Design Automation for Biological Models: A Pipeline that Incorporates Spatial and Molecular Complexity

Abstract

Understanding the dynamics of biochemical networks is a major goal of systems biology. Due to the heterogeneity of cells and the low copy numbers of key molecules, spatially resolved approaches are required to fully understand and model these systems. Until recently, most spatial modeling was performed using geometries obtained either through manual segmentation or manual fabrication both of which are time-consuming and tedious. Similarly, the system of reactions associated with the model had to be manually defined, a process that is both tedious and error-prone for large networks. As a result, spatially resolved simulations have typically only been performed in a limited number of geometries, which are often highly simplified, and with small reaction networks.