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A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems
March/April 2011 (vol. 8 no. 2)
pp. 335-341
ASCII Text
x 
 
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.
 
 
BibTex
x
 
@article{ 10.1109/TCBB.2010.97,
author = {Amit Sabnis and Robert W. Harrison},
title = {A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems},
journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
volume = {8},
number = {2},
issn = {1545-5963},
year = {2011},
pages = {335-341},
doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.97},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
TI - A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems
IS - 2
SN - 1545-5963
SP335
EP341
EPD - 335-341
A1 - Amit Sabnis,
A1 - Robert W. Harrison,
PY - 2011
KW - Systems biology
KW - stochastic simulation
KW - deterministic
KW - biochemical networks.
VL - 8
JA - IEEE/ACM Transactions on Computational Biology and Bioinformatics
ER -
 
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.

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Index Terms:
Systems biology, stochastic simulation, deterministic, biochemical networks.
Citation:
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
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