Constructing logical models of gene regulatory networks by integrating transcription factor-DNA interactions with...

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.