arXiv - PHYS - Biological Physics最新文献_第4页

Tunable glassy dynamics in models of dense cellular tissue 致密细胞组织模型中的可调玻璃状动力学

arXiv - PHYS - Biological Physics Pub Date : 2024-08-31 DOI: arxiv-2409.00496

Helen S. Ansell, Chengling Li, Daniel M. Sussman

{"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":"280 1","pages":""},"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}

引用次数: 0

Flow Matching for Optimal Reaction Coordinates of Biomolecular System 生物分子系统最佳反应坐标的流动匹配

arXiv - PHYS - Biological Physics Pub Date : 2024-08-30 DOI: arxiv-2408.17139

Mingyuan Zhang, Zhicheng Zhang, Yong Wang, Hao Wu

引用次数: 0

Boundary layer heterogeneities can enhance scroll wave stability 边界层异质性可增强涡旋波的稳定性

arXiv - PHYS - Biological Physics Pub Date : 2024-08-30 DOI: arxiv-2409.00183

Sebastian Echeverria-Alar, Wouter-Jan Rappel

引用次数: 0

Switchable Conformation in Protein Subunits: Unveiling Assembly Dynamics of Icosahedral Viruses 蛋白质亚基的可转换构象：揭示二十面体病毒的组装动力学

arXiv - PHYS - Biological Physics Pub Date : 2024-08-30 DOI: arxiv-2409.00226

Siyu Li, Guillaume Tresset, Roya Zandi

{"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":"7 1","pages":""},"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}

引用次数: 0

Epistatic pathways in evolvable mechanical networks 可进化机械网络中的外显路径

arXiv - PHYS - Biological Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16926

Samar Alqatari, Sidney Nagel

{"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":"3 1","pages":""},"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}

引用次数: 0

Control, bi-stability and preference for chaos in time-dependent vaccination campaign 受时间影响的疫苗接种活动中的控制、双稳定性和混乱偏好

arXiv - PHYS - Biological Physics Pub Date : 2024-08-29 DOI: arxiv-2409.08293

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

{"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":"44 1","pages":""},"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}

引用次数: 0

Shape matters: Inferring the motility of confluent cells from static images 形状很重要从静态图像推断汇合细胞的运动性

arXiv - PHYS - Biological Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16368

Quirine J. S. Braat, Giulia Janzen, Bas C. Jansen, Vincent E. Debets, Simone Ciarella, Liesbeth M. C. Janssen

{"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":"59 1","pages":""},"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}

引用次数: 0

Action potential dynamics on heterogenous neural networks: from kinetic to macroscopic equations 异源神经网络的动作电位动力学：从动力学方程到宏观方程

arXiv - PHYS - Biological Physics Pub Date : 2024-08-29 DOI: arxiv-2408.16214

Marzia Bisi, Martina Conte, Maria Groppi

引用次数: 0

Identifying Influential and Vulnerable Nodes in Interaction Networks through Estimation of Transfer Entropy Between Univariate and Multivariate Time Series 通过估算单变量和多变量时间序列之间的转移熵识别交互网络中的影响节点和脆弱节点

arXiv - PHYS - Biological Physics Pub Date : 2024-08-28 DOI: arxiv-2408.15811

Julian Lee

{"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":"5 1","pages":""},"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}

引用次数: 0

Integer Topological Defects Reveal Effective Forces in Active Nematics 整数拓扑缺陷揭示活性向列的有效作用力

arXiv - PHYS - Biological Physics Pub Date : 2024-08-27 DOI: arxiv-2408.15431

Zihui Zhao, Yisong Yao, He Li, Yongfeng Zhao, Yujia Wang, Hepeng Zhang, Hugues Chat'e, Masaki Sano

引用次数: 0