Cell Division and Motility Enable Hexatic Order in Biological Tissues

Tang, Yiwen; Chen, Siyuan; Bowick, Mark J.; Bi, Dapeng

doi:10.1103/physrevlett.132.218402

Cited by 3 publications

References 72 publications

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Capture behavior of self-propelled particles into a hexatic ordering obstacle

Li 李,

Shi 石,

Wang 王

et al. 2024

Chinese Phys. B

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Computer simulations are utilized to investigate the dynamic behavior of self-propelled particles (SPPs) within a complex obstacle environment. The findings reveal that SPPs exhibit three distinct aggregation states within the obstacle, each contingent on specific conditions. A phase diagram outlining the aggregation states concerning self-propulsion conditions is presented. The results illustrate a transition of SPPs from a dispersion state to a transition state as persistence time increases within the obstacle. Conversely, as the driving strength increases, self-propelled particles shift towards a cluster state. A systematic exploration of the interplay between driving strength, persistence time, and matching degree on the dynamic behavior of self-propelled particles is conducted. Furthermore, an analysis is performed on the spatial distribution of SPPs along the y-axis, capture rate, maximum capture probability, and mean-square displacement. The insights gained from this research serve as valuable contributions to understanding the capture and collection of active particles.

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Capture behavior of self-propelled particles into a hexatic ordering obstacle

Li 李,

Shi 石,

Wang 王

et al. 2024

Chinese Phys. B

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Topological Excitations govern Ordering Kinetics in Endothelial Cell Layers

Ruider,

Thijssen,

Vannier

et al. 2024

Preprint

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Many physiological processes, such as the shear flow alignment of endothelial cells in the vasculature, depend on the transition of cell layers between disordered and ordered phases. Here, we demonstrate that such a transition is driven by the non-monotonic evolution of nematic topological defects and the emergence of topological strings that bind the defects together, unveiling an intermediate phase of ordering kinetics in biological matter. We used time-resolved large-scale imaging and physical modeling to resolve the nature of the non-monotonic decrease in the number of defect pairs. The interaction of the intrinsic cell layer activity and the alignment field determines the occurrence of defect domains, which defines the nature of the transition. Defect pair annihilation is mediated by topological strings spanning multicellular scales within the cell layer. We propose that these long-range interactions in the intermediate ordering phase have significant implications for a wide range of biological phenomena in morphogenesis, tissue remodeling, and disease progression.

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Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks

Yang,

Meyer,

Huang

et al. 2024

PRX Life

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Multicellular self-assembly into functional structures is a dynamic process that is critical in the development of biological structures and diseases, including embryo development, organ formation, tumor invasion, and other processes. Being able to infer collective cell migratory dynamics from their static configuration is valuable for both understanding and predicting these complex behaviors. However, the identification of structural features that can indicate multicellular motion has been difficult, and existing metrics largely rely on physical instincts. Here we show that, through the use of a graph neural network, the motion of multicellular collectives can be inferred from a static snapshot of cell positions, in both experimental and synthetic datasets. Published by the American Physical Society 2024

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Cell Division and Motility Enable Hexatic Order in Biological Tissues

Cited by 3 publications

References 72 publications

Capture behavior of self-propelled particles into a hexatic ordering obstacle

Capture behavior of self-propelled particles into a hexatic ordering obstacle

Topological Excitations govern Ordering Kinetics in Endothelial Cell Layers

Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks

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