Observations of glassy dynamics in experiments on confluent cellular tissue have inspired a wealth of computational and theoretical research to model their emergent collective behavior. Initial studies of the physical properties of several geometric cell models, including vertex-type models, have highlighted anomalous sub-Arrhenius, or "ultra-strong," scaling of the dynamics with temperature. Here we show that the dynamics and material properties of the 2d Voronoi model deviate even further from the standard glassforming paradigm. By varying the characteristic shape index $p_0$, we demonstrate that the system properties can be tuned between displaying expected glassforming behavior, including the breakdown of the Stokes-Einstein-Sutherland relation and the formation of dynamical heterogeneities, and an unusual regime in which the viscosity does not diverge as the characteristic relaxation time increase and dynamical heterogeneities are strongly suppressed. Our results provide further insight into the fundamental properties of this class of anomalous glassy materials, and provide a step towards designing materials with predetermined glassy dynamics.
{"title":"Tunable glassy dynamics in models of dense cellular tissue","authors":"Helen S. Ansell, Chengling Li, Daniel M. Sussman","doi":"arxiv-2409.00496","DOIUrl":"https://doi.org/arxiv-2409.00496","url":null,"abstract":"Observations of glassy dynamics in experiments on confluent cellular tissue\u0000have inspired a wealth of computational and theoretical research to model their\u0000emergent collective behavior. Initial studies of the physical properties of\u0000several geometric cell models, including vertex-type models, have highlighted\u0000anomalous sub-Arrhenius, or \"ultra-strong,\" scaling of the dynamics with\u0000temperature. Here we show that the dynamics and material properties of the 2d\u0000Voronoi model deviate even further from the standard glassforming paradigm. By\u0000varying the characteristic shape index $p_0$, we demonstrate that the system\u0000properties can be tuned between displaying expected glassforming behavior,\u0000including the breakdown of the Stokes-Einstein-Sutherland relation and the\u0000formation of dynamical heterogeneities, and an unusual regime in which the\u0000viscosity does not diverge as the characteristic relaxation time increase and\u0000dynamical heterogeneities are strongly suppressed. Our results provide further\u0000insight into the fundamental properties of this class of anomalous glassy\u0000materials, and provide a step towards designing materials with predetermined\u0000glassy dynamics.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present Flow Matching for Reaction Coordinates (FMRC), a novel deep learning algorithm designed to identify optimal reaction coordinates (RC) in biomolecular reversible dynamics. FMRC is based on the mathematical principles of lumpability and decomposability, which we reformulate into a conditional probability framework for efficient data-driven optimization using deep generative models. While FMRC does not explicitly learn the well-established transfer operator or its eigenfunctions, it can effectively encode the dynamics of leading eigenfunctions of the system transfer operator into its low-dimensional RC space. We further quantitatively compare its performance with several state-of-the-art algorithms by evaluating the quality of Markov State Models (MSM) constructed in their respective RC spaces, demonstrating the superiority of FMRC in three increasingly complex biomolecular systems. Finally, we discuss its potential applications in downstream applications such as enhanced sampling methods and MSM construction.
{"title":"Flow Matching for Optimal Reaction Coordinates of Biomolecular System","authors":"Mingyuan Zhang, Zhicheng Zhang, Yong Wang, Hao Wu","doi":"arxiv-2408.17139","DOIUrl":"https://doi.org/arxiv-2408.17139","url":null,"abstract":"We present Flow Matching for Reaction Coordinates (FMRC), a novel deep\u0000learning algorithm designed to identify optimal reaction coordinates (RC) in\u0000biomolecular reversible dynamics. FMRC is based on the mathematical principles\u0000of lumpability and decomposability, which we reformulate into a conditional\u0000probability framework for efficient data-driven optimization using deep\u0000generative models. While FMRC does not explicitly learn the well-established\u0000transfer operator or its eigenfunctions, it can effectively encode the dynamics\u0000of leading eigenfunctions of the system transfer operator into its\u0000low-dimensional RC space. We further quantitatively compare its performance\u0000with several state-of-the-art algorithms by evaluating the quality of Markov\u0000State Models (MSM) constructed in their respective RC spaces, demonstrating the\u0000superiority of FMRC in three increasingly complex biomolecular systems.\u0000Finally, we discuss its potential applications in downstream applications such\u0000as enhanced sampling methods and MSM construction.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electrical activity in the heart is affected by the presence of scroll waves, causing lifethreatening arrhythmias. Clinical procedures to handle these electrical disorganizations create nonconductive heterogeneities in the cardiac tissue. We explore how boundary layer heterogeneities affect the scroll wave dynamics in a semidiscrete electrophysiological model. We show that decreasing the coupling strength near the boundaries of the tissue can prevent a meandering instability in the bulk enhancing the stability of scroll waves confined to thin geometries. Based on the coupling strength, the boundary layer length, the slab thickness, and wave deformation, we propose a forced model to reveal how a heterogeneity-induced slowing down of the waves governs the stabilization.
{"title":"Boundary layer heterogeneities can enhance scroll wave stability","authors":"Sebastian Echeverria-Alar, Wouter-Jan Rappel","doi":"arxiv-2409.00183","DOIUrl":"https://doi.org/arxiv-2409.00183","url":null,"abstract":"The electrical activity in the heart is affected by the presence of scroll\u0000waves, causing lifethreatening arrhythmias. Clinical procedures to handle these\u0000electrical disorganizations create nonconductive heterogeneities in the cardiac\u0000tissue. We explore how boundary layer heterogeneities affect the scroll wave\u0000dynamics in a semidiscrete electrophysiological model. We show that decreasing\u0000the coupling strength near the boundaries of the tissue can prevent a\u0000meandering instability in the bulk enhancing the stability of scroll waves\u0000confined to thin geometries. Based on the coupling strength, the boundary layer\u0000length, the slab thickness, and wave deformation, we propose a forced model to\u0000reveal how a heterogeneity-induced slowing down of the waves governs the\u0000stabilization.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The packaging of genetic material within a protein shell, called the capsid, marks a pivotal step in the life cycle of numerous single-stranded RNA viruses. Understanding how hundreds, or even thousands, of proteins assemble around the genome to form highly symmetrical structures remains an unresolved puzzle. In this paper, we design novel subunits and develop a model that enables us to explore the assembly pathways and genome packaging mechanism of icosahedral viruses, which were previously inaccessible. Using molecular dynamics (MD) simulations, we observe capsid fragments, varying in protein number and morphology, assembling at different locations along the genome. Initially, these fragments create a disordered structure that later merges to form a perfect symmetric capsid. The model demonstrates remarkable strength in addressing numerous unresolved issues surrounding virus assembly. For instance, it enables us to explore the advantages of RNA packaging by capsid proteins over linear polymers. Our MD simulations are in excellent agreement with our experimental findings from small-angle X-ray scattering and cryo-transmission electron microscopy, carefully analyzing the assembly products of viral capsid proteins around RNAs with distinct topologies.
{"title":"Switchable Conformation in Protein Subunits: Unveiling Assembly Dynamics of Icosahedral Viruses","authors":"Siyu Li, Guillaume Tresset, Roya Zandi","doi":"arxiv-2409.00226","DOIUrl":"https://doi.org/arxiv-2409.00226","url":null,"abstract":"The packaging of genetic material within a protein shell, called the capsid,\u0000marks a pivotal step in the life cycle of numerous single-stranded RNA viruses.\u0000Understanding how hundreds, or even thousands, of proteins assemble around the\u0000genome to form highly symmetrical structures remains an unresolved puzzle. In\u0000this paper, we design novel subunits and develop a model that enables us to\u0000explore the assembly pathways and genome packaging mechanism of icosahedral\u0000viruses, which were previously inaccessible. Using molecular dynamics (MD)\u0000simulations, we observe capsid fragments, varying in protein number and\u0000morphology, assembling at different locations along the genome. Initially,\u0000these fragments create a disordered structure that later merges to form a\u0000perfect symmetric capsid. The model demonstrates remarkable strength in\u0000addressing numerous unresolved issues surrounding virus assembly. For instance,\u0000it enables us to explore the advantages of RNA packaging by capsid proteins\u0000over linear polymers. Our MD simulations are in excellent agreement with our\u0000experimental findings from small-angle X-ray scattering and cryo-transmission\u0000electron microscopy, carefully analyzing the assembly products of viral capsid\u0000proteins around RNAs with distinct topologies.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An elastic spring network is an example of evolvable matter. It can be pruned to couple separated pairs of nodes so that when a strain is applied to one of them, the other responds either in-phase or out-of-phase. This produces two pruned networks with incompatible functions that are nearly identical but differ from each other by a set of "mutations," each of which removes or adds a single bond in the network. The effect of multiple mutations is epistatic; that is, the effect of a mutation depends on what other mutations have already occurred. We generate ensembles of network pairs that differ by a fixed number, $M$, of discrete mutations and evaluate all $M!$ mutational paths between the in- and out-of phase behaviors up to $M = 14$. With a threshold response for the network to be considered functional, so that non-functional networks are disallowed, only some mutational pathways are viable. We find that there is a surprisingly high critical response threshold above which no evolutionarily viable path exists between the two networks. The few remaining pathways at this critical value dictate much of the behavior along the evolutionary trajectory. In most cases, the mutations break up into two distinct classes. The analysis clarifies how the number of mutations and the position of a mutation along the pathway affect the evolutionary outcome.
{"title":"Epistatic pathways in evolvable mechanical networks","authors":"Samar Alqatari, Sidney Nagel","doi":"arxiv-2408.16926","DOIUrl":"https://doi.org/arxiv-2408.16926","url":null,"abstract":"An elastic spring network is an example of evolvable matter. It can be pruned\u0000to couple separated pairs of nodes so that when a strain is applied to one of\u0000them, the other responds either in-phase or out-of-phase. This produces two\u0000pruned networks with incompatible functions that are nearly identical but\u0000differ from each other by a set of \"mutations,\" each of which removes or adds a\u0000single bond in the network. The effect of multiple mutations is epistatic; that\u0000is, the effect of a mutation depends on what other mutations have already\u0000occurred. We generate ensembles of network pairs that differ by a fixed number,\u0000$M$, of discrete mutations and evaluate all $M!$ mutational paths between the\u0000in- and out-of phase behaviors up to $M = 14$. With a threshold response for\u0000the network to be considered functional, so that non-functional networks are\u0000disallowed, only some mutational pathways are viable. We find that there is a\u0000surprisingly high critical response threshold above which no evolutionarily\u0000viable path exists between the two networks. The few remaining pathways at this\u0000critical value dictate much of the behavior along the evolutionary trajectory.\u0000In most cases, the mutations break up into two distinct classes. The analysis\u0000clarifies how the number of mutations and the position of a mutation along the\u0000pathway affect the evolutionary outcome.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enrique C. Gabrick, Eduardo L. Brugnago, Ana L. R. de Moraes, Paulo R. Protachevicz, Sidney T. da Silva, Fernando S. Borges, Iberê L. Caldas, Antonio M. Batista, Jürgen Kurths
In this work, effects of constant and time-dependent vaccination rates on the Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) seasonal model are studied. Computing the Lyapunov exponent, we show that typical complex structures, such as shrimps, emerge for given combinations of constant vaccination rate and another model parameter. In some specific cases, the constant vaccination does not act as a chaotic suppressor and chaotic bands can exist for high levels of vaccination (e.g., $> 0.95$). Moreover, we obtain linear and non-linear relationships between one control parameter and constant vaccination to establish a disease-free solution. We also verify that the total infected number does not change whether the dynamics is chaotic or periodic. The introduction of a time-dependent vaccine is made by the inclusion of a periodic function with a defined amplitude and frequency. For this case, we investigate the effects of different amplitudes and frequencies on chaotic attractors, yielding low, medium, and high seasonality degrees of contacts. Depending on the parameters of the time-dependent vaccination function, chaotic structures can be controlled and become periodic structures. For a given set of parameters, these structures are accessed mostly via crisis and in some cases via period-doubling. After that, we investigate how the time-dependent vaccine acts in bi-stable dynamics when chaotic and periodic attractors coexist. We identify that this kind of vaccination acts as a control by destroying almost all the periodic basins. We explain this by the fact that chaotic attractors exhibit more desirable characteristics for epidemics than periodic ones in a bi-stable state.
{"title":"Control, bi-stability and preference for chaos in time-dependent vaccination campaign","authors":"Enrique C. Gabrick, Eduardo L. Brugnago, Ana L. R. de Moraes, Paulo R. Protachevicz, Sidney T. da Silva, Fernando S. Borges, Iberê L. Caldas, Antonio M. Batista, Jürgen Kurths","doi":"arxiv-2409.08293","DOIUrl":"https://doi.org/arxiv-2409.08293","url":null,"abstract":"In this work, effects of constant and time-dependent vaccination rates on the\u0000Susceptible-Exposed-Infected-Recovered-Susceptible (SEIRS) seasonal model are\u0000studied. Computing the Lyapunov exponent, we show that typical complex\u0000structures, such as shrimps, emerge for given combinations of constant\u0000vaccination rate and another model parameter. In some specific cases, the\u0000constant vaccination does not act as a chaotic suppressor and chaotic bands can\u0000exist for high levels of vaccination (e.g., $> 0.95$). Moreover, we obtain\u0000linear and non-linear relationships between one control parameter and constant\u0000vaccination to establish a disease-free solution. We also verify that the total\u0000infected number does not change whether the dynamics is chaotic or periodic.\u0000The introduction of a time-dependent vaccine is made by the inclusion of a\u0000periodic function with a defined amplitude and frequency. For this case, we\u0000investigate the effects of different amplitudes and frequencies on chaotic\u0000attractors, yielding low, medium, and high seasonality degrees of contacts.\u0000Depending on the parameters of the time-dependent vaccination function, chaotic\u0000structures can be controlled and become periodic structures. For a given set of\u0000parameters, these structures are accessed mostly via crisis and in some cases\u0000via period-doubling. After that, we investigate how the time-dependent vaccine\u0000acts in bi-stable dynamics when chaotic and periodic attractors coexist. We\u0000identify that this kind of vaccination acts as a control by destroying almost\u0000all the periodic basins. We explain this by the fact that chaotic attractors\u0000exhibit more desirable characteristics for epidemics than periodic ones in a\u0000bi-stable state.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quirine J. S. Braat, Giulia Janzen, Bas C. Jansen, Vincent E. Debets, Simone Ciarella, Liesbeth M. C. Janssen
Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Previous work has established the importance of the average cell shape in predicting cell dynamics. Here, we aim to identify the importance of individual cell shape features, rather than collective features, to distinguish between high-motility (active) and low-motility (passive) cells in heterogeneous cell layers. Employing the Cellular Potts Model, we generate simulation snapshots and extract static features as inputs for a simple machine-learning model. Our results show that when the passive cells are non-motile, this machine-learning model can accurately predict whether a cell is passive or active using only single-cell shape features. Furthermore, we explore scenarios where passive cells also exhibit some degree of motility, albeit less than active cells. In such cases, our findings indicate that a neural network trained on shape features can accurately classify cell motility, particularly when the number of active cells is low, and the motility of active cells is significantly higher compared to passive cells. This work offers potential for physics-inspired predictions of single-cell properties with implications for inferring cell dynamics from static histological images.
{"title":"Shape matters: Inferring the motility of confluent cells from static images","authors":"Quirine J. S. Braat, Giulia Janzen, Bas C. Jansen, Vincent E. Debets, Simone Ciarella, Liesbeth M. C. Janssen","doi":"arxiv-2408.16368","DOIUrl":"https://doi.org/arxiv-2408.16368","url":null,"abstract":"Cell motility in dense cell collectives is pivotal in various diseases like\u0000cancer metastasis and asthma. A central aspect in these phenomena is the\u0000heterogeneity in cell motility, but identifying the motility of individual\u0000cells is challenging. Previous work has established the importance of the\u0000average cell shape in predicting cell dynamics. Here, we aim to identify the\u0000importance of individual cell shape features, rather than collective features,\u0000to distinguish between high-motility (active) and low-motility (passive) cells\u0000in heterogeneous cell layers. Employing the Cellular Potts Model, we generate\u0000simulation snapshots and extract static features as inputs for a simple\u0000machine-learning model. Our results show that when the passive cells are\u0000non-motile, this machine-learning model can accurately predict whether a cell\u0000is passive or active using only single-cell shape features. Furthermore, we\u0000explore scenarios where passive cells also exhibit some degree of motility,\u0000albeit less than active cells. In such cases, our findings indicate that a\u0000neural network trained on shape features can accurately classify cell motility,\u0000particularly when the number of active cells is low, and the motility of active\u0000cells is significantly higher compared to passive cells. This work offers\u0000potential for physics-inspired predictions of single-cell properties with\u0000implications for inferring cell dynamics from static histological images.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the context of multi-agent systems of binary interacting particles, a kinetic model for action potential dynamics on a neural network is proposed, accounting for heterogeneity in the neuron-to-neuron connections, as well as in the brain structure. Two levels of description are coupled: in a single area, pairwise neuron interactions for the exchange of membrane potential are statistically described; among different areas, a graph description of the brain network topology is included. Equilibria of the kinetic and macroscopic settings are determined and numerical simulations of the system dynamics are performed with the aim of studying the influence of the network heterogeneities on the membrane potential propagation and synchronization.
{"title":"Action potential dynamics on heterogenous neural networks: from kinetic to macroscopic equations","authors":"Marzia Bisi, Martina Conte, Maria Groppi","doi":"arxiv-2408.16214","DOIUrl":"https://doi.org/arxiv-2408.16214","url":null,"abstract":"In the context of multi-agent systems of binary interacting particles, a\u0000kinetic model for action potential dynamics on a neural network is proposed,\u0000accounting for heterogeneity in the neuron-to-neuron connections, as well as in\u0000the brain structure. Two levels of description are coupled: in a single area,\u0000pairwise neuron interactions for the exchange of membrane potential are\u0000statistically described; among different areas, a graph description of the\u0000brain network topology is included. Equilibria of the kinetic and macroscopic\u0000settings are determined and numerical simulations of the system dynamics are\u0000performed with the aim of studying the influence of the network heterogeneities\u0000on the membrane potential propagation and synchronization.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Transfer entropy (TE) is a powerful tool for measuring causal relationships within interaction networks. Traditionally, TE and its conditional variants are applied pairwise between dynamic variables to infer these causal relationships. However, identifying the most influential or vulnerable node in a system requires measuring the causal influence of each component on the entire system and vice versa. In this paper, I propose using outgoing and incoming transfer entropy-where outgoing TE quantifies the influence of a node on the rest of the system, and incoming TE measures the influence of the rest of the system on the node. The node with the highest outgoing TE is identified as the most influential, or "hub", while the node with the highest incoming TE is the most vulnerable, or "anti-hub". Since these measures involve transfer entropy between univariate and multivariate time series, naive estimation methods can result in significant errors, particularly when the number of variables is comparable to or exceeds the number of samples. To address this, I introduce a novel estimation scheme that computes outgoing and incoming TE only between significantly interacting partners. The feasibility of this approach is demonstrated by using synthetic data, and by applying it to a real data of oral microbiota. The method successfully identifies the bacterial species known to be key players in the bacterial community, demonstrating the power of the new method.
{"title":"Identifying Influential and Vulnerable Nodes in Interaction Networks through Estimation of Transfer Entropy Between Univariate and Multivariate Time Series","authors":"Julian Lee","doi":"arxiv-2408.15811","DOIUrl":"https://doi.org/arxiv-2408.15811","url":null,"abstract":"Transfer entropy (TE) is a powerful tool for measuring causal relationships\u0000within interaction networks. Traditionally, TE and its conditional variants are\u0000applied pairwise between dynamic variables to infer these causal relationships.\u0000However, identifying the most influential or vulnerable node in a system\u0000requires measuring the causal influence of each component on the entire system\u0000and vice versa. In this paper, I propose using outgoing and incoming transfer\u0000entropy-where outgoing TE quantifies the influence of a node on the rest of the\u0000system, and incoming TE measures the influence of the rest of the system on the\u0000node. The node with the highest outgoing TE is identified as the most\u0000influential, or \"hub\", while the node with the highest incoming TE is the most\u0000vulnerable, or \"anti-hub\". Since these measures involve transfer entropy\u0000between univariate and multivariate time series, naive estimation methods can\u0000result in significant errors, particularly when the number of variables is\u0000comparable to or exceeds the number of samples. To address this, I introduce a\u0000novel estimation scheme that computes outgoing and incoming TE only between\u0000significantly interacting partners. The feasibility of this approach is\u0000demonstrated by using synthetic data, and by applying it to a real data of oral\u0000microbiota. The method successfully identifies the bacterial species known to\u0000be key players in the bacterial community, demonstrating the power of the new\u0000method.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell layers are often categorized as contractile or extensile active nematics but recent experiments on neural progenitor cells with induced $+1$ topological defects challenge this classification. In a bottom-up approach, we first study a relevant particle-level model and then analyze a continuous theory derived from it. We show that both model and theory account qualitatively for the main experimental result, i.e. accumulation of cells at the core of any type of +1 defect. We argue that cell accumulation is essentially due to two generally ignored 'effective active forces'. We finally discuss the relevance and consequences of our findings in the context of other cellular active nematics experiments and previously proposed theories.
{"title":"Integer Topological Defects Reveal Effective Forces in Active Nematics","authors":"Zihui Zhao, Yisong Yao, He Li, Yongfeng Zhao, Yujia Wang, Hepeng Zhang, Hugues Chat'e, Masaki Sano","doi":"arxiv-2408.15431","DOIUrl":"https://doi.org/arxiv-2408.15431","url":null,"abstract":"Cell layers are often categorized as contractile or extensile active nematics\u0000but recent experiments on neural progenitor cells with induced $+1$ topological\u0000defects challenge this classification. In a bottom-up approach, we first study\u0000a relevant particle-level model and then analyze a continuous theory derived\u0000from it. We show that both model and theory account qualitatively for the main\u0000experimental result, i.e. accumulation of cells at the core of any type of +1\u0000defect. We argue that cell accumulation is essentially due to two generally\u0000ignored 'effective active forces'. We finally discuss the relevance and consequences of our findings in the\u0000context of other cellular active nematics experiments and previously proposed\u0000theories.","PeriodicalId":501040,"journal":{"name":"arXiv - PHYS - Biological Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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