Program

Meeting Program and Abstracts

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Day 1st: Aug. 3rd

Day 2nd: Aug. 4th

Day 3rd: Aug. 5th

Abstract

Evolution of complexity in an artificial RNA replication system
Ryo Mizuuchi (Waseda Univ.)

During the origins of life, self-replicating molecules such as RNA are believed to have evolved into complex living systems by continuously expanding information and functions. Theoretically, such evolutionary complexification could occur through (1) the successive appearance of novel species that interact with one another to form replication networks and (2) their following integration into a single large unit (e.g., individual genes to a genome). In my talk, I will present our progress in demonstrating possible complexification pathways through long-term evolution experiments of an artificial RNA replication system. The system consists of an RNA that encodes an RNA replicase and a cell-free translation system, enabling the RNA to replicate using its encoded replicase. In a recent evolution experiment, a single ancestral RNA species evolved into a quasi-stable replicator network with diverse interactions, comprising up to five RNA species [1]. The network included a highly cooperative RNA that helped replicate all other members, and parasitic species, both of which seemed essential for maintaining the network. We also designed an RNA ecosystem consisting of two species that cooperate for their replication by expressing replication or metabolic enzymes. In another evolution experiment with these two RNAs, we detected their integration into a single long RNA, allowing for replication without fragile cooperation [2]. Overall, our studies provide evidence that Darwinian evolution drives complexification at molecular levels, paving the way toward the emergence of living systems.

References:
[1] Mizuuchi, R., Furubayashi, T., Ichihashi, N. Evolutionary transition from a single RNA replicator to a multiple replicator network. Nat. Commun., 13, 1460 (2022).
[2] Ueda, K, Mizuuchi, R., Ichihashi, N. Emergence of linkage between cooperative RNA replicators encoding replication and metabolic enzymes thorough experimental evolution. bioRxiv.doi: 10.1101/2022.10.11.511852

Entangled gene regulatory networks with cooperative expression endow responses to unforeseen environmental changes
Masayo Inoue (Kyushu Institute of Technology)

Cells often have appropriate, robust responses to environmental changes, including those not previously experienced. Through numerical evolution of gene regulatory networks satisfying given input-output relationship, we have uncovered that complex entangled networks consisting of sloppy units can generally make robust adaptive responses even to unforeseen environmental changes. These behaviors are supported by global, correlated responses across genes that are similar for diverse input signals. In addition, there are several detours in the regulatory network to compensate for the sloppiness of each unit. By taking advantage of the averaging over such detours, the network shows a higher robustness to environmental and intracellular noise, as well as to mutations in the network. Moreover, such complex entangled networks with cooperative responses achieved an appropriate response to unforeseen environmental changes they have never experienced before, prior to the evolutionary rewiring of the networks. This is because many genes exhibit similar dynamic expression responses irrespective of inputs, including unforeseen inputs. The results explain why cells adopt complex gene regulatory networks and exhibit global expression changes, as is consistent with microbial experiments with transcriptome and network analyses. This investigation provides insights into how cells survive fluctuating and unforeseen environmental changes, together with a universal conceptual framework to go beyond the standard picture using a combination of network motifs. 

References
M. Inoue and K. Kaneko, Entangled gene regulatory networks with cooperative expression endow robust adaptive responses to unforeseen environmental changes, Physical Review Research, Vol.3, 033183 (2021).
M. Inoue and K. Kaneko, Cooperative reliable response from sloppy gene-expression dynamics, Europhysics Letters, Vol.124, 38002 (2018).

Hysteresis and memory formation in evolutionary processes on changing 
fitness landscapes
Joachim Krug (Univ. of Cologne)

Biological evolution is governed by the fitness landscape, a map from the genetic sequence of an organism to its fitness.  Here fitness denotes some quantitative measure of reproductive success, such as the expected number of offspring. A fitness landscape depends on the organism’s environment, and evolution in changing environments is still poorly understood. After introducing the concept of fitness landscapes and their mathematical description, the talk will focus on a particular model of antibiotic resistance evolution in bacteria, where the drug concentration is an environmental parameter. Tradeoffs between adaptation to low and high concentration lead to a rugged landscape with an exponentially large number of fitness peaks. With evolutionary dynamics that follow fitness gradients, resistance evolution under slowly changing antibiotic concentration resembles the zero temperature dynamics of  a disordered spin system under quasistatic driving. Specifically, the set of genetic sequences that form a fitness peak at some concentration maps exactly to the metastable states in an equivalent Preisach system, a paradigmatic model of hysteresis in random  magnets. Making use of the conceptual tool of state transition graphs developed in the context of driven disordered systems, we quantify the degree of genotypic and phenotypic reversibility in the response of the population to antibiotic concentration  cycling, and ask to what extent a memory of past concentration changes is stored in the current genetic sequence. The talk is based on joint work with Suman G. Das and Muhittin Mungan.

Machine learning predicts biological system evolution by gene gains and losses
Naoki Konno¹, Wataru Iwasaki^1,2
(¹Graduate School of Science, The Univ. of Tokyo, ²Graduate School of Frontier Scinences, The Univ. of Tokyo)

Evolutionary processes can be regarded as trajectories of ever-changing genomes, where their bifurcations and dead-ends are speciation and extinctions, respectively. To reveal the laws behind their dynamics, whether genome evolution is stochastic or deterministic, or how much genome evolution is predictable, is a fundamental problem. While the predictability of short-term and sequence-level evolution has been investigated, that of long-term and system-level evolution has yet to be systematically examined. Here, we show that the bacterial evolution of metabolic systems through gene gains and losses is generally predictable by applying ancestral gene content reconstruction and machine learning to ~3000 bacterial genomes. Our framework, Evodictor, successfully predicted gene gain and loss events on branches of the reference phylogenetic tree, suggesting universally shared evolutionary pressures and constraints on metabolic systems. We herein present the mathematical model of Evodictor and our findings on evolutionary rules of bacterial metabolic systems from physiological perspectives, together with a potential application of Evodictor to strategic pathogen control and genome engineering.

Optimal Information Processing and Control in Living Systems with limited resources
Tetsuya J. Kobayashi (Univ. of Tokyo)

Biological systems acquire environmental information and control their behavior to respond adaptively to various environments. Not only complex organisms like humans, but even simple organisms like bacteria, can sense extremely faint chemicals with stochastic receptors, process them in a reaction system, and control their behaviors appropriately. However, the available resources such as memory size, energy, etc, that simple organisms can exploit are severely limited compared with higher organisms like human beings. Thus, the optimal sensing and control of simple organisms should be different from those of higher organisms. However, conventional information theory, filtering theory and optimal control theory cannot directly address such resource limitations presumably because the theories were developed in the era when the available computational resources of modern computers increased continuously. In this presentation, we introduce the memory-limited partially observable optimal control, a new theoretical framework developed by our group to address this problem. We demonstrate the effectiveness and consistency of our theory with the conventional one.

Landscape and flux theory with experiemental quantifications for nonequilibrium biological systems
Jin Wang (Stony Brook Univ.)

Life is characterized by a myriad of complex dynamic processes allowing organisms to grow,
reproduce, and evolve. Physical approaches for describing systems out of thermodynamic equilibrium have been increasingly applied to living systems, which often exhibit phenomena not found in those traditionally studied in physics. Spectacular advances in experimentation during the last decade or two, for example, in microscopy, single-cell dynamics, in the reconstruction of subcellular and multicellular systems outside of living organisms, and in high throughput data acquisition, have yielded an unprecedented wealth of data on cell dynamics, genetic regulation, and organismal development. These data have motivated the development and refinement of concepts and tools to dissect the physical mechanisms underlying biological processes. Notably, landscape and flux theory has proven useful in this endeavor. Based on the landscape and flux theory, significant progress has been made in unraveling the principles underlying efficient energy transport in photosynthesis, cellular regulatory networks, embryonic development and cancer, neural network dynamics, population dynamics and ecology, as well as aging, immune responses, and evolution. Here recent advances in nonequilibrium dynamics and thermodynamics in terms of the landscape and flux theory are reviewd and their application to biological systems is surveyed. Many of these results are expected to be important cornerstones as the field continues to build our understanding of life.

A problem halved: Merging disciplines to tackle cooperative cell migration.
Luke Tweedy (Univ. of Glasgow)

While cell migration is heavily studied at the level of the individual cell, many natural examples involve the coordinated movement of cells in great numbers. Inflammatory responses, cancer metastasis, the aggregation of amoebae and cell arrangement during development are all fascinating examples where understanding the dynamic signals between cells is crucial to understanding their movement. As dynamical systems can be counterintuitive, we have found that predictive simulations are a vital guide to how we design experiments. Similarly, experimental experience helps us design simulations that are focused on testable predictions. I will discuss this approach in the context of several extraordinary examples: how environmental topology shapes collective chemotaxis, how competition between attractants can lead to repulsion from both, and how a pigment cell can’t cross a mouse on its own.

Cell-size space effects on a reaction-diffusion wave for cell center determination revealed by artificial cell experiments using defined factors
Kei Fujiwara (Keio Univ.)

The coupling of a chemical reaction and molecular diffusion generates nonlinear waves that shape macroscopic patterns. Recent studies have shown that these reaction-diffusion waves are associated with spatiotemporal regulation in living cells. However, the wavelength of these waves is comparable to the size of the space size of the cell, indicating that their behaviors should be significantly different from those observed in the bulk. To elucidate this point, we focused on a reaction-diffusion wave for cell division plane determination (Min wave) and analyzed how cell-size space alters their characteristics by an experimental system using artificial cells and defined factors. In this presentation, we will introduce cell-size space effects on the Min wave, including its scaling properties and the selection mechanism to select traveling and standing waves.

[1] S. Kohyama, et al., eLife, 2019, 8, e44591
[2] A. Yoshida, et al., Chem Sci, 2019, 10, 11064-11072
[3] S. Takada, et al., Sci. Adv, 2022, 8, eabm8460
[4] S. Takada, et al., ACS Nano, 2022, 16, 16853–16861

Disruption of metabolic homeostasis: Responsiveness due to the cofactor dynamics and network sparsity
Yusuke Himeoka (Univ. of Tokyo)

Homeostasis, a cornerstone of living systems, is vital for sustaining their life under the fluctuating environments. In this study, we probe how the homeostasis of bacterial metabolism can be disrupted by utilizing the kinetic models of Escherichia coli central carbon metabolism. We apply perturbations to metabolite concentrations, deviating from steady-state values, to explore the metabolic response and ensuing disturbance of homeostasis. We find that three distinct kinetic models commonly exhibit strong responses to perturbations, where the initial small deviation of metabolite concentrations from steady-state values intensifies over time, culminating in considerable deviation from the steady-state value. Through machine learning techniques and numerical analysis, we unveil the critical roles of cofactors, such as ATP, ADP, and AMP, in this disruption. Additionally, we examine the impact of network structure on metabolic dynamics, revealing that as the metabolic network densifies, the response to perturbations weakens. To further substantiate the roles of cofactors and network structures, we develop a minimal model of metabolic reactions, affirming the importance of these factors. By elucidating the mathematical principles central to metabolic homeostasis, our findings carry broad implications for bacterial physiology, innovative pharmaceutical interventions targeting bacterial vulnerability, and the design principles for robust artificial metabolism in synthetic biology and bioengineering.

Image-based parameter inference for epithelial mechanics
Kaoru Sugimura¹, Xin Yan², Goshi Ogita³, Shuji Ishihara⁴
(¹Graduate School of Science, The Univ. of Tokyo, ²Graduate School of Frontier Sciences, The Univ. of Tokyo, ³RIKEN Center for Biosystems Dynamics Research, ⁴Graduate School of Arts and Sciences, The Univ. of Tokyo)

Measuring mechanical parameters in tissues, such as the elastic modulus of cell-cell junctions, is essential to decipher the mechanical control of morphogenesis. However, their in vivo measurement is technically challenging. Here, we formulated an image-based statistical approach to estimate the mechanical parameters of epithelial cells. Candidate mechanical models are constructed based on force-cell shape correlations obtained from image data. Substitution of the model functions into force-balance equations at the cell vertex leads to an equation with respect to the parameters of the model, by which one can estimate the parameter values using a least-squares method. A test using synthetic data confirmed the accuracy of parameter estimation and model selection. By applying this method to Drosophila epithelial tissues, we found that the magnitude and orientation of feedback between the junction tension and shrinkage, which are determined by the spring constant of the junction, were correlated with the elevation of tension and myosin-II on shrinking junctions during cell rearrangement. Further, this method clarified how alterations in tissue polarity and stretching affect the anisotropy in tension parameters. Thus, our method provides a novel approach to uncovering the mechanisms governing epithelial morphogenesis. In this symposium, we will present an extension of the method in terms of statistics and physics.

Reference:
Goshi Ogita, Takefumi Kondo, Keisuke Ikawa, Tadashi Uemura, Shuji Ishihara, and Kaoru Sugimura. PLoS Computational Biology 18: e1010209 (2022).

A link between viscoelastic mechanics and biochemical function of proteins
Tsvi Tlusty (Ulsan National Institute of Science and Technology)

Our starting point is the idea that specific regions in the protein evolve to become flexible viscoelastic elements facilitating conformational changes associated with function, especially allostery. Simple theories show how these regions can emerge through evolution and indicate that they are easily identified by amino acid rearrangement upon binding (i.e., shear motion). Surprisingly, AlphaFold can also identify such regions by computing the shear induced by a single or a few mutations. With these methods, we have tested the concept of shear and its functional relevance in various proteins. I will present recent results from an experimental study of the enzyme guanylate kinase linking shear, large-scale motions, and catalytic function. Looking at proteins as evolving viscoelastic machines is proposed as a predictive approach to understanding the basic principles of existing proteins and designing new ones. 

Nine-banded armadillo transcriptome analysis reveals the persistent identity signatures derived from stochastic variability
Risa Kawaguchi (Kyoto Univ.)

While a genetic variation can be a primary factor to drive the phenotypic variability, genetically identical individuals still have a potential to obtain distinctively different phenotypic traits. This is because of rare somatic mutations, environmental fluctuation, or stochastic variability determined at an early developmental stage. Although investigating the non-deterministic variation is required to understand the mechanism of non-(or partially) genetic diseases, it has been difficult to elucidate the degrees of relative influence on the individual variation confounding environmental factors. In this study, we carried out time-course transcriptome analysis on nine-banded armadillo (Dasypus novemcinctus) litters, which are always genetically identical quadruplets. By comparing the transcriptome profiles within the siblings obtained over the years, we detected the stochastic variation of allele-specific expression levels of heterozygous SNPs. Our analysis revealed a significant enrichment of skewed genes toward one chromosome, and their skewed patterns tend to be persistent during the period as the variation increases. The skewed gene expression might be linked to the diseases that show differential disease penetrance. Thus, we proposed a model for the haploinsufficiency caused by the stochastic allelic imbalances, where small stochastic fluctuations at early development may trigger remarkable phenotypic differences such as disease penetrance in the future.

A model for branching morphogenesis in inflating tissues
Ignacio Bordeu (Universidad de Chile)

The mechanisms that regulate the patterning of branched epithelia remain a subject of long-standing debate. Recently, it has been proposed that the statistical organization of multiple ductal tissues can be explained through a local self-organizing principle based on the branching-annihilating random walk (BARW) in which proliferating tips drive a process of ductal elongation and stochastic bifurcation that terminates when tips encounter maturing ducts. In this talk, I will show that applied to mouse salivary gland, the BARW model struggles to explain the large-scale organization of tissue. Instead, we propose that the gland develops as a tip-driven branching-delayed random walk (BDRW). In this framework, a generalization of the BARW, tips inhibited through steric interaction with proximate ducts may continue their branching program as constraints become alleviated through the persistent expansion of the surrounding tissue. This inflationary BDRW model presents a general paradigm for branching morphogenesis when the ductal epithelium grows cooperatively with the domain into which it expands. If time allows, I will discuss how such branching paradigm applies to other biological and physical contexts.

Applicability of method for inferring effective mechanical potential of cell–cell interactions in
multicellular systems
Hiroshi Koyama (National Institute for Basic Biology, Japan)

Mechanical forces of cell–cell interactions are critical for morphogenetic events in multicellular systems. However, quantitative information of the mechanical properties of cell-cell interactions is lacking due to technical limitations, especially under 3-dimensional situations. We have been developing an image-based method for inferring effective mechanical forces of cell–cell interactions. We assumed a particle cell model, and pairwise forces of cell–cell interactions were inferred by fitting the model to observation data of 3-dimensional cell positions including their temporal evolution. We successfully detected effective pairwise potential of cell-cell interactions in blastomeres of mouse and C. elegans embryos, and showed that the profiles are linked to morphologic features of the embryos. To evaluate the applicability of our method to other cell types and tissues, we prepared various synthetic data: simulated epithelial cells which are highly deformable compared to blastomeres, and simulated tissues which harbor external factors such as liquid cavities, ECM, etc. Then, through application of our inference method to the synthetic data, we found that mechanical forces in epithelial cells such as cell-cell junction tension were quantitatively reflected into the inferred effective pairwise potentials. Moreover, some external factors were also quantitatively incorporated into. Finally, we applied our method to real epithelial cells with external factors, and evaluated the relationship between the inferred pairwise potentials and tissue morphologies.

Dynamics of contracting actomyosin networks with turnover
Kinneret Keren (Israel Institute of Technology)

Contracting networks of semi-flexible actin filaments and myosin molecular motors have essential roles in many processes in living cells including cell movement and division. To fulfill these functions, the networks must undergo continuous reorganization facilitated by network assembly and disassembly. Despite extensive research, the contractile network behavior in the presence of turnover is still not well understood. To address this issue, we rely on a reconstituted system based on cell extracts encapsulated into water-in-oil droplets. Thanks to the presence of rapid network turnover, our system exhibits contractile flows that persist for hours and self-organize into a wide array of spatiotemporal patterns. Interestingly, we observe a size-dependent transition in the behavior of the system, going from continuous contraction in smaller droplets to periodic contraction in the form of waves and spirals in larger droplets. This transition occurs at a characteristic length scale that is inversely dependent on the network contraction rate. These dynamics are recapitulated by a theoretical model, which considers the coexistence of different local density-dependent mechanical states with distinct rheological properties. The model shows how large-scale contractile behaviors emerge from the interplay between network percolation essential for long-range force transmission and rearrangements due to advection and turnover.

Expansion of keratocyte sheet maintaining its semicircular pattern
Chika Okimura (Yamaguchi Univ.)

Wound repair is a complex multistep process. Several weeks after injury, re-epithelialization is completed by the collective migration of epidermal cells. Even though major steps and principles of skin wound repair are conserved among mammals and fish, re-epithelialization rate of fish skin wounds is more than 50 times faster than that of human skin. When a fish scale is detached and adhered to a substrate, epithelial keratocyte sheets crawl out from it, building a semi-circular pattern. The sheets gradually enlarge while maintaining its pattern as it crawls out from the scale. It has been a mystery how they could expand while maintaining their semicircular pattern. All the keratocytes at the leading edge of the sheet are interconnected with each other via actomyosin cables. They advance as crawling “leader” cells by forward lamellipodial extension with subsequent follower cells. The leading edge of the sheet becomes gradually longer as it crawls out from the scale, regardless of the cell-to-cell connections. Here we show that (1) a mechanical interaction between two leader cells and a single follower cell can forcibly break the connection between the two leader cells and transform the follower cell into a leader cell, and that (2) the position at which this event occurs is determined by the tension of the actomyosin cables.

Using optogenetics to understand cell surface mechanics
Kei Yamamoto (Kyoto Univ.)

The actin cytoskeleton, ubiquitously expressed in animal cells, generates contractile force for cell migration, cytokinesis, and polarity establishment. Among them, cytokinesis is the final step to mechanically divide a cell into daughter cells. During this process, a contractile ring transiently forms and generates tension to divide a cell. The ring tension is counteracted by the cortical tension; however, the importance of cortical tension is still unclear, because it is difficult to estimate the strength of cortical tension relative to the ring tension. A new method that enables precise control of actomyosin contractility in spatial and temporal manner is required. To this end, we developed a new optogenetic tool, named OptoMYPT, enabling the relaxation of contractile force by light. Using OptoMYPT, we found that the relaxation of cortical tension accelerated the furrow ingression. Based on the experimental data and coarse-grained model, we estimated the cortical tension corresponds to at least 14~31% of the ring tension. This balance may achieve both morphological maintenance and timely cytokinesis. In this presentation, we mainly report on such development and application of the OptoMYPT system.

Cellular gradient flow structure connects single-cell-level rules and population-level dynamics
Shuhei A Horiguchi and Tetsuya J Kobayashi (Univ. of Tokyo)

In multicellular systems, populations of cells of various types are organized dynamically to achieve biological functions. For example, in embryonic development, the number of differentiated cells is precisely regulated according to the developmental stage. The immune system adaptively changes the number of immune cells in response to the invading pathogen. Since the population dynamics are realized by the reactions of the constituent cells, e.g., proliferation, death, and differentiation, the behavior of individual cells should be coordinated consistently with the functionality at the population level. For instance, cellular differentiation is often considered to be a unidirectional process following an epigenetic landscape. However, few studies are concerned with epigenetic landscapes in light of population-level behavior and function.

In this study, we show that interrelationships between the single-cell level and the population level emerge naturally from the generalized gradient flow structure of the cell population. We model the desirable cell number distribution by the landscape of a biological utility function. We derive the population dynamics that maximize the utility given the biological costs of single-cell reactions, which is a gradient flow of the utility function. We demonstrate that the standard model of T-cell population dynamics in the acute immune response can be understood as a gradient flow. Furthermore, we show that the single-cell-level landscape emerges from the population-level utility landscape, from which the unidirectional differentiation is derived. The results provide a theoretical basis for linking single-cell-level behaviors to population-level dynamics and functions in multicellular systems. 

Microeconomics of Metabolism: A Linear Response Theory of Evolved Metabolic Systems
Jumpei Yamagishi (Univ. Tokyo)

Many previous studies have attempted to predict the metabolic states of cells assuming metabolic regulation is optimized through (sometimes artificial) evolution for some objective, e.g., growth rate or production of some metabolites. Conventional approaches, however, require identifying the microscopic details of individual metabolic reactions and the objective functions of cells, and their predictions sensitively depend on such details. In this study, we focus on the responses of metabolic systems to environmental perturbations, rather than their metabolic states themselves, and theoretically demonstrate a universal property of the responses independent of the systems’ details. With the help of a microeconomic theory, we show a universal linear relationship between intracellular metabolic responses against nutrient conditions and metabolic inhibition due to manipulation such as drug administration. This quantitative relationship should hold in arbitrary metabolic systems as long as the law of mass conservation holds and cells are optimized for some objectives, but the true objective functions need not be known. Through numerical calculations using large-scale metabolic networks such as the E. coli core model, we confirmed that the relationship is valid from abstract to detailed models. It thus offers quantitative predictions without prior knowledge of systems

Morphological transitions of lipid vesicles driven by the contraction of cortical actomyosin networks
Makito Miyazaki (Kyoto Univ.)

The actomyosin networks beneath the cell membrane, generally called the actin cortex, regulate various biological functions through inducing morphological transitions including membrane blebbing. Recent studies revealed that the membrane blebbing drives amoeboid migration of animal cells, and thus the dynamics of actin cortex has attracted broad attention in the field of mechanobiology. However, the molecular mechanism of membrane deformation regulated by the actin cortex remains unclear. To elucidate the mechanism, we encapsulated purified cytoskeletal proteins into cell-sized lipid vesicles to reconstitute the actin cortex beneath the lipid membrane, and sought conditions in which actomyosin networks induce the vesicle deformation. This bottom-up approach identified the key parameters regulating the probability and magnitude of the membrane deformation. Time-lapse observation clarified that the membrane blebbing was induced by either membrane detachment from the actin cortex, or rupture of the actin cortex, and the actin-membrane interaction determined which case was dominant. These findings will bring us general insights into the physical mechanism of the morphological transitions of living cells.

Signal generation by an excitable system for cell migration
Satomi Matsuoka and Masahiro Ueda (Osaka Univ.)

Cells move spontaneously under homogeneous environments by generating signals that direct the moving direction. Self-organization process of the spontaneous signal generation is mechanistically underpinned by an excitability. An excitable system has been intensively investigated in a model organism, Dictyostelium discoideum, to identify a small GTPase, Ras, as its core. A membrane domain where an active form of Ras is enriched propagates as traveling waves on the cell membrane, which excitability is essential for the cellular spontaneous motility. However, the molecular mechanism has not been well understood. Our recent studies have focused on this issue. Through live-cell imaging and statistical analysis of the spatiotemporal dynamics, RasGEFs and RasGAPs responsible for the excitation and its regulation were uncovered. In addition, minor membrane lipids such as sphingomyelin were revealed to be an essential regulator of the excitability, through super-resolution microscopy combined with a pharmacological blockade of the metabolism. Numerical simulation of the dynamics with a reaction-diffusion model proposed that similar mechanism as a stochastic resonance exerts for a robust emergence of the traveling wave. Based on these results, the roles of molecular noises generated in the presence of the metabolism of the membrane lipids will be discussed to ensure stochastic fluctuations of sufficient magnitude for spontaneous Ras excitation and cell motility.

Generating active T1 transitions through mechanochemical feedback
Silke Henkes (Leiden Univ.)

Convergence-extension in embryos is controlled by chemical and mechanical signalling. A key cellular process is the exchange of neighbours via T1 transitions. We propose and analyse an active vertex model with positive feedback between recruitment of myosin motors and mechanical tension in cell junctions. The model produces active T1 events, which act to elongate the tissue perpendicular to the main direction of tissue stress. Using an idealised tissue patch comprising several active cells embedded in a matrix of passive hexagonal cells, we identified an optimal range of mechanical stresses to trigger an active T1 event. We show that directed stresses also generate tension chains in a realistic patch made entirely of active cells of random shapes and leads to convergence-extension over a range of parameters. Our findings show that active intercalations can generate stress that activates T1 events in neighbouring cells, resulting in tension-dependent tissue reorganisation, in qualitative agreement with experiments on gastrulation in chick embryos.

Chromatin compaction measured by single-cell imaging predicts epigenetic memory
Taihei Fujimori (Stanford Univ.)

Epigenetic memory, mitotically stable changes in gene expression mediated by chromatin modifications, is ubiquitous across many dynamical biological systems and used to remember past signals such as differentiation, infection, or metabolic stress. Repressive chromatin modifications are thought to compact chromatin to silence gene expression. However, the dynamics of chromatin state remains unknown, especially whether chromatin compaction remains after epigenetic memory establishment and whether compaction is correlated with gene silencing or epigenetic memory. Here, we used multiplexed DNA FISH to measure 3D chromatin structure changes after direct recruitment and release of chromatin regulators to a reporter gene. KRAB recruitment, known to cause epigenetic memory, leads to chromatin compaction across tens of kilobases that is retained in stably silenced cells even after KRAB release. Silencing by histone deacetylase HDAC4 does not lead to epigenetic memory nor large-scale compaction, suggesting transcriptional silencing is not sufficient to induce chromatin compaction at the tens of kilobases scale. Compaction arises at the average level, but chromatin structure is heterogeneous in single cells, with open and compacted conformations present in both active and silent cells. By varying the duration of KRAB recruitment and using KRAB mutants with partial loss of function, we generate cell populations with different percentages of stably silenced cells (i.e. epigenetic memory), and find that chromatin compaction upon recruitment quantitatively predicts the epigenetic memory weeks later. Finally, this quantitative connection also holds in a natural gene regulatory context: chromatin compaction at the Nanog locus predicts mouse ES cell fate commitment. These findings suggest that chromatin compaction upon transient epigenetic silencing is predictive of future gene expression.

Self-organization of primitive metabolic cycles and shape-shifting complexes due to non-reciprocal interactions
Ramin Golestanian (Max Planck Institute & Oxford Univ.)

One of the greatest mysteries concerning the origin of life is how it has emerged so quickly after the formation of the earth. In particular, it is not understood how the intricate structures of metabolic cycles, which power the non-equilibrium activity of cells and support their functions under homeostatic conditions, have come into existence in the first instances. These structures have emerged from a dilute primordial soup of chemicals that have turned out to be suitable partners in certain reactions in the roles of reactants and catalysts. While it is generally expected that non-equilibrium conditions would have been necessary for the formation of these primitive metabolic structures, the focus has so far been on externally imposed non-equilibrium conditions, such as temperature or proton gradients. I introduce an alternative paradigm in which naturally occurring non-reciprocal interactions between catalysts that can potentially partner together in a cyclic reaction lead to their rapid recruitment into self-organized functional structures [1]. Within this paradigm, we uncover different classes of self-organized cycles that form through exponentially rapid coarsening processes, depending on the parity of the cycle and the nature of the interaction motifs, which are all generic but have readily tuneable features. Our results also shed light on possibilities that may be explored in designing efficient synthetic cycles. Moreover, we identify programmable non-reciprocal interactions as a tool to achieve the ability to employ a common set of building blocks that can self-organize into a multitude of different structures [2]. The design rule is composed of reciprocal interactions that lead to the equilibrium assembly of the different structures, through a process denoted as multifarious self-assembly, and non-reciprocal interactions that give rise to non-equilibrium dynamical transitions between the structures. The design of such self-organized shape-shifting structures can be implemented at different scales, from nucleic acids and peptides to proteins and colloids.

[1] Vincent Ouazan-Reboul, Jaime Agudo-Canalejo, and Ramin Golestanian, Nature Communications (2023)
[2] Saeed Osat and Ramin Golestanian, Nature Nanotechnology 18, 79–85 (2023)

Homeorhesis, Irreversible differentiation, and Evolution-Development Congruence
Kunihiko Kaneko (Niels Bohr Institute)

Recent studies in universal biology have elucidated dimensional reduction of phenotypes and evolutionary fluctuation-response relationship, by noting consistency between the dynamics at cellular and molecular levels and their robustness. To extend the studies to a multicellular level, the following questions need to be answered:  How does irreversible differentiation to robust cell types progress? How is homeorhesis, i.e., the robustness in the developmental paths achieved? Is the concept of dimensional reduction relevant to multicellular dynamics, so that developmental process be controlled by few variables? All together, how is Waddington’s epigeneic landscape shaped?

We address these questions by focusing on the coupled dynamical systems with inter-intra cellular dynamics and the time-scale interference between slow-fast processes. By considering the interplay among intra-cellular gene expression dynamics, cell-cell interaction, and epigenetic modification, we demonstrate how robust differentiation process (homeorhesis) is generally shaped, as a result of an interplay between fast oscillatory gene expression and slower processes, where the latter works as a controller for the former.  As a consequence of such low-dimensional control, evolution-development congruence is also suggested, whereas  dynamical-systems theory for cell reprogramming is also discussed.

References:
1. Furusawa, C., & Kaneko, K. (2012). A dynamical-systems view of stem cell biology. Science, 338(6104), 215-217.
2. Miyamoto, T., Furusawa, C., & Kaneko, K. (2015). Pluripotency, differentiation, and reprogramming: a gene expression dynamics model with epigenetic feedback regulation. PLoS computational biology, 11(8), e1004476.
3. Matsushita, Y., & Kaneko, K. (2020). Homeorhesis in Waddington’s landscape by epigenetic feedback regulation. Physical Review Research, 2(2), 023083.
4. Matsushita, Y., Hatakeyama, T. S., & Kaneko, K. (2022). Dynamical systems theory of cellular reprogramming. Physical Review Research, 4(2), L022008.
5. Kohsokabe, T., & Kaneko, K. (2022). Dynamical systems approach to evolution–development congruence: Revisiting Haeckel’s recapitulation theory. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, 338(1-2), 62-75.

List of Posters

Igors Dubanevics (OIST)
Population Genetics of E. coli in Microchannels: Theory, Experiment, and Numerical Simulation

Ushasi Roy (Indian Institute of Science, Bengaluru, India)
Emergent spatiotemporal multistability and oscillations enabled by different bio-mechanical underpinnings of underlying gene regulatory network motifs

Romualdo Pastor-Satorras (Department of Physics, Politecnic University of Catalonia, Barcelona, Spain)
Scale-free behavioral cascades in schooling fish

Jumpei Yamagishi  (The University of Tokyo)
Microeconomics of Metabolism: A Linear Response Theory of Evolved Metabolic Systems

Macoto Kikuchi (Osaka Univ.)
Genotypic entropy and bias in evolution of gene regulatory networks

Ignacio Bordeu Universidad de Chile
A model for branching morphogenesis in inflating tissues

Toshinori NAMBA (U. of Tokyo)
Tissue deformation quantification and experimental validation of kinematic equations for epithelial morphogenesis

Thoma Itoh (National Institute for Basic Biology / SOKENDAI)
Environmental fluctuation drives the evolution of bow-tie architecture in biology

Akinori Awazu (Graduate School of Integrated Sciences for Life, Hiroshima University)
Formation of Small-World Network Containing Module Networks in Globally and Locally Coupled Map System with Changes in Global Connection with Time Delay Effects

Yuma Fujimoto (SOKENDAI)
Evolutionary stability of cooperation in indirect reciprocity under noisy and private assessment

Sakura Takada (Keio Univ.)
Turing pattern constructed in artificial cells

Hiroshi Koyama (Division of Embryology, National Insitute for Basic Biology, Japan)
Applicability of method for inferring effective mechanical potential of cell–cell interactions in multicellular systems

Gen HONDA (Tokyo Univ.)
Slow diffusion and signal amplification on membranes regulated by a phospholipase

Saburo Tsuru (Univ. of Tokyo)
Genetic properties affecting transcriptional plasticity and evolvability in E. coli

Risa Kawaguchi (Kyoto University)
Nine-banded armadillo transcriptome analysis reveals the persistent identity signatures derived from stochastic variability

Shunsuke Yabunaka (Japan Atomic Energy Agency)
Universal direction in thermoosmosis of a near-critical binary fluid mixture

Satoshi Kuwana (The University of Tokyo College of Arts and Sciences Department of Basic Science)
Multicellular dynamics and molecular bases underlying single cell layer morphogenesis during Dictyostelium culmination

Tatsuya Fukuyama (Kyushu University)
Continuum mechanical model for collective cell migration under propagating signal wave

Kenji Okubo (SOKENDAI)
Evolution of recombination dominant mode and convexity of the fitness landscape

Masahito Uwamichi (Dept. of Basic Sci., The Univ. of Tokyo)
Deep learning-based data-driven estimation of 2-body interactions in collective cell migration

Takao K. Suzuki (UTokyo)
Macro-evolutionary dynamics of multi-component systems in microbial phenotypes

Kei Nishida (Department of Physics, Graduate School of Science, The University of Tokyo)
Emergence of Complex Host-Parasite networks of replicating RNA molecules: A Computational Analysis

Masaya Fukui (Hiroshima University)
A numerical analysis of a cytoskeletal model for self-organization of actin assembly

Xin YAN (Graduate School of Frontier Sciences, The University of Tokyo)
Extension of image-based parameter inference for epithelial mechanics by using Bayesian method

Anusuya Pal (The University of Tokyo)
Understanding the effects of motility and micro-environment in Drying Droplets of Chlamydomonas reinhardtii and pattern recognition using Feature-based Machine Learning Algorithm

Silke Henkes (Leiden University)
Generating active T1 transitions through mechanochemical feedback

Kohtoh Yukawa (University of Tokyo, Graduate school of Arts and Science)
The influence of environmental conditions on diversity and extinction of early replicator.

Shunsuke Ichii (the University of Tokyo)
Structure of catalytic reaction network for specialized compartments evolution

Simon K. Schnyder (The University of Tokyo)
Rational social distancing policy during epidemics with limited healthcare capacity

Seiya Nishikawa (The Univ. of Tokyo)
Investigation of 3D morphogenesis using thermodynamic and continuum modeling approaches

Tomoei Takahashi (Institute for Physics of Intelligence)
Estimation of the chemical potential of water in lattice protein design via maximizing the marginal likelihood

Ayumi Ozawa (University of Tokyo)
The Kuramoto model with stochastic phase resetting as a model of oscillators with turnover

Yuki Kanai (University of Tokyo)
Endosymbiotic Genome Reduction and the Evolvability of Hosts

Tetsuhiro Hatakeyama (University of Tokyo)
Active thermodynamic force driven mitochondrial alignment