Ion sequencing and DNA variation arrays facilitate the generation of personalized patient data, such as individual human genomes and individual SNP profiles, which offers unprecedented opportunities for personalized medicine. By integrating various `omics data sets and GWAS loci/eQTLs, personalized medicine is making promising progress. For example, the Cancer Genome Atlas (TCGA) research team has used the Mangafodipir (trisodium) dose latest sequencing technologies and sophisticated bioinformatic analytical methods to identify somatic variants in the genomes of thousands of tumor samples from at least 20 tumor types 104. Chen and colleagues presented an integrative personal `omics profile that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14-month period 105. This longitudinal analysis revealed various medical risks and individual disease states, including type II diabetes, rhinovirus, and respiratory syncytial virus infections, demonstrating the possibility of predictive and preventive medicine enabled by systems biology and whole genome sequencing.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptEVOLUTION OF SYSTEMS BIOLOGY INTO SYSTEMS MEDICINEBuilding on the successes of systems biology, systems medicine is defined as an emerging discipline that more comprehensively integrates computational modeling, `omics data, physiological data, clinical data, and environmental BQ-123 web factors to model disease expression predictively 17, 18. Systems medicine integrates basic research and clinical practice, and emphasizes translational and clinical research. As shown in Figure 6, the basic elements of systems medicine include systems-based approaches to human diseases and pharmacology and exploration of personalized patient clinical data space, including physiological data and environmental data. In addition to systems biology and network-based drug discovery, new high-dimensional patient data are another factor driving the emergence of systems medicine. Parallel to an explosion in the number of high-throughput molecular and cellular data sets, the digital revolution has induced a massive accumulation of electronic heath data that capture thousands of clinical measurements collected in medical practice 106. This valuable resource of longitudinal patient records has been overlooked by systems biologists in investigating human diseases due to tight privacy-based regulation of medical data. However, if made publically available, this data resource will provide rich information about individual disease states after integration with molecular data. In addition, each patient is different and needs personalization of medical treatment. To this end, environmental factors, such as diet, gender, age, and family histories, and physiological factors, such as tissues, organs, or whole body must be considered in a clinical practice context for systems medicine. This means that physicians must deal with large-scale non-linear, multidimensional data. Integration of such heterogeneous clinical data requires more sophisticated computational modeling strategies, but will make personalized medicine and personalized healthcare highly possible.Wiley Interdiscip Rev Syst Biol Med. Author manuscript; available in PMC 2016 July 01.Wang et al.PageSystems medicine is highly comprehensive and integrative, and utilizes all types of nonlinear information. Different from the collaborations in systems biology that focus on.Ion sequencing and DNA variation arrays facilitate the generation of personalized patient data, such as individual human genomes and individual SNP profiles, which offers unprecedented opportunities for personalized medicine. By integrating various `omics data sets and GWAS loci/eQTLs, personalized medicine is making promising progress. For example, the Cancer Genome Atlas (TCGA) research team has used the latest sequencing technologies and sophisticated bioinformatic analytical methods to identify somatic variants in the genomes of thousands of tumor samples from at least 20 tumor types 104. Chen and colleagues presented an integrative personal `omics profile that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14-month period 105. This longitudinal analysis revealed various medical risks and individual disease states, including type II diabetes, rhinovirus, and respiratory syncytial virus infections, demonstrating the possibility of predictive and preventive medicine enabled by systems biology and whole genome sequencing.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptEVOLUTION OF SYSTEMS BIOLOGY INTO SYSTEMS MEDICINEBuilding on the successes of systems biology, systems medicine is defined as an emerging discipline that more comprehensively integrates computational modeling, `omics data, physiological data, clinical data, and environmental factors to model disease expression predictively 17, 18. Systems medicine integrates basic research and clinical practice, and emphasizes translational and clinical research. As shown in Figure 6, the basic elements of systems medicine include systems-based approaches to human diseases and pharmacology and exploration of personalized patient clinical data space, including physiological data and environmental data. In addition to systems biology and network-based drug discovery, new high-dimensional patient data are another factor driving the emergence of systems medicine. Parallel to an explosion in the number of high-throughput molecular and cellular data sets, the digital revolution has induced a massive accumulation of electronic heath data that capture thousands of clinical measurements collected in medical practice 106. This valuable resource of longitudinal patient records has been overlooked by systems biologists in investigating human diseases due to tight privacy-based regulation of medical data. However, if made publically available, this data resource will provide rich information about individual disease states after integration with molecular data. In addition, each patient is different and needs personalization of medical treatment. To this end, environmental factors, such as diet, gender, age, and family histories, and physiological factors, such as tissues, organs, or whole body must be considered in a clinical practice context for systems medicine. This means that physicians must deal with large-scale non-linear, multidimensional data. Integration of such heterogeneous clinical data requires more sophisticated computational modeling strategies, but will make personalized medicine and personalized healthcare highly possible.Wiley Interdiscip Rev Syst Biol Med. Author manuscript; available in PMC 2016 July 01.Wang et al.PageSystems medicine is highly comprehensive and integrative, and utilizes all types of nonlinear information. Different from the collaborations in systems biology that focus on.
