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Volume 4

Full article

Modeling and simulation of biological systems with stochasticity

Tan Chee Meng, Sandeep Somani and Pawan Dhar*

Bioinformatics Institute Singapore, 119613
Email: cheemeng@bii.a-star.edu.sg,  ssomani@bii.a-star.edu.sg,  pk@bii.a-star.edu.sg

*  corresponding author


Edited by H. Michael; received November 05, 2003; revised and accepted March 26, 2004; published April 16, 2004

Abstract

Mathematical modeling is a powerful approach for understanding the complexity of biological systems. Recently, several successful attempts have been made for simulating complex biological processes like metabolic pathways, gene regulatory networks and cell signaling pathways. The pathway models have not only generated experimentally verifiable hypothesis but have also provided valuable insights into the behavior of complex biological systems. Many recent studies have confirmed the phenotypic variability of organisms to an inherent stochasticity that operates at a basal level of gene expression. Due to this reason, development of novel mathematical representations and simulations algorithms are critical for successful modeling efforts in biological systems. The key is to find a biologically relevant representation for each representation. Although mathematically rigorous and physically consistent, stochastic algorithms are computationally expensive, they have been successfully used to model probabilistic events in the cell. This paper offers an overview of various mathematical and computational approaches for modeling stochastic phenomena in cellular systems.

Key words: in silico biology, noise, gene regulation, signal transduction, Gillespie algorithms, stochastic resonance, stochastic focusing, pathway modeling