STOCKS: STOChastic Kinetic Simulation of biochemical processes with Gillespie algorithm.

STOCKS is a public domain (GNU GPL) software for stochastic simulations of biochemical processes. The features of the software are briefly listed below. For more details see manual and example files in software distribution. New version (stocks 2.0) implements novel hybrid simulation algorithm and provides SBML support (download).

Algorithm: STOCKS uses Gillespie's direct method [1] to simulate time evolution of the system composed of large number of first and second order chemical reactions. The program can perform simulations in the time scale of several cellular generations using linearly growing volume of reaction environment and simple model of cell division. Substances which are in equilibrium resulting from the competition of large number of processes can be modeled as random pools with Gaussian distribution.

Tests: The program has been already applied to study the dependence between transcription and translation initiation rates and the magnitude of stochastic fluctuations in prokaryotic gene expression [2,3]. Reference [3] shows also the performance of the software in the simulation involving reactions with rates varying by several orders of magnitude - gene expression and enzymatic activities of expressed proteins.

Platform: STOCKS is best suited to be run as a background process under UNIX operating system. Current distribution is tested under Linux and IRIX. The program can be compiled also for the other platforms as the source codes in standard C++ are available.

Utility programs: Distribution of STOCKS includes utility programs for averaging trajectories collected during simulations. The 2 dimensional phase plot presented here have been computed automatically with the use of PERL script running STOCKS and utility programs. The script is also available in the software distribution.

Availability: STOCKS is available under GNU GPL license from : http://www.sysbio.pl/stocks/stocks1.02.tar.gz Distribution contains C++ source codes, executables for Linux and IRIX, documentation and examples including the model of prokaryotic gene expression presented here.

Version 2.0: The biochemical reaction networks include elementary reactions differing by many orders of magnitude in the numbers of molecules involved. The kinetics of reactions involving small numbers of molecules can be studied by exact stochastic simulation implemented in the current version of STOCKS. This approach is not practical for the simulation of metabolic processes because of the computational cost of accounting for individual molecular collisions. STOCKS 2.0 implements “maximal timestep method” (4), a novel approach combining Gibson & Bruck 2001 (5) algorithm with tau-leap method of Gillespie 2001 (6). This algorithm allows stochastic simulation of systems composed of both intensive metabolic reactions and regulatory processes involving small numbers of molecules.

In our recent paper (4) we show application of maximal timestep method to the simulation of glucose, lactose and glycerol metabolism in E.coli. The gene expression, signal transduction, transport and enzymatic activities are modeled simultaneously. We show that random fluctuations in gene expression can propagate to the level of metabolic processes. In the cells switching from glucose to a mixture of lactose and glycerol, random delays in transcription initiation determine whether lactose or glycerol operon is induced. In a small fraction of cells severe decrease in metabolic activity may also occur. Both effects are epigenetically inherited by the progeny of the cell in which the random delay in transcription initiation occurred.

Download: vesrion 1, Linux, Stocks 2.0 Linux, Stocks 2.0 Win32, Stocks 2.0 input editor (Java)

REFERENCES

[1] Gillespie D.T. (1977) Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81, 2340-2361.

[2] Kierzek A.M., Zaim J. and Zielenkiewicz P, (2001) The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression. J. Biol. Chem. 276, 8165-8172. [Free full text at JBC]

[3] Kierzek A.M. (2002) STOCKS: STOChastic Kinetic Simulations of biochemical systems with Gillespie algorithm. Bioinformatics 18, 470-481

[4] Puchalka J. and Kierzek A.M. (2004) Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks. Biophys J. 86,1357-1372

[5] Gibson, M. A. and J. Bruck. 2000. Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. 104:1876-1889.

[6] Gillespie, D. T. (2001). Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115:1716-1733.