Studying single cell molecular dynamics in silico : A discrete event based stochastic simulation approach

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 simulation approach for studying the molecular dynamics of single 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 outline the mathematical formalism underlying the in silico modeling technique, present the simulation algorithm and delineate the core software components of the discrete event framework, called iSimBioSys. The accuracy of the simulation methodology is confirmed on a test-bed signal transduction pathway, the two component PhoPQ system, responsible for the expression of several virulence genes in the gram-negative bacteria Salmonella Typhimurium. The dynamic behavior of the systems is analyzed and validated against a wet-lab experimental setup for the same pathway. We also measure the performance of iSimBioSys as a biosimulation tool, based on the model biological system in terms of system usage and response.