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From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks (2002)
| Venue: | Proc. IEEE |
| Citations: | 123 - 23 self |
BibTeX
@INPROCEEDINGS{Shmulevich02fromboolean,
author = {Ilya Shmulevich and Edward R. Dougherty and Wei Zhang},
title = {From Boolean to Probabilistic Boolean Networks as Models of Genetic Regulatory Networks},
booktitle = {Proc. IEEE},
year = {2002},
pages = {1778--1792}
}
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Abstract
Mathematical and computational modeling of genetic regulatory networks promises to uncover the fundamental principles governing biological systems in an integrarive and holistic manner. It also paves the way toward the development of systematic approaches for effective therapeutic intervention in disease. The central theme in this paper is the Boolean formalism as a building block for modeling complex, large-scale, and dynamical networks of genetic interactions. We discuss the goals of modeling genetic networks as well as the data requirements. The Boolean formalism is justified from several points of view. We then introduce Boolean networks and discuss their relationships to nonlinear digital filters. The role of Boolean networks in understanding cell differentiation and cellular functional states is discussed. The inference of Boolean networks from real gene expression data is considered from the viewpoints of computational learning theory and nonlinear signal processing, touching on computational complexity of learning and robustness. Then, a discussion of the need to handle uncertainty in a probabilistic framework is presented, leading to an introduction of probabilistic Boolean networks and their relationships to Markov chains. Methods for quantifying the influence of genes on other genes are presented. The general question of the potential effect of individual genes on the global dynamical network behavior is considered using stochastic perturbation analysis. This discussion then leads into the problem of target identification for therapeutic intervention via the development of several computational tools based on first-passage times in Markov chains. Examples from biology are presented throughout the paper. 1
Keyphrases
probabilistic boolean network genetic regulatory network boolean network markov chain boolean formalism real gene expression data several computational tool nonlinear signal processing nonlinear digital filter several point building block data requirement probabilistic framework computational learning theory target identification global dynamical network behavior biological system fundamental principle holistic manner general question potential effect therapeutic intervention genetic network genetic regulatory network promise genetic interaction systematic approach computational modeling stochastic perturbation analysis computational complexity first-passage time dynamical network individual gene cell differentiation central theme cellular functional state effective therapeutic intervention
