A fast and efficient gene-network reconstruction method from multiple over-expression experiments

Abstract

BackgroundReverse engineering of gene regulatory networks presents one of the big challenges in systems biology. Gene regulatory networks are usually inferred from a set of single-gene over-expressions and/or knockout experiments. Functional relationships between genes are retrieved either from the steady state gene expressions or from respective time series.ResultsWe present a novel algorithm for gene network reconstruction on the basis of steady-state gene-chip data from over-expression experiments. The algorithm is based on a straight forward solution of a linear gene-dynamics equation, where experimental data is fed in as a first predictor for the solution. We compare the algorithm's performance with the NIR algorithm, both on the well known E. coli experimental data and on in-silico experiments.ConclusionWe show superiority of the proposed algorithm in the number of correctly reconstructed links and discuss computational time and robustness. The proposed algorithm is not limited by combinatorial explosion problems and can be used in principle for large networks.

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

“…The topology of the predicted network often determines the type of the best method. This is one reason, why combination of various methods (or the use of iterative approaches) may outperform individual methodologies (Liang et al, 1998a; Akutsu et al, 1999; Ideker et al, 2000; Kholodenko et al, 2002; Yeung et al, 2002; Segal et al, 2003; Tegnér et al, 2003; Friedman, 2004; Tegnér & Björkegren, 2007; Cosgrove et al, 2008; Kim et al, 2008; Ahmed & Xing, 2009; Stokić et al, 2009; Marbach et al, 2010; Yip et al, 2010; Pham et al, 2011; Schaffter et al, 2011; Altay, 2012; Crombach et al, 2012; Kotera et al, 2012; Marbach et al, 2012). Jurman et al (2012a) designed a network sampling stability-based tool to assess network reconstruction performance.…”

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