DOI: 10.1126/science.1104635 , 640 (2004); 306Science et al.Leroy Hood,Predictive and Preventative MedicineSystems Biology and New Technologies Enable www.sciencemag.org (this information is current as of February 11, 2008 ):The following resources related to this article are available online at http://www.sciencemag.org/cgi/content/full/306/5696/640version of this article at: including high-resolution figures, can be found in the onlineUpdated information and services,found at: can berelated to this articleA list of selected additional articles on the Science Web sites http://www.sciencemag.org/cgi/content/full/306/5696/640#related-content http://www.sciencemag.org/cgi/content/full/306/5696/640#otherarticles, 23 of which can be accessed for free: cites 45 articlesThis article 129 article(s) on the ISI Web of Science. cited byThis article has been http://www.sciencemag.org/cgi/content/full/306/5696/640#otherarticles 24 articles hosted by HighWire Press; see: cited byThis article has been http://www.sciencemag.org/cgi/collection/medicineMedicine, Diseases : subject collectionsThis article appears in the following http://www.sciencemag.org/about/permissions.dtl in whole or in part can be found at: this articlepermission to reproduce of this article or about obtaining reprintsInformation about obtaining registered trademark of AAAS. is aScience2004 by the American Association for the Advancement of Science; all rights reserved. The title CopyrightAmerican Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by theScience on February 11, 2008 www.sciencemag.orgDownloaded from15. K. Shuai, C. Schindler, V. R. Prezioso, J. E. Darnell Jr.,Science 258, 1808 (1992).16. R. W. Blakesley et al., Genome Res. 14, 2235 (2004).17. K. D. Pruitt, T. Tatusova, D. R. Maglott, Nucleic AcidsRes. 31, 34 (2003).18. International HapMap Consortium, Nat. Rev. Genet.5, 467 (2004).19. W. J. Kent et al., Genome Res. 12, 996 (2002).20. P. T. Spellman et al., Genome Biol 3, RESEARCH0046(2002).21. N. D. Trinklein et al., Genome Res. 14, 62 (2004).22. B. Ren, B. D. Dynlacht, Methods Enzymol. 376, 304(2004).23. ENCODE Consortium, unpublished data.24. Mammalian Gene Collection (MGC) Project Team,Genome Res.14, 2121 (2004).25. International HapMap Consortium, Nature 426, 789(2003).26. A. Felsenfeld, J. Peterson, J. Schloss, M. Guyer,Genome Res. 9, 1 (1999).27. A. Siepel, D. Haussler, in Statistical Methods inMolecular Evolution,R.Nielsen,Ed.(Springer,NewYork, in press).28. M. Blanchette et al., Genome Res. 14, 708 (2004).29. The Consortium thanks the ENCODE Scientific Ad-visory Panel for their helpful advice on the project:G. Weinstock, G. Churchill, M. Eisen, S. Elgin, S. Elledge,J. Rine, and M. Vidal. We thank D. Leja, and M.Cichanowski for their work in creating figures for thispaper. Supported by the National Human GenomeResearch Institute, the National Library of Medicine,the Wellcome Trust, and the Howard Hughes MedicalInstitute.Supporting Online Materialwww.sciencemag.org/cgi/content/full/306/5696/636/DC2Tables S1 to S3VIEWPOINTSystems Biology and New Technologies EnablePredictive and Preventative MedicineLeroy Hood,1*James R. Heath,2,3Michael E. Phelps,3Biaoyang Lin1Systems approaches to disease are grounded in the idea that disease-perturbedprotein and gene regulatory networks differ from their normal counterparts; we havebeen pursuing the possibility that these differences may be reflected by multi-parameter measurements of the blood. Such concepts are transforming currentdiagnostic and therapeutic approaches to medicine and, together with new tech-nologies, will enable a predictive and preventive medicine that will lead to per-sonalized medicine.Biological information is divided into the dig-ital information of the genome and the envi-ronmental cues that arise outside the genome.Integration of these types of information leadsto the dynamic execution of instructions as-sociated with the development of organismsand their physiological responses to their en-vironments. The digital information of thegenome is ultimately completely knowable,implying that biology is unique among thesciences, in that biologists start their questfor understanding systems with a knowablecore of information. Systems biology is a sci-entific discipline that endeavors to quantifyall of the molecular elements of a biologicalsystem to assess their interactions and to in-tegrate that information into graphical net-work models (1–4) that serve as predictivehypotheses to explain emergent behaviors.The genome encodes two major types ofinformation: (i) genes whose proteins exe-cute the functions of life and (ii) cis controlelements. Proteins may function alone, incomplexes, or in networks that arise fromprotein interactions or from proteins thatare interconnected functionally through smallmolecules (such as signal transduction ormetabolic networks). The cis control ele-ments, together with transcription factors,regulate the levels of expression of individualgenes. They also form the linkages and ar-chitectures of the gene regulatory networksthat integrate dynamically changing inputsfrom signal transduction pathways and pro-vide dynamically changing outputs to thebatteries of genes mediating physiologicaland developmental responses (5, 6). Thehypothesis that is beginning to revolutionizemedicine is that disease may perturb the nor-mal network structures of a system throughgenetic perturbations and/or by pathologicalenvironmental cues, such as infectious agentsor chemical carcinogens.Systems Approaches to Model Systemsand Implications for DiseaseA model of a metabolic process (galactoseutilization) in yeast was developed from exist-ing literature data to formulate a network hy-pothesis that was tested and refined through aseries of genetic knockouts and environmentalperturbations (7). Messenger RNA (mRNA)concentrations were monitored for all 6000genes in the genome, and these data wereintegrated with protein/protein and protein/DNA interaction data from the literature by agraphical network program (Fig. 1).The model provided new insights into thecontrol of a metabolic process and its in-teractions with other cellular processes. Italso suggested several concepts for systemsapproaches to human disease. Each genet-ic knockout strain had a distinct pattern ofperturbed gene expression, with