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Harnessing Big Data for Systems Pharmacology

Lei Xie, Eli J. Draizen, Philip E. Bourne
doi: https://doi.org/10.1101/077115
Lei Xie
1Department of Computer Science, Hunter College, The City University of New York, U. S. A.
2The Graduate Center, The City University of New York, U. S. A.
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  • For correspondence: lei.xie@hunter.cuny.edu
Eli J. Draizen
3National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA
4Program in Bioinformatics, Boston University, Boston, MA, USA
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Philip E. Bourne
3National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA
5Office of the Director, National Institutes of Health, Bethesda, MD, U. S. A.
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Abstract

Systems pharmacology aims to holistically understand genetic, molecular, cellular, organismal, and environmental mechanisms of drug actions through developing mechanistic or predictive models. Data-driven modeling plays a central role in systems pharmacology, and has already enabled biologists to generate novel hypotheses. However, more is needed. The drug response is associated with genetic/epigenetic variants and environmental factors, is coupled with molecular conformational dynamics, is affected by possible off-targets, is modulated by the complex interplay of biological networks, and is dependent on pharmacokinetics. Thus, in order to gain a comprehensive understanding of drug actions, systems pharmacology requires integration of models across data modalities, methodologies, organismal hierarchies, and species. This imposes a great challenge on model management, integration, and translation. Here, we discuss several upcoming issues in systems pharmacology and potential solutions to them using big data technology. It will allow systems pharmacology modeling to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 23, 2016.
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Harnessing Big Data for Systems Pharmacology
Lei Xie, Eli J. Draizen, Philip E. Bourne
bioRxiv 077115; doi: https://doi.org/10.1101/077115
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Harnessing Big Data for Systems Pharmacology
Lei Xie, Eli J. Draizen, Philip E. Bourne
bioRxiv 077115; doi: https://doi.org/10.1101/077115

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