Abstract
BackgroundIn recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. However, the effects of various stochastic simulation and data analysis conditions on the observed dynamics of complex biomolecular reaction networks have not recieved much attention. In order to investigate these issues, we employed a a software package developed in out group, called Biomolecular Network Simulator (BNS), to simulate and analyze the behavior of such systems. The behavior of a hypothetical two gene in vitro transcription-translation reaction network is investigated using the Gillespie exact stochastic algorithm to illustrate some of the factors that influence the analysis and interpretation of these data.ResultsSpecific 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.ConclusionThe 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.
Highlights
In recent years, several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks
Due to the scale of the model (249 state variables), it is not possible to show the total set of data for all state variables, but a few selected and important state variables are shown in Figure 2 to illustrate the general behavior of the system
Since GTP is utilized by both mRNA synthesis as a substrate and protein synthesis as an energy source, both transcription of messenger RNA and translation of the protein products are simultaneously terminated at the time when GTP is depleted
Summary
Several stochastic simulation algorithms have been developed to generate Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks. 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. BMC Systems Biology 2009, 3:64 http://www.biomedcentral.com/1752-0509/3/64 numbers of substrate molecules, the behavior of individual instances of the system cannot be modeled accurately using continuous deterministic (C-D) approaches ([1,2,3]) These natural micro-systems should be modeled and simulated using basic theory of discrete stochastic (D-S) chemical kinetics [4]
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