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      Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

      , , ,
      Bioinformatics
      Oxford University Press (OUP)

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          Abstract

          Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.

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          Author and article information

          Journal
          Bioinformatics
          Bioinformatics
          Oxford University Press (OUP)
          1367-4803
          1460-2059
          February 01 2002
          February 01 2002
          : 18
          : 2
          : 261-274
          Article
          10.1093/bioinformatics/18.2.261
          11847074
          e14bcf4d-8230-4ac6-bbac-a97b96bda78c
          © 2002
          History

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