1 Studying single cell molecular dynamics in silico: A discrete event based stochastic simulation approach Samik Ghosh, Preetam Ghosh, Kalyan Basu, Sajal Das and Simon Daefler 2 The University of Texas at Arlington, Arlington, Texas, 760190015, USA 2 Mount Sinai School of Medicine, New York, USA Email: {sghosh, ghosh, basu, das}@cse.uta.edu, simon.daefler@mssm.edu Abstract—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. Index Terms—silico modeling and simulation, stochastic modeling, discrete event simulation, biosimulation I. INTRODUCTION Traditionally, the key focus of biology has been on detailed understanding of single genes, molecules or processes involved in particular phenotypic manifestations. This powerful approach has resulted in a significant understanding of the structure and function of individual genes and proteins. In the recent past, with the development of high throughput micro array experiments and bio chips, an explosive amount of empirical data on the molecular foundations of biological structures and functions [1] have been opened up to researchers. Complete genomic sequencing of new organisms has been completed and advanced databases like Genome Bank (GenBank), Protein Database (PDB), which store comprehensive anno- tations of genomic and protein structures, are being developed at previously unimaginable rates. Concomitant with this development, a large body of knowledge is being derived from different biological pathways activated by different regulatory genes, hormones and metabolic reactions through fluorescence tagging and other types of advanced in-vitro experiments. These results are captured in large volume of scientific papers and experimental data in PubMed [55] and other databases.