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Learning Global Models of Transcriptional Regulatory Networks from Data

  • Aviv Madar
  • Richard Bonneau
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 541)

Abstract

Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.

Key words

Network inference biclustering network reconstruction microarray data-integration cMonkey archaea the Inferelator 

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Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Aviv Madar
    • 1
  • Richard Bonneau
    • 2
  1. 1.Center for Comparative Functional GenomicsNew York UniversityNew YorkUSA
  2. 2.Department of Computer Science, Courant Institute of Mathematical SciencesNew York UniversityNew YorkUSA

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