Stochastic simulation and analysis of biomolecular reaction networks

Specific issues affecting the analysis and interpretation of simulation data are investigated, including: (1) the effect of time interval on data presentation and time-weighted averaging of molecule numbers, (2) effect of time averaging interval on reaction rate analysis, (3) effect of number of simulations on precision of model predictions, and (4) implications of stochastic simulations on optimization procedures.The two main factors affecting the analysis of stochastic simulations are: (1) the selection of time intervals to compute or average state variables and (2) the number of simulations generated to evaluate the system behavior.All biological processes at the cellular level are the consequence of a series of chemical-physical reactions at the molecular level that occur within the micro-volume of the cell. The collection of molecular species and the reactions among them is referred to here as a 'biomolecular reaction network'. The complete biomolecular reaction network for a cell includes thousands of molecular components and reactions involved in transcription, translation, molecular self-assembly, metabolic reactions, transport and physical movements. Since these reactions occur in an extremely small reaction volume, the number of molecules of any one molecular species that can participate in a given reaction ranges from single copies of genes to several hundred molecules of chemicals at the μM concentration to several hundred thousand molecules of chemicals at the mM concentration. As a consequence of the fact that a subset of all the reactions in the system involve low copy numbers of substrate molecules, the behavior of individual instances of the system cannot be modeled accurately using continuous deterministic (C-D) approaches ([1-3]). Thus, these natural micro-systems should be modeled and simulated using basic theory of discrete stochastic (D-S) chemical kinetics [4].With the evolution of systems biology in recent years, interest in modeling and simu