Time accelerated Monte Carlo simulations of biological networks using the binomial tau-leap method.
Developing a quantitative understanding of intracellular networks requires simulations and computational analyses. However, traditional differential equation modeling tools are often inadequate due to the stochasticity of intracellular reaction networks that can potentially influence the phenotypic characteristics. Unfortunately, stochastic simulations are computationally too intense for most biological systems. Herein, we have utilized the recently developed binomial tau-leap method to carry out stochastic simulations of the epidermal growth factor receptor induced mitogen activated protein kinase cascade. Results indicate that the binomial tau-leap method is computationally 100-1000 times more efficient than the exact stochastic simulation algorithm of Gillespie. Furthermore, the binomial tau-leap method avoids negative populations and accurately captures the species populations along with their fluctuations despite the large difference in their size.


BACKGROUND
http://www.dion.che.udel.edu/multiscale/Introduction.html. Fortran 90 code available for academic use by email.


BACKGROUND
Details about the binomial tau-leap algorithm, software and a manual are available at the above website.
Chatterjee
Abhijit
A
Department of Chemical Engineering, University of Delaware, Newark, 19716, USA.
Mayawala
Kapil
K
Edwards
Jeremy S
JS
Vlachos
Dionisios G
DG
D000465:Algorithms; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D012815:Signal Processing, Computer-Assisted; D015398:Signal Transduction / Q000502:physiology*; D013997:Time Factors