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Author(s): Shmulevich I; Dougherty ER; Mang W
Title: From Boolean to probabilistic Boolean networks as models of genetic regulatory networks
Source: PROCEEDINGS OF THE IEEE 90 (11): 1778-1792
Date: 2002 NOV
Document Type: Journal : Review
DOI:
Language: English
Comment:
Address: Univ Texas, MD Anderson Canc Ctr, Canc Genomics Lab, Houston, TX 77030 USA.
Texas A&M Univ, Dept Elect Engn, College Stn, TX 77843 USA. Reprint: Shmulevich, I, Univ Texas, MD Anderson Canc Ctr, Canc Genomics Lab,
1515 Holcombe Blvd, Houston, TX 77030 USA. E-mail:
Author Keywords: attractor; best-fit extension; Boolean network; cell differentiation;
coefficient of determination; consistency problem; gene; genetic
network; influence; Markov chain; microarray; nonlinear filter;
probabilistic Boolean network; root signal
KeyWords Plus: STACK FILTERS; ASSOCIATIVE MEMORY; LOGICAL ANALYSIS; ROOT PROPERTIES;
DRUG DISCOVERY; EXPRESSION; CLASSIFICATION; MICROARRAYS; ARRAYS;
PREDICTION
Abstract: Mathematical and computational modeling of genetic regulatory networks promises to uncover the fundamental principles governing biological systems in an integrative 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.
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