生长细胞聚集体中的图形多面体分裂

Urban Železnik, Matej Krajnc, Tanmoy Sarkar
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引用次数: 0

摘要

在最近提出的图顶点模型(GVM)中,细胞重排是以知识图表示的细胞聚集体的局部图变换来实现的[1]。本研究对 GVM 进行了扩展,将细胞分裂这一涉及形态发生、平衡和疾病进展的关键生物过程纳入其中。与细胞重排一样,GVM 中的细胞分裂也是通过在完整知识图谱中匹配合适的图谱模式或模板,首先识别出参与分裂的节点和链接子图谱。重要的是,当这种变换应用于二维平铺中的多边形时,它能执行分割多边形所需的变换,这表明三维图变换是通用的,也适用于二维顶点模型。
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Graph polyhedral divisions in growing cell aggregates
In the recently proposed Graph Vertex Model (GVM), cellular rearrangements are implemented as local graph transformations of the cell aggregate, represented by a knowledge graph [1]. This study extends GVM to incorporate cell division, a critical biological process involved in morphogenesis, homeostasis, and disease progression. Like cellular rearrangements, cell division in GVM begins by identifying a subgraph of nodes and links, involved in the division, by matching suitable graph patterns or templates within the full knowledge graph. The matched subgraph is then transformed to incorporate topological changes within the knowledge graph, caused by the division event. Importantly, when this transformation is applied to a polygon in a 2D tiling, it performs the transformation, required to divide a polygon, indicating that the 3D graph transformation is general and applicable also to 2D vertex models. Our extension of GVM enables the study of the dynamics of growing cell aggregates in 3D to offer new insights into developmental processes and cancer growth.
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