Harnessing Big Data for Systems Pharmacology.
Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.
Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065; email: lei.xie@hunter.cuny.edu.|The Graduate Center, The City University of New York, New York, NY 10016.
Lei
L
Xie
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894; email: philip.bourne@nih.gov.|Program in Bioinformatics, Boston University, Boston, Massachusetts 02215.
Eli J
EJ
Draizen
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894; email: philip.bourne@nih.gov.|Office of the Director, National Institutes of Health, Bethesda, Maryland 20894.
Philip E
PE
Bourne
D000818:Animals; D003627:Data Interpretation, Statistical; D016208:Databases, Factual / Q000706:statistics & numerical data*; D057166:High-Throughput Screening Assays / Q000379:methods / Q000639:trends; D006801:Humans; D010601:Pharmacology, Clinical / Q000379:methods* / Q000639:trends; D049490:Systems Biology / Q000379:methods* / Q000639:trends
NIH Commons; cloud computing; computational modeling; data science; machine learning; semantic web; systems biology; systems pharmacology modeling