title: Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks.
abstract: BACKGROUND
  Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein-protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs.
  
  
  RESULTS
  We developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression (BBSR)] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (>90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors.
  
  
  METHODS
  Code, datasets and networks presented in this article are available at http://bonneaulab.bio.nyu.edu/software.html.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Computational Biology Program, New York University Sackler School of Medicine, New York, NY 10065, USA.
authors.forename: Alex
authors.initials: A
authors.lastname: Greenfield
authors.forename: Christoph
authors.initials: C
authors.lastname: Hafemeister
authors.forename: Richard
authors.initials: R
authors.lastname: Bonneau
mesh_terms: D000465:Algorithms; D001412:Bacillus subtilis / Q000235:genetics / Q000378:metabolism; D001499:Bayes Theorem; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D015870:Gene Expression; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D012044:Regression Analysis; D049490:Systems Biology / Q000379:methods; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors