Constructing logical models of gene regulatory networks by integrating transcription factor-DNA interactions with expression data: an entropy-based approach.
Models of gene regulatory networks (GRNs) attempt to explain the complex processes that determine cells' behavior, such as differentiation, metabolism, and the cell cycle. The advent of high-throughput data generation technologies has allowed researchers to fit theoretical models to experimental data on gene-expression profiles. GRNs are often represented using logical models. These models require that real-valued measurements be converted to discrete levels, such as on/off, but the discretization often introduces inconsistencies into the data. Dimitrova et al. posed the problem of efficiently finding a parsimonious resolution of the introduced inconsistencies. We show that reconstruction of a logical GRN that minimizes the errors is NP-complete, so that an efficient exact algorithm for the problem is not likely to exist. We present a probabilistic formulation of the problem that circumvents discretization of expression data. We phrase the problem of error reduction as a minimum entropy problem, develop a heuristic algorithm for it, and evaluate its performance on mouse embryonic stem cell data. The constructed model displays high consistency with prior biological knowledge. Despite the oversimplification of a discrete model, we show that it is superior to raw experimental measurements and demonstrates a highly significant level of identical regulatory logic among co-regulated genes. A software implementing the method is freely available at: http://acgt.cs.tau.ac.il/modent.
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Guy
G
Karlebach
Ron
R
Shamir
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000235:genetics* / Q000378:metabolism; D019277:Entropy; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks / Q000235:genetics*; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism
D014157:Transcription Factors; D004247:DNA