Stochastic simulations are essential to the study of biological cells, yet there is no computational framework allowing for detailed spatial simulations of genetic regulatory network within large populations of cells.
We fill this gap by developing a parallel simulation framework capable of spatially resolved stochastic simulation of cell-cell signaling in multicellular systems, as well as cell mechanics. We use an operator-splitting method to decouple the internal reaction-diffusion kinetics from the interactions on the cells' boundaries and the physical mechanics between the cells, and allow for efficient and horizontally scalable simulations of large numbers of interacting cells. Our framework is highly compatible with many existing methods and allows for hybrid simulation where both coarse and detailed models can be considered at the same time. It is also greatly versatile and deployable on various high performance computing platforms, such as clusters or clouds.
The goal of this SNIC project is to study, profile and optimize our framework regarding weak and strong scaling, both on single node and distributed settings, and to validate our method by studying its convergence.