Systems Approaches to Biology and Disease Enable Translational Systems Medicine

The development and application of systems strategies to biology and disease are transforming medical research and clinical practice in an unprecedented rate. In the foreseeable future, clinicians, medical researchers, and ultimately the consumers and patients will be increasingly equipped with a deluge of personal health information, e.g., whole genome sequences, molecular profiling of diseased tissues, and periodic multi-analyte blood testing of biomarker panels for disease and wellness. The convergence of these practices will enable accurate prediction of disease susceptibility and early diagnosis for actionable preventive schema and personalized treatment regimes tailored to each individual. It will also entail proactive participation from all major stakeholders in the health care system. We are at the dawn of predictive, preventive, personalized, and participatory (P4) medicine, the fully implementation of which requires marrying basic and clinical researches through advanced systems thinking and the employment of high-throughput technologies in genomics, proteomics, nanofluidics, single-cell analysis, and computation strategies in a highly-orchestrated discipline we termed translational systems medicine.

[1]  Jared C. Roach,et al.  Chromosomal haplotypes by genetic phasing of human families. , 2011, American journal of human genetics.

[2]  Ying Wang,et al.  A distinct lineage of CD4 T cells regulates tissue inflammation by producing interleukin 17 , 2005, Nature Immunology.

[3]  Ruedi Aebersold,et al.  Proteomic analysis identifies that 14-3-3ζ interacts with β-catenin and facilitates its activation by Akt , 2004 .

[4]  Rong Fan,et al.  A Clinical Microchip for Evaluation of Single Immune Cells Reveals High Functional Heterogeneity in Phenotypically Similar T Cells Nih Public Access Author Manuscript Design Rationale and Detection Limit of the Scbc Online Methods Microchip Fabrication On-chip Secretion Profiling Supplementary Mater , 2022 .

[5]  Ruedi Aebersold,et al.  Proteomic analysis identifies that 14-3-3zeta interacts with beta-catenin and facilitates its activation by Akt. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Pradeep S Rajendran,et al.  Single-cell dissection of transcriptional heterogeneity in human colon tumors , 2011, Nature Biotechnology.

[7]  Jessica C. Ebert,et al.  Accurate whole genome sequencing and haplotyping from10-20 human cells , 2012, Nature.

[8]  Hua Zhao,et al.  Elevated circulating endothelial progenitor marker CD133 messenger RNA levels predict colon cancer recurrence , 2007, Cancer.

[9]  Jennifer L. Osborn,et al.  Direct multiplexed measurement of gene expression with color-coded probe pairs , 2008, Nature Biotechnology.

[10]  Inyoul Y. Lee,et al.  A systems approach to prion disease , 2009, Molecular systems biology.

[11]  J. Troge,et al.  Tumour evolution inferred by single-cell sequencing , 2011, Nature.

[12]  L. Hood,et al.  Integrated barcode chips for rapid, multiplexed analysis of proteins in microliter quantities of blood , 2008, Nature Biotechnology.

[13]  P. Shannon,et al.  Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing , 2010, Science.

[14]  Gilbert S Omenn,et al.  SRM targeted proteomics in search for biomarkers of HCV‐induced progression of fibrosis to cirrhosis in HALT‐C patients , 2012, Proteomics.

[15]  Amy K. Schmid,et al.  A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell , 2007, Cell.

[16]  Mitsuru Sasako,et al.  Clinical significance of circulating tumor cells, including cancer stem-like cells, in peripheral blood for recurrence and prognosis in patients with Dukes' stage B and C colorectal cancer. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  L. Hood,et al.  Dysregulated gene expression networks in human acute myelogenous leukemia stem cells , 2009, Proceedings of the National Academy of Sciences.

[18]  Roger E Bumgarner,et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. , 2001, Science.

[19]  Lukas N. Mueller,et al.  Full Dynamic Range Proteome Analysis of S. cerevisiae by Targeted Proteomics , 2009, Cell.

[20]  V. Thorsson,et al.  Integrated Genomic and Proteomic Analyses of Gene Expression in Mammalian Cells*S , 2004, Molecular & Cellular Proteomics.

[21]  Nathan D Price,et al.  A CD133-related gene expression signature identifies an aggressive glioblastoma subtype with excessive mutations , 2011, Proceedings of the National Academy of Sciences.