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  • 2012
  • Issue No. 3 - May-June
  • Abstract - Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing
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Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing
May-June 2012 (vol. 9 no. 3)
pp. 668-678
Diego di Bernardo, Telethon Inst. of Genetics & Med. (TIGEM), Naples, Italy
G. D'Angelo, Telethon Inst. of Genetics & Med. (TIGEM), Naples, Italy
M. Santoro, Telethon Inst. of Genetics & Med. (TIGEM), Naples, Italy
V. Siciliano, Telethon Inst. of Genetics & Med. (TIGEM), Naples, Italy
F. Gregoretti, Inst. of High Performance Comput. & Networking ICAR-CNR, Naples, Italy
V. Belcastro, Telethon Inst. of Genetics & Med. (TIGEM), Naples, Italy
G. Oliva, Inst. of High Performance Comput. & Networking ICAR-CNR, Naples, Italy
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.

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Index Terms:
reverse engineering,biology computing,cellular biophysics,genetics,genomics,liver,molecular biophysics,parallel algorithms,proteins,mitochondrial proteins,genome-wide gene regulatory networks,gene expression profiles,high-performance computing,mammalian cells,parallel computing algorithm,reverse-engineer genome-scale gene regulatory networks,pairwise mutual information,gene-pair,Mus Musculus gene regulatory network,liver,public repository,parallel hierarchical clustering algorithm,network community,biological functions,Probes,Gene expression,Proteins,Bioinformatics,Mutual information,Mice,Joints,parallel computing.,Reverse engineering,gene regulatory network,clustering algorithm
Diego di Bernardo, G. D'Angelo, M. Santoro, V. Siciliano, F. Gregoretti, V. Belcastro, G. Oliva, "Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from Gene Expression Profiles Using High-Performance Computing," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 668-678, May-June 2012, doi:10.1109/TCBB.2011.60
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