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Control Theory and Systems Biology$
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Pablo A. Iglesias and Brian P. Ingalls

Print publication date: 2009

Print ISBN-13: 9780262013345

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262013345.001.0001

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Modeling and Analysis of Stochastic Biochemical Networks

Modeling and Analysis of Stochastic Biochemical Networks

Chapter:
(p.29) 2 Modeling and Analysis of Stochastic Biochemical Networks
Source:
Control Theory and Systems Biology
Author(s):

Mustafa Khammash

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262013345.003.0002

This chapter introduces types of stochastic modeling used to address the probabilistic nature of processes. It reviews some of the key approaches for this modeling and considers the analysis of cellular noise and the resulting fluctuations in the copy numbers of cellular constituents. It addresses the origins and implications of noise in biological networks. It then describes the stochastic framework for modeling chemical reactions and provides a link between stochastic and deterministic descriptions. This chapter shows that the density computation methods are especially suitable for very low molecule counts, moment closure methods for medium to large counts, and the linear noise approximation for very large counts.

Keywords:   stochastic modeling, cellular noise, biological networks, chemical reactions, density computation methods, linear noise approximation, moment closure methods

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