Abstract
BackgroundThe inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature. One important consequence of underdetermination is the existence of many possible solutions to this inference. Our previously proposed ensemble inference algorithm TRaCE addressed this issue by inferring an ensemble of network directed graphs (digraphs) using differential gene expressions from gene knock-out (KO) experiments. However, TRaCE could not deal with the mode of the transcriptional regulations (activation or repression), an important feature of GRNs.ResultsIn this work, we developed a new algorithm called TRaCE+ for the inference of an ensemble of signed GRN digraphs from transcriptional expression data of gene KO experiments. The sign of the edges indicates whether the regulation is an activation (positive) or a repression (negative). TRaCE+ generates the upper and lower bounds of the ensemble, which define uncertain regulatory interactions that could not be verified by the data. As demonstrated in the case studies using Escherichia coli GRN and 100-gene gold-standard GRNs from DREAM 4 network inference challenge, by accounting for regulatory signs, TRaCE+ could extract more information from the KO data than TRaCE, leading to fewer uncertain edges. Importantly, iterating TRaCE+ with an optimal design of gene KOs could resolve the underdetermined issue of GRN inference in much fewer KO experiments than using TRaCE.ConclusionsTRaCE+ expands the applications of ensemble GRN inference strategy by accounting for the mode of the gene regulatory interactions. In comparison to TRaCE, TRaCE+ enables a better utilization of gene KO data, thereby reducing the cost of tackling underdetermined GRN inference. TRaCE+ subroutines for MATLAB are freely available at the following website: http://www.cabsel.ethz.ch/tools/trace.html.
Highlights
The inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature
As we demonstrated recently [12], these difficulties arose because the above GRN inference problems were underdetermined, i.e. the GRNs were not inferable and there could exist an ensemble of GRN structures that agreed with the gene KO data
We previously developed transitive reduction and closure ensemble (TRaCE) (Transitive Reduction and Closure Ensemble) for constructing an ensemble of GRN structures that are consistent with the input transcriptional expression data of all genes in the network from gene KO experiments [12]
Summary
The inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature. TRaCE could not deal with the mode of the transcriptional regulations (activation or repression), an important feature of GRNs. The central dogma of molecular biology describes the process by which genetic information flows linearly from deoxyribonucleic acid (DNA) to ribonucleic acid (RNA) to proteins through the process of transcription and translation [1]. A multitude of network inference methods exist in the literature for the identification of GRN structure from gene transcriptional expression data [3,4,5,6,7,8,9]. These methods adapted concepts and techniques from multiple disciplines such as information theory, statistics, machine learning and systems theory. The DREAM (Dialogue for Reverse Engineering, Assessment and Methods) project materialized as an answer [10], and the GRN inference became a topic in several community-wide challenges within this project
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