AB-QSSPN: Integration of Agent-Based Simulation of Cellular Populations with Quasi-Steady State Simulation of Genome Scale Intracellular Networks

摘要

We present a tool for simulation of populations of living cells interacting in spatial structures. Each cell is modelled with the Quasi-Steady Petri Net that integrates dynamic regulatory network expressed with a Petri Net (PN) and Genome Scale Metabolic Networks (GSMN) where linear programming is used to explore the steady-state metabolic flux distributions in the whole-cell model. Similar simulations have already been conducted for single cells, but we present an architecture to simulate populations of millions of interacting cells organized in spatial structures which can be used to model tumour growth or formation of tuberculosis lesions. For that we use the Spark framework and organize the computation in an agent based "think like a vertex" fashion as in Pregel like systems. In the cluster we introduce a special kind of per node caching to speed up computation of the steady-state metabolic flux. Our tool can be used to provide a mechanistic link between genotype and behaviour of multicellular system.