Center for Computational and Theoretical Biology, University of Würzburg, Campus Hubland Nord 32, Würzburg, Germany.
PLoS Comput Biol. 2020 Nov 30;16(11):e1008003. doi: 10.1371/journal.pcbi.1008003. eCollection 2020 Nov.
Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function.
空间生物网络存在于生命的各个尺度上，从单细胞到生态系统，它们执行着各种重要的功能，包括信号传输和营养物质运输。这些生物功能依赖于网络的结构，而网络结构则是动态、反馈驱动的发育过程的结果。虽然细胞在生长过程中的行为可以通过遗传编码，但最终的网络结构取决于空间限制和组织架构。由于网络生长通常难以在实验中观察到，因此计算机模拟可以帮助我们理解局部细胞行为如何决定最终的网络结构。我们在这里提出了一个基于方向统计学的计算框架，用于在任意空间限制下对时空网络形成进行建模。生长被描述为一个有偏的相关随机游走，其中方向和分支取决于局部环境条件和限制，这些条件和限制以 3D 多层网格的形式呈现。为了演示我们工具的应用，我们对细胞之间的密集网络进行了生长模拟，并将结果与骨组织中骨细胞网络的实验数据进行了比较。我们的通用框架可以帮助更好地理解网络模式如何依赖于空间限制，或者识别偏离健康网络功能的生物学原因。
