A context-specific network of protein-DNA and protein-protein interactions reveals new regulatory motifs in human B cells

  • Authors:

  • Celine Lefebvre;Wei Keat Lim;Katia Basso;Riccardo Dalla Favera;Andrea Califano

  • Affiliations:

  • Center for Computational Biology and Bioinformatics, Columbia University, New York, NY;Center for Computational Biology and Bioinformatics, Columbia University, New York, NY;Institute of Cancer Genetics, Columbia University, New York, NY;Institute of Cancer Genetics, Columbia University, New York, NY;Center for Computational Biology and Bioinformatics, Columbia University, New York, NY

  • Venue:
  • RECOMB'06 Proceedings of the joint 2006 satellite conference on Systems biology and computational proteomics
  • Year:
  • 2006

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Abstract

Recent genome wide studies in yeast have started to unravel the complex, combinatorial nature of transcriptional regulation in eukaryotic cells, including the concerted regulation of proteins involved in complex formation. In this work, we use a Bayesian evidence integration framework to assemble an integrated network, including both protein-DNA and protein-protein interactions, in a specific cellular context (human B cells). We then use it to study common interaction motifs between protein complexes and regulatory programs, using an enrichment analysis approach. Specifically, we compare the frequency of mixed interaction motifs in the real network against random networks of equivalent connectivity. These motifs include sets of target genes regulated by multiple interacting transcription factors, and gene sets encoding same complex proteins regulated by different transcription factors.