Mostov Raphael, Lewis Greyson, Sturm Gabriel, Marshall Wallace F
UCSF.
University of California, San Francisco (UCSF).
bioRxiv. 2025 Mar 28:2025.03.17.643721. doi: 10.1101/2025.03.17.643721.
This paper addresses the increasing need for comprehensive mathematical descriptions of cell organization by examining the algebraic structure of mitochondrial network dynamics. Mitochondria are cellular structures involved in metabolism that take the form of a network of membrane-based tubes that undergo continuous re-arrangement by a set of morphological processes, including fission and fusion, carried out by protein-based machinery. Because of their network structure, mitochondria can be represented as graphs, and the morphological operations that take place in the cell, referred to as mitochondrial dynamics, can be represented by changes to the graphs. Prior studies have classified mitochondrial graphs based on graph-theoretic features, but an alternative approach is to focus not on the graphs themselves but on the set of morphological operations inducing mitochondrial dynamics, since this may provide a simpler representation. Moreover, the operations are what determine the graphs that will be generated in a biological system. Here we show that mitochondrial dynamics on a single connected mitochondrion constitute a groupoid that includes the automorphism group of each mitochondria graph. For multi-component mitochondria we define a graph structure that encapsulates the structure of mitochondrial dynamics. Using these formalisms we define a distance metric for similarity between mitochondrial structures based on an edit distance. In the course of defining these structures we provide a mathematical motivation for new experimental questions regarding mitochondrial fusion and the impacts of cell division on mitochondrial morphology. This work points to a general strategy for formulating a cell structure state-space, based not on the shapes of cellular structures, but on relations between the dynamic operations that produce them.
本文通过研究线粒体网络动力学的代数结构,探讨了对细胞组织进行全面数学描述的日益增长的需求。线粒体是参与新陈代谢的细胞结构,呈基于膜的管状网络形式,通过一组由蛋白质机制执行的形态学过程(包括裂变和融合)不断重新排列。由于其网络结构,线粒体可以表示为图,而细胞中发生的形态学操作(称为线粒体动力学)可以通过图的变化来表示。先前的研究已根据图论特征对线粒体图进行分类,但另一种方法是不关注图本身,而是关注诱导线粒体动力学的形态学操作集,因为这可能提供更简单的表示。此外,这些操作决定了生物系统中将会生成的图。在这里,我们表明单个相连线粒体上的线粒体动力学构成一个广群,其中包括每个线粒体图的自同构群。对于多组分线粒体,我们定义了一种图结构,它封装了线粒体动力学的结构。使用这些形式体系,我们基于编辑距离定义了线粒体结构之间相似性的距离度量。在定义这些结构的过程中,我们为有关线粒体融合以及细胞分裂对线粒体形态影响的新实验问题提供了数学依据。这项工作指出了一种制定细胞结构状态空间的一般策略,该策略不是基于细胞结构的形状,而是基于产生这些结构的动态操作之间的关系。