A Boolean Algorithm for Reconstructing the Structure of Regulatory Networks

Sarika Mehra, Wei-Shou Hu, and George Karypis
Metabolic Engineering, Vol. 6. No. 4, pp. 326 - 339, 2004
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Advances in transcriptional analysis offer great opportunities to delineate the structure and hierarchy of regulatory networks in
biochemical systems. We present an approach based on Boolean analysis to reconstruct a set of parsimonious networks from gene
disruption and over expression data. Our algorithms, Causal Predictor (CP) and Relaxed Causal Predictor (RCP) distinguish the
direct and indirect causality relations from the non-causal interactions, thus significantly reducing the number of miss-predicted
edges. The algorithms also yield substantially fewer plausible networks. This greatly reduces the number of experiments required to
deduce a unique network from the plausible network structures. Computational simulations are presented to substantiate these
results. The algorithms are also applied to reconstruct the entire network of galactose utilization pathway in Saccharomyces
cerevisiae. These algorithms will greatly facilitate the elucidation of regulatory networks using large scale gene expression
profile data.
Research topics: Bioinformatics