Journal Article

Ensemble Inference and Inferability of Gene Regulatory Networks

Gunawan, Rudiyanto, Ud-Dean, S. M. Minhaz

2014

Overview

The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Bioengineering / Bioinformatics / Biology / Biology and Life Sciences / Computational Biology - standards / Computer and Information Sciences / E coli / Escherichia coli - genetics / Forecasting / Gene expression / Gene Expression Profiling - standards / Gene Expression Regulation, Bacterial / Gene Expression Regulation, Fungal / Gene Regulatory Networks / Inference / Metabolism / Metabolites / Methods / Organisms, Genetically Modified / Reproducibility of Results / Research and Analysis Methods / Research Design / Reverse engineering / Saccharomyces cerevisiae - genetics / Yeast