shapes of wealth and social reputation: the band, without economic or social disparities; the tribe, with economic but without social disparities; the chiefdom, with both; and the kingdom, with economic disparity and weak social disparity except for an outlier, namely, the ``monarch''. The emergence of strong disparities is characterized by power law distributions and is attributed to the ``rich get richer'' process. In contrast, the absence of such a process leads to exponential distributions due to random fluctuations. The phases depend on the parameters characterizing the frequency and scale of gift interactions. Our findings provide quantitative criteria for classifying social organizations based on economic and social disparities, consistent with anthropological theory and empirical observations. Thus, we propose empirically measurable explanatory variables and characteristics for the evolution of social organizations. The constructive model, guided by social scientific theory, can provide the basic mechanistic explanation of social evolution and integrate theories of the social sciences.

physics.soc-ph↗

arXiv2024

Constructing universal Phenomenology for biological cellular systems: An idiosyncratic review on evolutionary dimensional reduction

Possibility to establish macroscopic phenomenological theory for biological systems, akin to the akin to the well-established framework of thermodynamics, is briefly reviewed. We introduce the concept of an evolutionary fluctuation-response relationship, which highlights the need for a tight correlation between the variance in phenotypic traits caused by genetic mutations and by internal noise. We provide a distribution theory that allows us to derive these relationships, which suggests that the changes in traits resulting from adaptation and evolution are considerably constrained within a lower-dimensional space. We explore the reasons behind this dimensional reduction, focusing on the constraints posed by the requirements for steady growth and robustness achieved through the evolutionary process. We draw support from recent laboratory and numerical experiments to substantiate our claims. Universality of evolutionary dimensional reduction is presented, whereas potential theoretical formulations for it are discussed. We conclude by briefly considering the prospects of establishing a macroscopic framework that characterizes biological robustness and irreversibility in cell differentiation, as well as an ideal cell model.

physics.bio-ph↗

arXiv2023

Solvable Neural Network Model for Input-Output Associations: Optimal Recall at the Onset of Chaos

In neural information processing, an input modulates neural dynamics to generate a desired output. To unravel the dynamics and underlying neural connectivity enabling such input-output association, we proposed an exactly soluble neural-network model with a connectivity matrix explicitly consisting of inputs and required outputs. An analytic form of the response upon the input is derived, whereas three distinctive types of responses including chaotic dynamics as bifurcation against input strength are obtained depending on the neural sensitivity and number of inputs. Optimal performance is achieved at the onset of chaos, and the relevance of the results to cognitive dynamics is discussed.

q-bio.NC↗

arXiv2023

Evolutionary Shaping of Low-Dimensional Path Facilitates Robust and Plastic Switching Between Phenotypes

Biological systems must be robust for stable function against perturbations, but robustness alone is not sufficient. The ability to switch between appropriate states (phenotypes) in response to different conditions is essential for biological functions. How are robustness and plasticity simultaneously acquired through evolution? We examine the evolution of genotypes that realize plastic switching between two phenotypes upon external inputs, as well as stationary expressions of phenotypes. We introduce a statistical physics model consisting of spins, with active and regulatory sites, which are distinct from each other. We represent the phenotype and genotype as spin configurations and the spin-spin interactions, respectively. The fitness for selection is given so that it takes a higher value as more of the active sites take two requested spin configurations depending on the regulation. We numerically evolve the interaction matrix by changing them with mutations and selection of those with higher fitness. Our numerical simulations show that characteristic genotypes evolve slightly above the transition temperature between replica symmetric and replica symmetry breaking phase. These genotypes shape two spin configurations separately depending on the regulation, where the two phenotypes are dominantly represented by the genotypes' first and second eigenmodes, and smooth switching of two phenotypes are achieved by following a one-dimensional path connecting the two phenotypes. Upon changes in regulations, spin configurations are attracted to this path, which allows for robust and plastic switching between the two phenotypes. The statistical-physics analysis show that the free energy landscape has a valley along the switching path. Our finding indicates that the compatibility of the robustness and plasticity is acquired by the evolution of the low-dimensionality in the phenotype space.

q-bio.PE↗

arXiv2023

Universal Transitions between Growth and Dormancy via Intermediate Complex Formation

A simple cell model consisting of a catalytic reaction network with intermediate complex formation is numerically studied. As nutrients are depleted, the transition from the exponential growth phase to the growth-arrested dormant phase occurs along with hysteresis and a lag time for growth recovery. This transition is caused by the accumulation of intermediate complexes, leading to the jamming of reactions and the diversification of components. These properties are generic in random reaction networks, as supported by dynamical systems analyses of corresponding mean-field models.

q-bio.CB↗

arXiv2023

Prediction of Cross-Fitness for Adaptive Evolution to Different Environmental Conditions: Consequence of Phenotypic Dimensional Reduction

How adaptive evolution to one environmental stress improves or suppresses adaptation to another is an important problem in evolutionary biology. For instance, in microbiology, the evolution of bacteria to be resistant to different antibiotics is a critical issue that has been investigated as cross-resistance. In fact, recent experiments on bacteria have suggested that the cross-resistance of their evolution to various stressful environments can be predicted by the changes to their transcriptome upon application of stress. However, there are no studies so far that explain a possible theoretical relationship between cross-resistance and changes in the transcriptome, which causes high-dimensional changes to cell phenotype. Here, we show that a correlation exists between fitness change in stress tolerance evolution and response to the environment, using a cellular model with a high-dimensional phenotype and establishing the relationship theoretically. The present results allow for the prediction of evolution from transcriptome information in response to different stresses before evolution. The relevance of this to microbiological evolution experiments is discussed.

q-bio.PE↗

arXiv2022

Double-replica theory for evolution of genotype-phenotype interrelationship

The relationship between genotype and phenotype plays a crucial role in determining the function and robustness of biological systems. Here the evolution progresses through the change in genotype, whereas the selection is based on the phenotype, and genotype-phenotype relation also evolves. Theory for such phenotypic evolution remains poorly-developed, in contrast to evolution under the fitness landscape determined by genotypes. Here we provide statistical-physics formulation of this problem by introducing replicas for genotype and phenotype. We apply it to an evolution model, in which phenotypes are given by spin configurations; genotypes are interaction matrix for spins to give the Hamiltonian, and the fitness depends only on the configuration of a subset of spins called target. We describe the interplay between the genetic variations and phenotypic variances by noise in this model by our new approach that extends the replica theory for spin-glasses to include spin-replica for phenotypes and coupling-replica for genotypes. Within this framework we obtain a phase diagram of the evolved phenotypes against the noise and selection pressure, where each phase is distinguished by the fitness and overlaps for genotypes and phenotypes. Among the phases, robust fitted phase, relevant to biological evolution, is achieved under the intermediate level of noise (temperature), where robustness to noise and to genetic mutation are correlated, as a result of replica symmetry. We also find a trade-off between maintaining a high fitness level of phenotype and acquiring a robust pattern of genes as well as the dependence of this trade-off on the ratio between the size of the functional (target) part to that of the remaining non-functional (non-target) one. The selection pressure needed to achieve high fitness increases with the fraction of target spins.

q-bio.PE↗

arXiv2022

Bayesian inference is facilitated by modular neural networks with different time scales

Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference, the prior distribution must be shaped by sampling noisy external inputs. However, the mechanism by which neural activities represent such distributions has not yet been elucidated. In this study, we demonstrated that the neural networks with modular structures including fast and slow modules effectively represented the prior distribution in performing accurate Bayesian inferences. Using a recurrent neural network consisting of a main module connected with input and output layers and a sub-module connected only with the main module and having slower neural activity, we demonstrated that the modular network with distinct time scales performed more accurate Bayesian inference compared with the neural networks with uniform time scales. Prior information was represented selectively by the slow sub-module, which could integrate observed signals over an appropriate period and represent input means and variances. Accordingly, the network could effectively predict the time-varying inputs. Furthermore, by training the time scales of neurons starting from networks with uniform time scales and without modular structure, the above slow-fast modular network structure spontaneously emerged as a result of learning wherein prior information was selectively represented in the slower sub-module. These results explain how the prior distribution for Bayesian inference is represented in the brain, provide insight into the relevance of modular structure with time scale hierarchy to information processing, and elucidate the significance of brain areas with slower time scales.

q-bio.NC↗

arXiv2022

Generic Adaptation by Fast Chaotic Exploration and Slow Feedback Fixation

Living systems adapt to various environmental conditions by changing their internal states. Inspired by gene expression and epigenetic modification dynamics, we herein propose a generic mechanism for adaptation by combining fast oscillatory dynamics and a slower feedback fixation process. Through extensive model simulations, we reveal that fast chaotic dynamics serve as global searching for adapted states fixed by slower dynamics. The mechanism improves as the number of elements is increased. Relevance to cellular adaptation and optimization in artificial neural networks is also discussed herein.

q-bio.QM↗

arXiv2022

Transition of Social Organisations Driven by Gift Relationship

Anthropologists have observed gift relationships that establish social relations as well as the transference of goods in many human societies. The totality of such social relations constitutes the network. Social scientists have analysed different types of social organisations with their characteristic networks. However, the factors and mechanisms that cause the transition between these types have hardly been explained. Here, we focus on the gift as the driving force for such changes. We build the model by idealising gift interactions and simulating the consequent social change due to long-term massive interactions. We demonstrate the emergence of disparities and various social organisations depending on the frequency of the gift, consistent with the empirical data. The constructive simulation study, as presented here, explains how people's interactions shape various social structures in response to environmental conditions. Combined with empirical studies, this could contribute to the formulation of a general theory in the social sciences.

physics.soc-ph↗

arXiv2022

Error Catastrophe Can Be Avoided by Proofreading Innate to Template-Directed Polymerization

An important issue for the origins of life is ensuring the accurate maintenance of information in replicating polymers in the face of inevitable errors. Here, we investigated how this maintenance depends on reaction kinetics by incorporating the elementary steps of polymerization into the population dynamics of polymers. We found that template-directed polymerization entails an inherent error-correction mechanism akin to kinetic proofreading, generating long polymers that are more tolerant to an error catastrophe. Because this mechanism does not require enzymes, it is likely to operate under broad prebiotic conditions.

physics.bio-ph↗

arXiv2021

A scaling law of multilevel evolution: how the balance between within- and among-collective evolution is determined

Numerous living systems are hierarchically organised, whereby replicating components are grouped into reproducing collectives -- e.g., organelles are grouped into cells, and cells are grouped into multicellular organisms. In such systems, evolution can operate at two levels: evolution among collectives, which tends to promote selfless cooperation among components within collectives (called altruism), and evolution within collectives, which tends to promote cheating among components within collectives. The balance between within- and among-collective evolution thus exerts profound impacts on the fitness of these systems. Here, we investigate how this balance depends on the size of a collective (denoted by $N$) and the mutation rate of components ($m$) through mathematical analyses and computer simulations of multiple population genetics models. We first confirm a previous result that increasing $N$ or $m$ accelerates within-collective evolution relative to among-collective evolution, thus promoting the evolution of cheating. Moreover, we show that when within- and among-collective evolution exactly balance each other out, the following scaling relation generally holds: $Nm^α$ is a constant, where scaling exponent $α$ depends on multiple parameters, such as the strength of selection and whether altruism is a binary or quantitative trait. This relation indicates that although $N$ and $m$ have quantitatively distinct impacts on the balance between within- and among-collective evolution, their impacts become identical if $m$ is scaled with a proper exponent. Our results thus provide a novel insight into conditions under which cheating or altruism evolves in hierarchically-organised replicating systems.

q-bio.PE↗

arXiv2021

Evolution of family systems and resultant socio-economic structures

Families form the basis of society, and anthropologists have characterised various family systems. This study developed a multi-level evolutionary model of pre-industrial agricultural societies to simulate the evolution of family systems and determine how each of them adapts to environmental conditions and forms a characteristic socio-economic structure. In the model, competing societies evolve, which themselves comprise multiple evolving families that grow through family labour. Each family has two strategy parameters: the time children leave the parental home and the distribution of inheritance among siblings. The evolution of these parameters demonstrates that four basic family systems emerge; families can become either nuclear or extended, and have either an equal or unequal inheritance distribution. Nuclear families emerge where land resources are sufficient, whereas extended families emerge where land resources are limited. Equal inheritance emerges where the amount of wealth required for a family to survive is large, whereas unequal inheritance emerges where the required wealth is small. Analyses on the wealth distribution of families demonstrate a higher level of poverty in extended families, and that the accumulation of wealth is accelerated for unequal inheritance. By comparing wealth distributions in the model with historical data, family systems are associated with characteristic economic structures and modern social ideologies. Empirical data analyses using the cross-cultural ethnographic database verify the theoretical relationship between the environmental conditions, family systems, and socio-economic structures. Theoretical studies by this simple constructive model, as presented here, will integrate the understandings of family systems in evolutionary anthropology, demography, and socioeconomic histories.

physics.soc-ph↗

arXiv2021

Exploitation by asymmetry of information reference in coevolutionary learning in prisoner's dilemma game

Mutual relationships, such as cooperation and exploitation, are the basis of human and other biological societies. The foundations of these relationships are rooted in the decision making of individuals, and whether they choose to be selfish or altruistic. How individuals choose their behaviors can be analyzed using a strategy optimization process in the framework of game theory. Previous studies have shown that reference to individuals' previous actions plays an important role in their choice of strategies and establishment of social relationships. A fundamental question remains as to whether an individual with more information can exploit another who has less information when learning the choice of strategies. Here we demonstrate that a player using a memory-one strategy, who can refer to their own previous action and that of their opponent, can be exploited by a reactive player, who only has the information of the other player, based on mutual adaptive learning. This is counterintuitive because the former has more choice in strategies and can potentially obtain a higher payoff. We demonstrated this by formulating the learning process of strategy choices to optimize the payoffs in terms of coupled replicator dynamics and applying it to the prisoner's dilemma game. Further, we show that the player using a memory-one strategy, by referring to their previous experience, can sometimes act more generous toward the opponent's defection, thereby accepting the opponent's exploitation. Mainly, we found that through adaptive learning, a player with limited information usually exploits the player with more information, leading to asymmetric exploitation.

math.OC↗

arXiv2021

Dynamical-systems theory of cellular reprogramming

In cellular reprogramming, almost all epigenetic memories of differentiated cells are erased by the overexpression of few genes, regaining pluripotency, potentiality for differentiation. Considering the interplay between oscillatory gene expression and slower epigenetic modifications, such reprogramming is perceived as an unintuitive, global attraction to the unstable manifold of a saddle, which represents pluripotency. The universality of this scheme is confirmed by the repressilator model, and by gene regulatory networks randomly generated and those extracted from embryonic stem cells.

physics.bio-ph↗

arXiv2021

Short term memory by transient oscillatory dynamics in recurrent neural networks

Despite the significance of short-term memory in cognitive function, the process of encoding and sustaining the input information in neural activity dynamics remains elusive. Herein, we unveiled the significance of transient neural dynamics to short-term memory. By training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristics of the short-term memory mechanism were obtained in which the input information was encoded in the amplitude of transient oscillations, rather than the stationary neural activities. This transient trajectory was attracted to a slow manifold, which permitted the discarding of irrelevant information. Additionally, we investigated the process by which the dynamics acquire robustness to noise. In this transient oscillation, the robustness to noise was obtained by a strong contraction of the neural states after perturbation onto the manifold. This mechanism works for several neural network models and tasks, which implies its relevance to neural information processing in general.

q-bio.NC↗