A central challenge for systems biology is to transform the data deluge characteristic of the post- genomic world, into the kind of knowledge that will lead to advances in biomedical science and clinical medicine. The transformation requires computer systems that will enable organizing, mining, analyzing and displaying the data in a manner that both informs and drives new hypotheses. VisANT (a visualization, mining system) and Predictome (an integrated database component) form an online graphical workspace and interface for principled integration, mining, discovery, and analysis of molecular networks. The system currently includes, or seamlessly accesses, data sets based on some 70 laboratory and computational methods, and more than 100 species. It has a distinguished and active advisory Board, more than 1000 registered users, is accessed by more than 1100 independent sites per month and has been cited approximately 300 times in the scientific literature. We propose substantially expanding the range of VisANT by including metabolic, chemical reaction, drug and disease related data. Equally importantly, a central goal of VisANT has been integration, and we will substantially extend its integrative capabilities, enabling the identification of disease related pathways, of similarities in mechanisms between phenotypically distinct diseases, and the identification of potential lead compounds for therapy. At the same time we propose substantial expansion of our outreach activities, and movement toward a true many-to many communication and discovery system.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Special Emphasis Panel (ZRG1-BST-H (50))
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Ravichandran, Veerasamy
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Boston University
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Zhao, Qi; Stettner, Arion I; Reznik, Ed et al. (2016) Mapping the landscape of metabolic goals of a cell. Genome Biol 17:109
Zomorrodi, Ali R; Segrè, Daniel (2016) Synthetic Ecology of Microbes: Mathematical Models and Applications. J Mol Biol 428:837-61
Chen, Hsiao-Rong; Sherr, David H; Hu, Zhenjun et al. (2016) A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer. BMC Med Genomics 9:51
Liu, Yang; Hu, Zhenjun; DeLisi, Charles (2016) Mutated Pathways as a Guide to Adjuvant Therapy Treatments for Breast Cancer. Mol Cancer Ther 15:184-9
Granger, Brian R; Chang, Yi-Chien; Wang, Yan et al. (2016) Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0. PLoS Comput Biol 12:e1004875
Liu, Yang; Tian, Feng; Hu, Zhenjun et al. (2015) Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers. Sci Rep 5:10204
Maino, Barbara; D'Agata, Velia; Severini, Cinzia et al. (2015) Igf1 and Pacap rescue cerebellar granule neurons from apoptosis via a common transcriptional program. Cell Death Discov 1:
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Harcombe, William R; Riehl, William J; Dukovski, Ilija et al. (2014) Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep 7:1104-15
Hu, Zhenjun (2014) Using VisANT to Analyze Networks. Curr Protoc Bioinformatics 45:8.8.1-39

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