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Nen Saito

Publications and source records attributed to Nen Saito.

15 recordsLinked to original sources

Dense HeLa cell monolayers remain liquid-like despite strong crowding

Collective dynamics in dense cell monolayers are governed by the interplay between crowding and cellular motility. Although increasing density can slow cellular motion and promote glass-like behaviour, the dynamical state of dense HeLa monolayers remains unclear. Here, we combine in vitro time-lapse imaging of HeLa cell monolayers with simulations of a deformable active-cell model to examine how cell density and motility regulate collective relaxation. Within the experimentally accessible density and time ranges, untreated HeLa monolayers remain liquid-like: structural relaxation progressively slows down with increasing density but remains observable throughout the investigated range. Under low-nutrient conditions, cell motility is strongly reduced, and structural relaxation becomes substantially slower. To elucidate the mechanisms underlying these experimental observations, we further performed simulations using a deformable-cell model. The model qualitatively reproduces the density-dependent increase in structural relaxation time and further shows that reducing self-propulsion promotes long-lived caging dynamics at high packing fractions. These results show that dense HeLa monolayers can sustain slow, heterogeneous, yet relaxing collective dynamics under untreated conditions, and indicate that persistent cellular motility is an important factor in maintaining structural relaxation at high density, which may provide insight into the metastatic potential of cancer cells.

cond-mat.soft

Spatially Structured Flocking in a Proliferating Population of Self-Propelled Organisms without Explicit Alignment Interactions

While it is well established that self-propelled particles with alignment interactions can exhibit orientational order, the impact of self-replication and annihilation, which are key characteristics in cellular systems, on spatiotemporal order remains poorly understood. To explore the interplay between self-propulsion and self-replication, we introduce the active Brownian bug (ABB) model, in which self-propelled agents undergo stochastic, density-dependent replication and constant-rate death. Despite the absence of alignment interactions, the system exhibits flocking behavior characterized by high orientational order, while maintaining ordered hexagonal arrays. This emergent order arises from stochastic birth and death processes, offering a novel mechanism for flocking in proliferating cellular populations.

q-bio.PE

Attraction-Induced Cluster Fragmentation and Local Alignment in Active Particle Systems

We numerically studied active Brownian particles with attractive interactions. Contrary to our intuition, the attractive force between particles disrupts the formation of a single cluster observed in motility-induced phase separation, giving rise to a multi-cluster state characterized by a power-law distribution of cluster sizes. Remarkably, the self-propulsion directions spontaneously align within each cluster, resulting in enhanced cluster motility despite the absence of alignment interactions. This study revealed the intricate role of attractive interactions in the aggregation of motile systems.

cond-mat.soft

Active Deformable Cells Undergo Cell Shape Transition Associated with Percolation of Topological Defects

Cell deformability is an essential determinant for tissue-scale mechanical nature, such as fluidity and rigidity, and is thus crucial for understanding tissue homeostasis and stable developmental processes. However, numerical simulations for the collective dynamics of cells with arbitral cell deformations akin to mesenchymal, ameboid, and epithelial cells in a non-confluent situation need high computational costs and are still challenging. Here we propose a new method that allows us to study significantly larger numbers of cells than existing methods. Using the method, we investigated the densely packed active cell population interacting via excluded volume interactions, and discovered the emergence of two fluid phases in deformable cell populations, a soft-fluid phase with drastically deformed cell shapes and a fluid phase with circular cell shapes. The transition between these two phases is characterized by the percolation of topological defects, which is experimentally testable.

q-bio.TO

Rare-Event Sampling Analysis Uncovers the Fitness Landscape of the Genetic Code

The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure. Theoretical studies comparing the SGC and numerically created hypothetical random genetic codes have suggested that the SGC has been subject to strong selection pressure for being robust against translation errors. However, these prior studies have searched for random genetic codes in only a small subspace of the possible code space due to limitations in computation time. Thus, how the genetic code has evolved, and the characteristics of the genetic code fitness landscape, remain unclear. By applying multicanonical Monte Carlo, an efficient rare-event sampling method, we efficiently sampled random codes from a much broader random ensemble of genetic codes than in previous studies, estimating that only one out of every $10^{20}$ random codes is more robust than the SGC. This estimate is significantly smaller than the previous estimate, one in a million. We also characterized the fitness landscape of the genetic code that has four major fitness peaks, one of which includes the SGC. Furthermore, genetic algorithm analysis revealed that evolution under such a multi-peaked fitness landscape could be strongly biased toward a narrow peak, in an evolutionary path-dependent manner.

q-bio.PE

The advantage of leakage of essential metabolites and resultant symbiosis of diverse species

Microbial communities display extreme diversity. A variety of strains or species coexist even when limited by a single resource. It has been argued that metabolite secretion creates new niches and facilitates such diversity. Nonetheless, it is still a controversial topic why cells secrete even essential metabolites so often; in fact, even under isolation conditions, microbial cells secrete various metabolites, including those essential for their growth. First, we demonstrate that leaking essential metabolites can be advantageous. If the intracellular chemical reactions include multibody reactions like catalytic reactions, this advantageous leakage of essential metabolites is possible and indeed typical for most metabolic networks via "flux control" and "growth-dilution" mechanisms; the later is a result of the balance between synthesis and growth-induced dilution with autocatalytic reactions. Counterintuitively, the mechanisms can work even when the supplied resource is scarce. Next, when such cells are crowded, the presence of another cell type, which consumes the leaked chemicals is beneficial for both cell types, so that their coexistence enhances the growth of both. The latter part of the paper is devoted to the analysis of such unusual form of symbiosis: "consumer" cell types benefit from the uptake of metabolites secreted by "leaker" cell types, and such consumption reduces the concentration of metabolites accumulated in the environment; this environmental change enables further secretion from the leaker cell types. This situation leads to frequency-dependent coexistence of several cell types, as supported by extensive simulations. A new look at the diversity in a microbial ecosystem is thus presented.

q-bio.PE

Motif Analysis for Small-Number Effects in Chemical Reaction Dynamics

The number of molecules involved in a cell or subcellular structure is sometimes rather small. In this situation, ordinary macroscopic-level fluctuations can be overwhelmed by non-negligible large fluctuations, which results in drastic changes in chemical-reaction dynamics and statistics compared to those observed under a macroscopic system (i.e., with a large number of molecules). In order to understand how salient changes emerge from fluctuations in molecular number, we here quantitatively define small-number effect by focusing on a `mesoscopic' level, in which the concentration distribution is distinguishable both from micro- and macroscopic ones, and propose a criterion for determining whether or not such an effect can emerge in a given chemical reaction network. Using the proposed criterion, we systematically derive a list of motifs of chemical reaction networks that can show small-number effects, which includes motifs showing emergence of the power law and the bimodal distribution observable in a mesoscopic regime with respect to molecule number. The list of motifs provided herein is helpful in the search for candidates of biochemical reactions with a small-number effect for possible biological functions, as well as for designing a reaction system whose behavior can change drastically depending on molecule number, rather than concentration.

q-bio.MN

Symbiotic Cell Differentiation and Cooperative Growth in Multicellular Aggregates

As cells grow and divide under a given environment, they become crowded and resources are limited, as seen in bacterial biofilms and multicellular aggregates. These cells often show strong interactions through exchanging chemicals, as in quorum sensing, to achieve mutualism. Here, to achieve stable division of labor, three properties are required. First, isogenous cells differentiate into several types. Second, this aggregate of distinct cell types shows better growth than that of isolated cells, by achieving division of labor. Third, this cell aggregate is robust in the number distribution of differentiated cell types. We here address how cells acquire the ability of cell differentiation and division of labor simultaneously, which is also connected with the robustness of a cell society. For this purpose, we developed a dynamical-systems model of cells consisting of chemical components with intracellular catalytic reaction dynamics. The reactions convert external nutrients into internal components for cellular growth, and the divided cells interact via chemical diffusion. We found that cells sharing an identical catalytic network spontaneously differentiate via induction from cell-cell interactions, and then achieve division of labor, enabling a higher growth rate than that in the unicellular case. This symbiotic differentiation emerged for a class of reaction networks with limited resources and strong cell-cell interactions. Then, robustness in the cell type distribution was achieved, while instability of collective growth could emerge even among the cooperative cells when the internal reserves of products were dominant. The present mechanism is simple and general as a natural result of interacting cells with resource limitation, and is consistent with the observed behaviors and forms of several aggregates of unicellular organisms.

q-bio.CB

Theoretical Analysis of Discreteness-Induced Transition in Autocatalytic Reaction Dynamics

Transitions in the qualitative behavior of chemical reaction dynamics with a decrease in molecule number have attracted much attention. Here, a method based on a Markov process with a tridiagonal transition matrix is applied to the analysis of this transition in reaction dynamics. The transition to bistability due to the small-number effect and the mean switching time between the bistable states are analytically calculated in agreement with numerical simulations. In addition, a novel transition involving the reversal of the chemical reaction flow is found in the model under an external flow, and also in a three-component model. The generality of this transition and its correspondence to biological phenomena are also discussed.

q-bio.MN

Multicanonical MCMC for Sampling Rare Events

Multicanonical MCMC (Multicanonical Markov Chain Monte Carlo; Multicanonical Monte Carlo) is discussed as a method of rare event sampling. Starting from a review of the generic framework of importance sampling, multicanonical MCMC is introduced, followed by applications in random matrices, random graphs, and chaotic dynamical systems. Replica exchange MCMC (also known as parallel tempering or Metropolis-coupled MCMC) is also explained as an alternative to multicanonical MCMC. In the last section, multicanonical MCMC is applied to data surrogation; a successful implementation in surrogating time series is shown. In the appendices, calculation of averages and normalizing constant in an exponential family, phase coexistence, simulated tempering, parallelization, and multivariate extensions are discussed.

cond-mat.stat-mech

Robustness Leads Close to the Edge of Chaos in Coupled Map Networks: toward the understanding of biological networks

Dynamics in biological networks are in general robust against several perturbations. We investigate a coupled map network as a model motivated by gene regulatory networks and design systems which are robust against phenotypic perturbations (perturbations in dynamics), as well as systems which are robust against mutation (perturbations in network structure). To achieve such a design, we apply a multicanonical Monte Carlo method. Analysis based on the maximum Lyapunov exponent and parameter sensitivity shows that systems with marginal stability, which are regarded as systems at the edge of chaos, emerge when robustness against network perturbations is required. This emergence of the edge of chaos is a self-organization phenomenon and does not need a fine tuning of parameters.

q-bio.MN

The Baldwin effect under multi-peaked fitness landscapes: Phenotypic fluctuation accelerates evolutionary rate

Phenotypic fluctuations and plasticity can generally affect the course of evolution, a process known as the Baldwin effect. Several studies have recast this effect and claimed that phenotypic plasticity acceler- ates evolutionary rate (the Baldwin expediting effect); however, the validity of this claim is still controversial. In this study, we investi- gate the evolutionary population dynamics of a quantitative genetic model under a multi-peaked fitness landscape, in order to evaluate the validity of the effect. We provide analytical expressions for the evolutionary rate and average population fitness. Our results indicate that under a multi-peaked fitness landscape, phenotypic fluctuation always accelerates evolutionary rate, but it decreases the average fit- ness. As an extreme case of the trade-off between the rate of evolution and average fitness, phenotypic fluctuation is shown to accelerate the error catastrophe, in which a population fails to sustain a high-fitness peak. In the context of our findings, we discuss the role of phenotypic plasticity in adaptive evolution.

q-bio.PE

Multicanonical sampling of rare events in random matrices

A method based on multicanonical Monte Carlo is applied to the calculation of large deviations in the largest eigenvalue of random matrices. The method is successfully tested with the Gaussian orthogonal ensemble (GOE), sparse random matrices, and matrices whose components are subject to uniform density. Specifically, the probability that all eigenvalues of a matrix are negative is estimated in these cases down to the values of $\sim 10^{-200}$, a region where naive random sampling is ineffective. The method can be applied to any ensemble of matrices and used for sampling rare events characterized by any statistics.

cond-mat.stat-mech

Evolution of Genetic Redundancy : The Relevance of Complexity in Genotype-Phenotype Mapping

Genetic redundancy is ubiquitous and can be found in any organism. However, it has been argued that genetic redundancy reduces total population fitness, and therefore, redundancy is unlikely to evolve. In this letter, we study an evolutionary model with high-dimensional genotype-phenotype mapping (GPM) to investigate the relevance of complexity in GPM to the evolution of genetic redundancy. By applying the replica method to deal with quenched randomness, the redundancy dependence of the fitness is analytically obtained, which demonstrates that genetic redundancy can indeed evolve, provided that the GPM is complex. Our result provides a novel insight into how genetic redundancy evolves.

q-bio.PE

Probability of graphs with large spectral gap by multicanonical Monte Carlo

Graphs with large spectral gap are important in various fields such as biology, sociology and computer science. In designing such graphs, an important question is how the probability of graphs with large spectral gap behaves. A method based on multicanonical Monte Carlo is introduced to quantify the behavior of this probability, which enables us to calculate extreme tails of the distribution. The proposed method is successfully applied to random 3-regular graphs and large deviation probability is estimated.

cond-mat.stat-mech