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Issue No.02 - March/April (2011 vol.8)
pp: 335-341
Amit Sabnis , Georgia State University, Atlanta
Robert W. Harrison , Georgia State University, Atlanta
Computational systems biology is largely driven by mathematical modeling and simulation of biochemical networks, via continuous deterministic methods or discrete event stochastic methods. Although the deterministic methods are efficient in predicting the macroscopic behavior of a biochemical system, they are severely limited by their inability to represent the stochastic effects of random molecular fluctuations at lower concentration. In this work, we have presented a novel method for simulating biochemical networks based on a deterministic solution with a modification that permits the incorporation of stochastic effects. To demonstrate the feasibility of our approach, we have tested our method on three previously reported biochemical networks. The results, while staying true to their deterministic form, also reflect the stochastic effects of random fluctuations that are dominant as the system transitions into a lower concentration. This ability to adapt to a concentration gradient makes this method particularly attractive for systems biology-based applications.
Systems biology, stochastic simulation, deterministic, biochemical networks.
Amit Sabnis, Robert W. Harrison, "A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.8, no. 2, pp. 335-341, March/April 2011, doi:10.1109/TCBB.2010.97
[1] H. Kitano, "Computational Systems Biology," Nature, vol. 420, pp. 206-210, Nov. 2002.
[2] H. Kitano, "Systems Biology: A Brief Overview," Science, vol. 295, pp. 1662-1664, Mar. 2002.
[3] D.T. Gillespie, "Exact Stochastic Simulation of Coupled Chemical Reactions," J. Physical Chemistry, vol. 81, pp. 2340-2361, Dec. 1977.
[4] D.T. Gillespie, "Approximate Accelerated Stochastic Simulation of Chemically Reacting Systems," J. Chemical Physics, vol. 115, pp. 1716-1732, Apr. 2001.
[5] T.R. Kiehl, R.M. Mattheyses, and M.K. Simmons, "Hybrid Simulation of Cellular Behavior," Bioinformatics, vol. 20, pp. 316-322, July 2004.
[6] H. Salis and Y. Kaznessis, "Accurate Hybrid Stochastic Simulation of a System of Coupled Chemical or Biochemical Reactions," J. Chemical Physics, vol. 122, pp. 54-103, Jan. 2005.
[7] D.J. Wilkinson,Stochastic Modelling for Systems Biology. Chapman and Hall/CRC Press, 2006.
[8] S. Ramsey, D. Orrell, and H. Bolouri, "Dizzy: Stochastic Simulation of Large-Scale Genetic Regulatory Networks," J. Bioinformatics and Computational Biology, vol. 3, pp. 415-436, Apr. 2005.
[9] Q. Zhang, S. Bhattacharya, D.E. Kline, R.B. Crawford, R.B. Conolly, R.S. Thomas, N.E. Kaminski, and M.E. Anderson, "Stochastic Modeling of B Lymphocyte Terminal Differentiation and Its Suppression by Dioxin," BMC Systems Biology, vol. 4:40, pp. 1-18, Apr. 2010.
[10] A. Sabnis and R.W. Harrison, "Simulation of the Oscillatory Dynamics of Blood Testosterone Levels Using the Crossover Method," Proc. IEEE Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1-6, May 2010.
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