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WIREs Syst Biol Med

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Systems medicine: evolution of systems biology from bench to bedside

Overview

Rui‐Sheng Wang, Bradley A. Maron, Joseph Loscalzo

Published Online: Apr 17 2015

DOI: 10.1002/wsbm.1297

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High‐throughput experimental techniques for generating genomes, transcriptomes, proteomes, metabolomes, and interactomes have provided unprecedented opportunities to interrogate biological systems and human diseases on a global level. Systems biology integrates the mass of heterogeneous high‐throughput data and predictive computational modeling to understand biological functions as system‐level properties. Most human diseases are biological states caused by multiple components of perturbed pathways and regulatory networks rather than individual failing components. Systems biology not only facilitates basic biological research but also provides new avenues through which to understand human diseases, identify diagnostic biomarkers, and develop disease treatments. At the same time, systems biology seeks to assist in drug discovery, drug optimization, drug combinations, and drug repositioning by investigating the molecular mechanisms of action of drugs at a system's level. Indeed, systems biology is evolving to systems medicine as a new discipline that aims to offer new approaches for addressing the diagnosis and treatment of major human diseases uniquely, effectively, and with personalized precision. WIREs Syst Biol Med 2015, 7:141–161. doi: 10.1002/wsbm.1297 This article is categorized under: Analytical and Computational Methods > Computational Methods Laboratory Methods and Technologies > Macromolecular Interactions, Methods Translational, Genomic, and Systems Medicine > Translational Medicine

High‐throughput data and their hierarchical relationships in describing cellular phenotypes or human diseases. Each type of biological data represents a certain dimension of complex biological systems. Interpretation of cellular phenotypes or human diseases using systems biology approaches requires integration of all heterogeneous high‐throughput data types.

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Basic elements of systems medicine. System medicine is far more than systems biology of human diseases. It comprehensively integrates computational modeling, 'omics data, physiological data, clinical data, and environmental factors to address major human diseases uniquely, efficiently, and with personalized precision.

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An example of network‐based drug repositioning. (a) Drug repositioning by identifying new drug–target interactions (the dotted line). (b) Drug repositioning by identifying new target–disease associations (the dotted line).

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Directionality of disease progression. (Reprinted with permission from Ref . © 2009 Hidalgo et al. (a) Distribution of λ_1→2. (b) Disease precedence Λ_i as a function of disease prevalence P_i. The inset shows the same plot after removing the trend from disease precedence [Λ_i* = Λ_i + 496.08 log₁₀(P_i) − 2446.2]. (c) Disease connectivity calculated from the ϕ‐phenotypic disease network (PDN) as a function of Λ_i*. The yellow line shows the best fit for the 518 diseases with a prevalence larger than 1/500 (yellow circles), while the red line shows the best fit for the 463 diseases at the center of the cloud (red points). The correlation coefficient is represented by r and its associated P‐value by P. (d) Percentage of patients who died 2 and 8 years after being diagnosed with a disease with a given detrended precedence Λ_i*. The yellow lines show the best fit for all the 518 diseases (yellow circles), while the red lines show the fit for the 434 (top panel) and 465 (bottom panel) diseases at the bulk of the cloud.

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Illustration of some concepts of network analysis. (a) Network biomarker. Different from traditional individual biomarkers, a network biomarker is a subnetwork consisting of two or more differentially expressed components in control samples versus disease samples. (b) Differential network analysis examines the same network over two different conditions, highlighting the topological changes induced by diseases. (c) A disease module represents a group of nodes whose perturbation can be linked to a particular disease phenotype. (d) Network alignment compares two networks from different species and aligns orthologous components and their interactions.

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An overview of computational modeling methods used in systems biology. Computational approaches in systems biology apply a wide spectrum of mathematical formalisms across different scales, ranging from data‐driven top‐down methods to model‐driven bottom‐up methods, and from static qualitative models to dynamic quantitative models.

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References

Ahn, AC, Tewari, M, Poon, CS, Phillips, RS. The limits of reductionism in medicine: could systems biology offer an alternative? PLoS Med 2006, 3:e208.
MacLellan, WR, Wang, Y, Lusis, AJ. Systems‐based approaches to cardiovascular disease. Nat Rev Cardiol 2012, 9:172–184.
Brown, PO, Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat Genet 1999, 21:33–37.
Mardis, ER. Next‐generation DNA sequencing methods. Annu Rev Genomics Hum Genet 2008, 9:387–402.
Rolland, T, Ta An, M, Charloteaux, B, Pevzner, SJ, Zhong, Q, Sahni, N, Yi, S, Lemmens, I, Fontanillo, C, Mosca, R, et al. A proteome‐scale map of the human interactome network. Cell 2014, 159:1212–1226.
Wang, Z, Gerstein, M, Snyder, M. RNA‐Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57–63.
Barabasi, AL, Oltvai, ZN. Network biology: understanding the cell`s functional organization. Nat Rev Genet 2004, 5:101–113.
Barabasi, AL, Gulbahce, N, Loscalzo, J. Network medicine: a network‐based approach to human disease. Nat Rev Genet 2011, 12:56–68.
Sobie, EA, Lee, YS, Jenkins, SL, Iyengar, R. Systems biology—biomedical modeling. Sci Signal 2011, 4:tr2.
Loscalzo, J, Kohane, I, Barabasi, AL. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol Syst Biol 2007, 3:124.
Schadt, EE. Molecular networks as sensors and drivers of common human diseases. Nature 2009, 461:218–223.
Ideker, T, Krogan, NJ. Differential network biology. Mol Syst Biol 2012, 8:565.
Wist, AD, Berger, SI, Iyengar, R. Systems pharmacology and genome medicine: a future perspective. Genome Med 2009, 1:11.
Hopkins, AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008, 4:682–690.
Antman, E, Weiss, S, Loscalzo, J. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. Wiley Interdiscip Rev Syst Biol Med 2012, 4:367–383.
Zhou, X, Menche, J, Barabasi, AL, Sharma, A. Human symptoms‐disease network. Nat Commun 2014, 5:4212.
Wolkenhauer, O, Auffray, C, Jaster, R, Steinhoff, G, Dammann, O. The road from systems biology to systems medicine. Pediatr Res 2013, 73:502–507.
Auffray, C, Chen, Z, Hood, L. Systems medicine: the future of medical genomics and healthcare. Genome Med 2009, 1:2.
Rung, J, Brazma, A. Reuse of public genome‐wide gene expression data. Nat Rev Genet 2013, 14:89–99.
Ong, SE, Foster, LJ, Mann, M. Mass spectrometric‐based approaches in quantitative proteomics. Methods 2003, 29:124–130.
Wiese, S, Reidegeld, KA, Meyer, HE, Warscheid, B. Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics 2007, 7:340–350.
Uhlen, M, Oksvold, P, Fagerberg, L, Lundberg, E, Jonasson, K, Forsberg, M, Zwahlen, M, Kampf, C, Wester, K, Hober, S, et al. Towards a knowledge‐based Human Protein Atlas. Nat Biotechnol 2010, 28:1248–1250.
Uhlen, M, Fagerberg, L, Hallstrom, BM, Lindskog, C, Oksvold, P, Mardinoglu, A, Sivertsson, A, Kampf, C, Sjostedt, E, Asplund, A, et al. Tissue‐based map of the human proteome. Science 2015, 347:1260419.
Shah, SH, Kraus, WE, Newgard, CB. Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 2012, 126:1110–1120.
Bendall, SC, Simonds, EF, Qiu, P, Amir el, AD, Krutzik, PO, Finck, R, Bruggner, RV, Melamed, R, Trejo, A, Ornatsky, OI, et al. Single‐cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 2011, 332:687–696.
Benson, DA, Clark, K, Karsch‐Mizrachi, I, Lipman, DJ, Ostell, J, Sayers, EW. GenBank. Nucleic Acids Res 2014, 42:D32–D37.
Kodama, Y, Mashima, J, Kosuge, T, Katayama, T, Fujisawa, T, Kaminuma, E, Ogasawara, O, Okubo, K, Takagi, T, Nakamura, Y. The DDBJ Japanese Genotype‐phenotype Archive for genetic and phenotypic human data. Nucleic Acids Res 2015, 43:D18–D22.
Cunningham, F, Amode, MR, Barrell, D, Beal, K, Billis, K, Brent, S, Carvalho‐Silva, D, Clapham, P, Coates, G, Fitzgerald, S, et al. Ensembl 2015. Nucleic Acids Res 2015, 43:D662–D669.
Edgar, R, Domrachev, M, Lash, AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002, 30:207–210.
Petryszak, R, Burdett, T, Fiorelli, B, Fonseca, NA, Gonzalez‐Porta, M, Hastings, E, Huber, W, Jupp, S, Keays, M, Kryvych, N, et al. Expression Atlas update—a database of gene and transcript expression from microarray‐ and sequencing‐based functional genomics experiments. Nucleic Acids Res 2014, 42:D926–D932.
Li, JH, Liu, S, Zhou, H, Qu, LH, Yang, JH. starBase v2.0: decoding miRNA‐ceRNA, miRNA‐ncRNA and protein‐RNA interaction networks from large‐scale CLIP‐Seq data. Nucleic Acids Res 2014, 42:D92–D97.
Keshava Prasad, TS, Goel, R, Kandasamy, K, Keerthikumar, S, Kumar, S, Mathivanan, S, Telikicherla, D, Raju, R, Shafreen, B, Venugopal, A, et al. Human Protein Reference Database—2009 update. Nucleic Acids Res 2009, 37:D767–D772.
Chatr‐Aryamontri, A, Breitkreutz, BJ, Oughtred, R, Boucher, L, Heinicke, S, Chen, D, Stark, C, Breitkreutz, A, Kolas, N, O`Donnell, L, et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res 2015, 43:D470–D478.
Lee, TI, Rinaldi, NJ, Robert, F, Odom, DT, Bar‐Joseph, Z, Gerber, GK, Hannett, NM, Harbison, CT, Thompson, CM, Simon, I, et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002, 298:799–804.
Landt, SG, Marinov, GK, Kundaje, A, Kheradpour, P, Pauli, F, Batzoglou, S, Bernstein, BE, Bickel, P, Brown, JB, Cayting, P, et al. ChIP‐seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 2012, 22:1813–1831.
Hausser, J, Zavolan, M. Identification and consequences of miRNA‐target interactions—beyond repression of gene expression. Nat Rev Genet 2014, 15:599–612.
Ewing, RM, Chu, P, Elisma, F, Li, H, Taylor, P, Climie, S, McBroom‐Cerajewski, L, Robinson, MD, O`Connor, L, Li, M, et al. Large‐scale mapping of human protein‐protein interactions by mass spectrometry. Mol Syst Biol 2007, 3:89.
Vidal, M, Cusick, ME, Barabasi, AL. Interactome networks and human disease. Cell 2011, 144:986–998.
Petschnigg, J, Groisman, B, Kotlyar, M, Taipale, M, Zheng, Y, Kurat, CF, Sayad, A, Sierra, JR, Mattiazzi Usaj, M, Snider, J, et al. The mammalian‐membrane two‐hybrid assay (MaMTH) for probing membrane‐protein interactions in human cells. Nat Methods 2014, 11:585–592.
Smith, MG, Ptacek, J, Snyder, M. Kinase substrate interactions. Methods Mol Biol 2011, 723:201–212.
Saliba, AE, Vonkova, I, Ceschia, S, Findlay, GM, Maeda, K, Tischer, C, Deghou, S, van Noort, V, Bork, P, Pawson, T, et al. A quantitative liposome microarray to systematically characterize protein‐lipid interactions. Nat Methods 2014, 11:47–50.
Barrios‐Rodiles, M, Brown, KR, Ozdamar, B, Bose, R, Liu, Z, Donovan, RS, Shinjo, F, Liu, Y, Dembowy, J, Taylor, IW, et al. High‐throughput mapping of a dynamic signaling network in mammalian cells. Science 2005, 307:1621–1625.
Boone, C, Bussey, H, Andrews, BJ. Exploring genetic interactions and networks with yeast. Nat Rev Genet 2007, 8:437–449.
Laufer, C, Fischer, B, Huber, W, Boutros, M. Measuring genetic interactions in human cells by RNAi and imaging. Nat Protoc 2014, 9:2341–2353.
McCarthy, MI, Abecasis, GR, Cardon, LR, Goldstein, DB, Little, J, Ioannidis, JP, Hirschhorn, JN. Genome‐wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 2008, 9:356–369.
Kharchenko, PV, Silberstein, L, Scadden, DT. Bayesian approach to single‐cell differential expression analysis. Nat Methods 2014, 11:740–742.
Subramanian, A, Tamayo, P, Mootha, VK, Mukherjee, S, Ebert, BL, Gillette, MA, Paulovich, A, Pomeroy, SL, Golub, TR, Lander, ES, et al. Gene set enrichment analysis: a knowledge‐based approach for interpreting genome‐wide expression profiles. Proc Natl Acad Sci USA 2005, 102:15545–15550.
Hong, S, Chen, X, Jin, L, Xiong, M. Canonical correlation analysis for RNA‐seq co‐expression networks. Nucleic Acids Res 2013, 41:e95.
Eisen, MB, Spellman, PT, Brown, PO, Botstein, D. Cluster analysis and display of genome‐wide expression patterns. Proc Natl Acad Sci USA 1998, 95:14863–14868.
Dewey, FE, Perez, MV, Wheeler, MT, Watt, C, Spin, J, Langfelder, P, Horvath, S, Hannenhalli, S, Cappola, TP, Ashley, EA. Gene coexpression network topology of cardiac development, hypertrophy, and failure. Circ Cardiovasc Genet 2011, 4:26–35.
Jeong, H, Mason, SP, Barabasi, AL, Oltvai, ZN. Lethality and centrality in protein networks. Nature 2001, 411:41–42.
Spirin, V, Mirny, LA. Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci USA 2003, 100:12123–12128.
Chan, SY, Loscalzo, J. The emerging paradigm of network medicine in the study of human disease. Circ Res 2012, 111:359–374.
Sharma, A, Menche, J, Huang, C, Ort, T, Zhou, X, Kitsak, M, Sahni, N, Thibault, D, Voung, L, Guo, F, et al. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in Athsma. Hum Mol Genet 2015. doi: 10.1093/hmg/ddv001.
Wang, RS, Albert, R. Elementary signaling modes predict the essentiality of signal transduction network components. BMC Syst Biol 2011, 5:44.
Wang, RS, Sun, Z, Albert, R. Minimal functional routes in directed graphs with dependent edges. Int Trans Oper Res 2013, 20:391–409.
Sharan, R, Suthram, S, Kelley, RM, Kuhn, T, McCuine, S, Uetz, P, Sittler, T, Karp, RM, Ideker, T. Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA 2005, 102:1974–1979.
McGeachie, MJ, Chang, HH, Weiss, ST. CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data. PLoS Comput Biol 2014, 10:e1003676.
Friedman, N, Linial, M, Nachman, I, Pe`er, D. Using Bayesian networks to analyze expression data. J Comput Biol 2000, 7:601–620.
Chu, JH, Hersh, CP, Castaldi, PJ, Cho, MH, Raby, BA, Laird, N, Bowler, R, Rennard, S, Loscalzo, J, Quackenbush, J, et al. Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD. BMC Syst Biol 2014, 8:78.
Mourad, R, Sinoquet, C, Leray, P. Probabilistic graphical models for genetic association studies. Brief Bioinform 2012, 13:20–33.
Wang, RS, Saadatpour, A, Albert, R. Boolean modeling in systems biology: an overview of methodology and applications. Phys Biol 2012, 9:055001.
Samaga, R, Klamt, S. Modeling approaches for qualitative and semi‐quantitative analysis of cellular signaling networks. Cell Commun Signal 2013, 11:43.
Ryall, KA, Holland, DO, Delaney, KA, Kraeutler, MJ, Parker, AJ, Saucerman, JJ. Network reconstruction and systems analysis of cardiac myocyte hypertrophy signaling. J Biol Chem 2012, 287:42259–42268.
Wittmann, DM, Krumsiek, J, Saez‐Rodriguez, J, Lauffenburger, DA, Klamt, S, Theis, FJ. Transforming Boolean models to continuous models: methodology and application to T‐cell receptor signaling. BMC Syst Biol 2009, 3:98.
Wang, RS, Oldham, WM, Loscalzo, J. Network‐based association of hypoxia‐responsive genes with cardiovascular diseases. New J Phys 2014, 16:105014.
Rzhetsky, A, Wajngurt, D, Park, N, Zheng, T. Probing genetic overlap among complex human phenotypes. Proc Natl Acad Sci USA 2007, 104:11694–11699.
Khor, B, Gardet, A, Xavier, RJ. Genetics and pathogenesis of inflammatory bowel disease. Nature 2011, 474:307–317.
Jostins, L, Ripke, S, Weersma, RK, Duerr, RH, McGovern, DP, Hui, KY, Lee, JC, Schumm, LP, Sharma, Y, Anderson, CA, et al. Host‐microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 2012, 491:119–124.
Hidalgo, CA, Blumm, N, Barabasi, AL, Christakis, NA. A dynamic network approach for the study of human phenotypes. PLoS Comput Biol 2009, 5:e1000353.
Menche, J, Sharma, A, Kitsak, M, Ghiassian, SD, Vidal, M, Loscalzo, J, Barabasi, AL. Disease networks. Uncovering disease‐disease relationships through the incomplete interactome. Science 2015, 347:1257601.
Zucco, L, Zhang, Q, Kuliszewski, MA, Kandic, I, Faughnan, ME, Stewart, DJ, Kutryk, MJ. Circulating angiogenic cell dysfunction in patients with hereditary hemorrhagic telangiectasia. PLoS One 2014, 9:e89927.
Xu, G, Barrios‐Rodiles, M, Jerkic, M, Turinsky, AL, Nadon, R, Vera, S, Voulgaraki, D, Wrana, JL, Toporsian, M, Letarte, M. Novel protein interactions with endoglin and activin receptor‐like kinase 1: potential role in vascular networks. Mol Cell Proteomics 2014, 13:489–502.
Sharma, A, Gulbahce, N, Pevzner, SJ, Menche, J, Ladenvall, C, Folkersen, L, Eriksson, P, Orho‐Melander, M, Barabasi, AL. Network‐based analysis of genome wide association data provides novel candidate genes for lipid and lipoprotein traits. Mol Cell Proteomics 2013, 12:3398–3408.
Velez, P, Parguina, AF, Ocaranza‐Sanchez, R, Grigorian‐Shamagian, L, Rosa, I, Alonso‐Orgaz, S, de la Cuesta, F, Guitian, E, Moreu, J, Barderas, MG, et al. Identification of a circulating microvesicle protein network involved in ST‐elevation myocardial infarction. Thromb Haemost 2014, 112:716–726.
Faner, R, Cruz, T, Lopez‐Giraldo, A, Agusti, A. Network medicine, multimorbidity and the lung in the elderly. Eur Respir J 2014, 44:775–788.
Menche, J, Sharma, A, Cho, MH, Mayer, RJ, Rennard, SI, Celli, B, Miller, BE, Locantore, N, Tal‐Singer, R, Ghosh, S, et al. A diVIsive Shuffling Approach (VIStA) for gene expression analysis to identify subtypes in chronic obstructive pulmonary disease. BMC Syst Biol 2014, 8(Suppl 2):S8.
Davidsen, PK, Herbert, JM, Antczak, P, Clarke, K, Ferrer, E, Peinado, VI, Gonzalez, C, Roca, J, Egginton, S, Barbera, JA, et al. A systems biology approach reveals a link between systemic cytokines and skeletal muscle energy metabolism in a rodent smoking model and human COPD. Genome Med 2014, 6:59.
Najafi, A, Masoudi‐Nejad, A, Ghanei, M, Nourani, MR, Moeini, A. Pathway reconstruction of airway remodeling in chronic lung diseases: a systems biology approach. PLoS One 2014, 9:e100094.
Zhao, Y, Peng, J, Lu, C, Hsin, M, Mura, M, Wu, L, Chu, L, Zamel, R, Machuca, T, Waddell, T, et al. Metabolomic heterogeneity of pulmonary arterial hypertension. PLoS One 2014, 9:e88727.
Maron, BA, Oldham, WM, Chan, SY, Vargas, SO, Arons, E, Zhang, YY, Loscalzo, J, Leopold, JA. Upregulation of steroidogenic acute regulatory protein by hypoxia stimulates aldosterone synthesis in pulmonary artery endothelial cells to promote pulmonary vascular fibrosis. Circulation 2014, 130:168–179.
Malaney, P, Pathak, RR, Xue, B, Uversky, VN, Dave, V. Intrinsic disorder in PTEN and its interactome confers structural plasticity and functional versatility. Sci Rep 2013, 3:2035.
Gamez‐Pozo, A, Perez Carrion, RM, Manso, L, Crespo, C, Mendiola, C, Lopez‐Vacas, R, Berges‐Soria, J, Lopez, IA, Margeli, M, Calero, JL, et al. The Long‐HER study: clinical and molecular analysis of patients with HER2+ advanced breast cancer who become long‐term survivors with trastuzumab‐based therapy. PLoS One 2014, 9:e109611.
Wegdam, W, Argmann, CA, Kramer, G, Vissers, JP, Buist, MR, Kenter, GG, Aerts, JM, Meijer, D, Moerland, PD. Label‐free LC‐MSe in tissue and serum reveals protein networks underlying differences between benign and malignant serous ovarian tumors. PLoS One 2014, 9:e108046.
Chu, LH, Lee, E, Bader, JS, Popel, AS. Angiogenesis interactome and time course microarray data reveal the distinct activation patterns in endothelial cells. PLoS One 2014, 9:e110871.
Finley, SD, Chu, LH, Popel, AS. Computational systems biology approaches to anti‐angiogenic cancer therapeutics. Drug Discov Today 2015, 20:187–197.
Vempati, P, Mac Gabhann, F, Popel, AS. Quantifying the proteolytic release of extracellular matrix‐sequestered VEGF with a computational model. PLoS One 2010, 5:e11860.
Li, P, Liu, Y, Wang, H, He, Y, Wang, X, Lv, F, Chen, H, Pang, X, Liu, M, Shi, T, et al. PubAngioGen: a database and knowledge for angiogenesis and related diseases. Nucleic Acids Res 2015, 43:D963–D967.
Karagiannis, GS, Saraon, P, Jarvi, KA, Diamandis, EP. Proteomic signatures of angiogenesis in androgen‐independent prostate cancer. Prostate 2014, 74:260–272.
Yoon, DY, Buchler, P, Saarikoski, ST, Hines, OJ, Reber, HA, Hankinson, O. Identification of genes differentially induced by hypoxia in pancreatic cancer cells. Biochem Biophys Res Commun 2001, 288:882–886.
Huang, H, Vangay, P, McKinlay, CE, Knights, D. Multi‐omics analysis of inflammatory bowel disease. Immunol Lett 2014, 162:62–68.
Erickson, AR, Cantarel, BL, Lamendella, R, Darzi, Y, Mongodin, EF, Pan, C, Shah, M, Halfvarson, J, Tysk, C, Henrissat, B, et al. Integrated metagenomics/metaproteomics reveals human host‐microbiota signatures of Crohn`s disease. PLoS One 2012, 7:e49138.
Gevers, D, Kugathasan, S, Denson, LA, Vazquez‐Baeza, Y, Van Treuren, W, Ren, B, Schwager, E, Knights, D, Song, SJ, Yassour, M, et al. The treatment‐naive microbiome in new‐onset Crohn`s disease. Cell Host Microbe 2014, 15:382–392.
Tuller, T, Atar, S, Ruppin, E, Gurevich, M, Achiron, A. Common and specific signatures of gene expression and protein‐protein interactions in autoimmune diseases. Genes Immun 2013, 14:67–82.
Seeley, EH, Washington, MK, Caprioli, RM, M`Koma, AE. Proteomic patterns of colonic mucosal tissues delineate Crohn`s colitis and ulcerative colitis. Proteomics Clin Appl 2013, 7:541–549.
M`Koma, AE, Seeley, EH, Washington, MK, Schwartz, DA, Muldoon, RL, Herline, AJ, Wise, PE, Caprioli, RM. Proteomic profiling of mucosal and submucosal colonic tissues yields protein signatures that differentiate the inflammatory colitides. Inflamm Bowel Dis 2011, 17:875–883.
Fitzgerald, JB, Schoeberl, B, Nielsen, UB, Sorger, PK. Systems biology and combination therapy in the quest for clinical efficacy. Nat Chem Biol 2006, 2:458–466.
Sun, X, Vilar, S, Tatonetti, NP. High‐throughput methods for combinatorial drug discovery. Sci Transl Med 2013, 5:205rv1.
Bansal, M, Yang, J, Karan, C, Menden, MP, Costello, JC, Tang, H, Xiao, G, Li, Y, Allen, J, Zhong, R, et al. A community computational challenge to predict the activity of pairs of compounds. Nat Biotechnol 2014, 32:1213–1222.
Zhao, J, Zhang, XS, Zhang, S. Predicting cooperative drug effects through the quantitative cellular profiling of response to individual drugs. CPT Pharmacometrics Syst Pharmacol 2014, 3:e102.
Jin, G, Zhao, H, Zhou, X, Wong, ST. An enhanced Petri‐net model to predict synergistic effects of pairwise drug combinations from gene microarray data. Bioinformatics 2011, 27:i310–i316.
Zhao, S, Nishimura, T, Chen, Y, Azeloglu, EU, Gottesman, O, Giannarelli, C, Zafar, MU, Benard, L, Badimon, JJ, Hajjar, RJ, et al. Systems pharmacology of adverse event mitigation by drug combinations. Sci Transl Med 2013, 5:206ra140.
Lamb, J, Crawford, ED, Peck, D, Modell, JW, Blat, IC, Wrobel, MJ, Lerner, J, Brunet, JP, Subramanian, A, Ross, KN, et al. The Connectivity Map: using gene‐expression signatures to connect small molecules, genes, and disease. Science 2006, 313:1929–1935.
Iorio, F, Bosotti, R, Scacheri, E, Belcastro, V, Mithbaokar, P, Ferriero, R, Murino, L, Tagliaferri, R, Brunetti‐Pierri, N, Isacchi, A, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA 2010, 107:14621–14626.
Iskar, M, Zeller, G, Blattmann, P, Campillos, M, Kuhn, M, Kaminska, KH, Runz, H, Gavin, AC, Pepperkok, R, van Noort, V, et al. Characterization of drug‐induced transcriptional modules: towards drug repositioning and functional understanding. Mol Syst Biol 2013, 9:662.
Farkas, IJ, Korcsmaros, T, Kovacs, IA, Mihalik, A, Palotai, R, Simko, GI, Szalay, KZ, Szalay‐Beko, M, Vellai, T, Wang, S, et al. Network‐based tools for the identification of novel drug targets. Sci Signal 2011, 4:pt3.
Keiser, MJ, Setola, V, Irwin, JJ, Laggner, C, Abbas, AI, Hufeisen, SJ, Jensen, NH, Kuijer, MB, Matos, RC, Tran, TB, et al. Predicting new molecular targets for known drugs. Nature 2009, 462:175–181.
Hwang, WC, Zhang, A, Ramanathan, M. Identification of information flow‐modulating drug targets: a novel bridging paradigm for drug discovery. Clin Pharmacol Ther 2008, 84:563–572.
Zhu, M, Zhang, H, Humphreys, WG. Drug metabolite profiling and identification by high‐resolution mass spectrometry. J Biol Chem 2011, 286:25419–25425.
Cami, A, Arnold, A, Manzi, S, Reis, B. Predicting adverse drug events using pharmacological network models. Sci Transl Med 2011, 3:114ra127.
Tatonetti, NP, Ye, PP, Daneshjou, R, Altman, RB. Data‐driven prediction of drug effects and interactions. Sci Transl Med 2012, 4:125ra131.
Weinstein, JN, Collisson, EA, Mills, GB, Shaw, KR, Ozenberger, BA, Ellrott, K, Shmulevich, I, Sander, C, Stuart, JM. The Cancer Genome Atlas Pan‐Cancer analysis project. Nat Genet 2013, 45:1113–1120.
Chen, R, Mias, GI, Li‐Pook‐Than, J, Jiang, L, Lam, HY, Miriami, E, Karczewski, KJ, Hariharan, M, Dewey, FE, Cheng, Y, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012, 148:1293–1307.
Roden, DM, Xu, H, Denny, JC, Wilke, RA. Electronic medical records as a tool in clinical pharmacology: opportunities and challenges. Clin Pharmacol Ther 2012, 91:1083–1086.
Bousquet, J, Jorgensen, C, Dauzat, M, Cesario, A, Camuzat, T, Bourret, R, Best, N, Anto, JM, Abecassis, F, Aubas, P, et al. Systems medicine approaches for the definition of complex phenotypes in chronic diseases and ageing. From concept to implementation and policies. Curr Pharm Des 2014, 20:5928–5944.
Hood, L, Friend, SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol 2011, 8:184–187.

Further Reading

Emmert‐Streib, F, Dehmer, M. Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too. Front Genet 2013, 4:241.
Gustafsson, M, Nestor, CE, Zhang, H, Barabási, AL, Baranzini, S, Brunak, S, Chung, KF, Federoff, HJ, Gavin, AC, Meehan, RR, et al. Modules, networks and systems medicine for understanding disease and aiding diagnosis. Genome Med 2014, 6:82.
Ahn, AC, Tewari, M, Poon, C‐S, Phillips, RS. The clinical applications of a systems approach. PLoS Med 2006, 3:e209.

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