Stochastic Simulation of Biomolecular Networks in Dynamic Environments

• DOI: 10.1371/journal.pcbi.1004923
• Publication Date: 2016-06-02T00:53:33Z
• Authors: Margaritis Voliotis; Philipp Thomas; Ramon Grima; Clive G. Bowsher
• Keywords: 0301 basic medicine; 570; Quantitative Biology - Subcellular Processes; Bioinformatics; QH301-705.5; Molecular Networks (q-bio.MN); 530; Quantitative Biology - Quantitative Methods; Models, Biological; 03 medical and health sciences; Models; Quantitative Biology - Molecular Networks; Computer Simulation; Biology (General); Subcellular Processes (q-bio.SC); 01 Mathematical Sciences; Quantitative Methods (q-bio.QM); 08 Information And Computing Sciences; Stochastic Processes; Systems Biology; 500; Computational Biology; Quorum Sensing; 06 Biological Sciences; Biological; FOS: Biological sciences; Algorithms; Metabolic Networks and Pathways; Research Article

Download source code for SI information<br/>Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate, using decision-making by a large population of quorum sensing bacteria, that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.<br/>