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Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

Adalsteinsson D, McMillen D, Elston TC - BMC Bioinformatics (2004)

Bottom Line: Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks.The discrete variables are simulated using an efficient implementation of the Gillespie algorithm.We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3250, USA. david@amath.unc.edu

ABSTRACT

Background: Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks.

Results: We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS.

Conclusions: We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

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Power spectra for the repressor protein number. Panel A is the discrete case and B is the hybrid model.
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Figure 8: Power spectra for the repressor protein number. Panel A is the discrete case and B is the hybrid model.

Mentions: The chemical species Pa, Pr, Pr_A, and Pr_A are binary random variables: they can only take on the values 0 or 1. Therefore, these species can not be approximated as continuous random variables. All the other chemical species appear in sufficient quantities to justify the continuum approximation. Figure 6B shows a time series corresponding to Fig. 6A using the hybrid model. The hybrid model was run using the semi-implicit Euler method, and for these parameter values, runs 3 times faster than full model. Visually, the agreement between the two methods appears good. To test the accuracy of the Euler method, we used BioNetS to construct 2-D histograms of R versus mRN Ar. The results for the discrete and hybrid models are shown in Figs. 7B and 7B. To construct these histograms 10, 000 oscillations were used. Excellent agreement between the discrete and hybrid model is seen. This indicates that the hybrid model is accurately sampling the steady-state distribution. To verify that the hybrid model faithfully captures the dynamics of the system, we computed the power spectra of both models. The results are shown in Figs. 8A and 8B. Again, excellent agreement is seen between the discrete and hybrid model.


Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

Adalsteinsson D, McMillen D, Elston TC - BMC Bioinformatics (2004)

Power spectra for the repressor protein number. Panel A is the discrete case and B is the hybrid model.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC408466&req=5

Figure 8: Power spectra for the repressor protein number. Panel A is the discrete case and B is the hybrid model.
Mentions: The chemical species Pa, Pr, Pr_A, and Pr_A are binary random variables: they can only take on the values 0 or 1. Therefore, these species can not be approximated as continuous random variables. All the other chemical species appear in sufficient quantities to justify the continuum approximation. Figure 6B shows a time series corresponding to Fig. 6A using the hybrid model. The hybrid model was run using the semi-implicit Euler method, and for these parameter values, runs 3 times faster than full model. Visually, the agreement between the two methods appears good. To test the accuracy of the Euler method, we used BioNetS to construct 2-D histograms of R versus mRN Ar. The results for the discrete and hybrid models are shown in Figs. 7B and 7B. To construct these histograms 10, 000 oscillations were used. Excellent agreement between the discrete and hybrid model is seen. This indicates that the hybrid model is accurately sampling the steady-state distribution. To verify that the hybrid model faithfully captures the dynamics of the system, we computed the power spectra of both models. The results are shown in Figs. 8A and 8B. Again, excellent agreement is seen between the discrete and hybrid model.

Bottom Line: Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks.The discrete variables are simulated using an efficient implementation of the Gillespie algorithm.We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3250, USA. david@amath.unc.edu

ABSTRACT

Background: Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks.

Results: We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS.

Conclusions: We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

Show MeSH
Related in: MedlinePlus