Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing

  • Authors:

  • Vincenzo Belcastro;Francesco Gregoretti;Velia Siciliano;Michele Santoro;Giovanni D'Angelo;Gennaro Oliva;Diego di Bernardo

  • Affiliations:

  • Telethon Institute of Genetics and Medicine, Naples and Open University, Milton Keynes;Institute of High Performance Computing and Networking ICAR-CNR, Naples;Telethon Institute of Genetics and Medicine, Naples and Open University, Milton Keynes;Telethon Institute of Genetics and Medicine, Naples;Telethon Institute of Genetics and Medicine, Naples and Open University, Milton Keynes;Institute of High Performance Computing and Networking ICAR-CNR, Naples;Telethon Institute of Genetics and Medicine, Naples and "Federico II" University of Naples, Naples

  • Venue:
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
  • Year:
  • 2012

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Abstract

Regulation of gene expression is a carefully regulated phenomenon in the cell. "Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover "communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.