Harnessing Big Data for Systems Pharmacology

被引:34
作者
Xie, Lei [1 ,2 ]
Draizen, Eli J. [3 ,4 ]
Bourne, Philip E. [3 ,5 ]
机构
[1] CUNY, Hunter Coll, Dept Comp Sci, New York, NY 10065 USA
[2] CUNY, Grad Ctr, New York, NY 10016 USA
[3] NIH, Natl Ctr Biotechnol Informat, Natl Lib Med, Bethesda, MD 20894 USA
[4] Boston Univ, Program Bioinformat, Boston, MA 02215 USA
[5] NIH, Off Director, Bethesda, MD 20894 USA
来源
ANNUAL REVIEW OF PHARMACOLOGY AND TOXICOLOGY, VOL 57 | 2017年 / 57卷
关键词
cloud computing; data science; machine learning; semantic web; computational modeling; systems biology; systems pharmacology modeling; NIH Commons; SEMANTIC WEB TECHNOLOGIES; NEED EXACT SOLVERS; DRUG; MODELS; GENOMICS; DISEASE; BIOLOGY; KNOWLEDGEBASE; MECHANISMS; RESOURCES;
D O I
10.1146/annurev-pharmtox-010716-104659
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
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.
引用
收藏
页码:245 / 262
页数:18
相关论文
共 89 条
  • [11] Bourne PE, 2015, NATURE, V527, pS16, DOI 10.1038/527S16a
  • [12] Breiman L, 1996, MACH LEARN, V24, P49
  • [13] Callahan Alison, 2013, Semantic Web: Semantics and Big Data. Proceedings of 10th International Conference (ESWC 2013): LNCS 7882, P200
  • [14] Collective judgment predicts disease-associated single nucleotide variants
    Capriotti, Emidio
    Altman, Russ B.
    Bromberg, Yana
    [J]. BMC GENOMICS, 2013, 14
  • [15] Antibacterial mechanisms identified through structural systems pharmacology
    Chang, Roger L.
    Xie, Lei
    Bourne, Philip E.
    Palsson, Bernhard O.
    [J]. BMC SYSTEMS BIOLOGY, 2013, 7
  • [16] Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model
    Chang, Roger L.
    Xie, Li
    Xie, Lei
    Bourne, Philip E.
    Palsson, Bernhard O.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2010, 6 (09)
  • [17] Reply to "Do genome-scale models need exact solvers or clearer standards?"
    Chindelevitch, Leonid
    Trigg, Jason
    Regev, Aviv
    Berger, Bonnie
    [J]. MOLECULAR SYSTEMS BIOLOGY, 2015, 11 (10)
  • [18] An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models
    Chindelevitch, Leonid
    Trigg, Jason
    Regev, Aviv
    Berger, Bonnie
    [J]. NATURE COMMUNICATIONS, 2014, 5
  • [19] Chiu SH, 2015, BIORXIV101101024513
  • [20] A community effort to assess and improve drug sensitivity prediction algorithms
    Costello, James C.
    Heiser, Laura M.
    Georgii, Elisabeth
    Gonen, Mehmet
    Menden, Michael P.
    Wang, Nicholas J.
    Bansal, Mukesh
    Ammad-ud-din, Muhammad
    Hintsanen, Petteri
    Khan, Suleiman A.
    Mpindi, John-Patrick
    Kallioniemi, Olli
    Honkela, Antti
    Aittokallio, Tero
    Wennerberg, Krister
    Collins, James J.
    Gallahan, Dan
    Singer, Dinah
    Saez-Rodriguez, Julio
    Kaski, Samuel
    Gray, Joe W.
    Stolovitzky, Gustavo
    [J]. NATURE BIOTECHNOLOGY, 2014, 32 (12) : 1202 - U57