A Discrete Event Based Stochastic Simulation Platform for ‘In silico’ Study of Molecular-level Cellular Dynamics

摘要

With the completion of the human genome project and the complete genome sequencing of other organisms, huge databases cataloguing the various molecular “parts” of complex biological systems, have been opened up to scientists. As huge volumes of high throughput experimental data become available, the focus is shifting from studying biological systems as static models of loosely linked molecular devices to understanding their ensemble dynamics. In this work, we present a discrete event based stochastic modeling approach for studying the molecular dynamics of cells. In this approach, a biological process is modeled as a collection of interacting functions driven in time by a set of discrete events. We develop the simulation methodology and present the mathematical formalism underlying the in silico system. We present the core components of the simulation framework, called iSimBioSys, which interactively simulates the dynamics of a biological process. As a test bed for illustrating our approach in studying cell dynamics, we model the two component PhoPQ system, responsible for the expression of several virulence genes in the gram-negative bacteria Salmonella Typhimurium. We analyze the effect of extracellular magnesium on the behavioral dynamics of this pathway using our framework and validate the results with a wet-lab experimental system. We also analyze the performance of iSimBioSys as a biosimulation tool, based on the model biological system in terms of system usage and response. We envision that such a discrete event based stochastic biosimulation platform can provide a generic, computing paradigm for testing various hypotheses of an experiment“in silico”.