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

Ilya Shmulevich, Edward R. Dougherty, Seungchan Kim et al.

2002 · Bioinformatics · 1,617 citations

MOTIVATION: 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. RESULTS: We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty.…

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