Constructing logical models of gene regulatory networks by integrating transcription factor-DNA interactions with expression data: An entropy-based approach

Guy Karlebach, Ron Shamir

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)30-41
Number of pages12
JournalJournal of Computational Biology
Volume19
Issue number1
DOIs
StatePublished - 1 Jan 2012

Keywords

  • algorithms
  • computational molecular biology

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