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Translational Bioinformatics Approaches for Systems and Dynamical Medicine

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Book cover Pharmacogenomics in Drug Discovery and Development

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1175))

Abstract

The exponential growth of experimental and clinical data generated from systematic studies, the complexity in health and diseases, and the request for the establishment of systems models are bringing bioinformatics to the center stage of pharmacogenomics and systems biology. Bioinformatics plays an essential role in bridging the gap among different knowledge domains for the translation of the voluminous data into better diagnosis, prognosis, prevention, and treatment. Bioinformatics is essential in finding the spatiotemporal patterns in pharmacogenomics, including the time-series analyses of the associations between genetic structural variations and functional alterations such as drug responses. The elucidation of the cross talks among different systems levels and time scales can contribute to the discovery of accurate and robust biomarkers at various diseases stages for the development of systems and dynamical medicine. Various resources are available for such purposes, including databases and tools supporting “omics” studies such as genomics, proteomics, epigenomics, transcriptomics, metabolomics, lipidomics, pharmacogenomics, and chronomics. The combination of bioinformatics and health informatics methods would provide powerful decision support in both scientific and clinical environments. Data integration, data mining, and knowledge discovery (KD) methods would enable the simulation of complex systems and dynamical networks to establish predictive models for achieving predictive, preventive, and personalized medicine.

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References

  1. Yan Q (2010) Translational bioinformatics and systems biology approaches for personalized medicine. Methods Mol Biol 662:167–178

    Article  CAS  PubMed  Google Scholar 

  2. Yan Q (2012) Translational bioinformatics in psychoneuroimmunology: methods and applications. Methods Mol Biol 934:383–400

    Article  PubMed  Google Scholar 

  3. Suh KS, Remache YK, Patel JS et al (2009) Informatics-guided procurement of patient samples for biomarker discovery projects in cancer research. Cell Tissue Bank 10:43–48

    Article  PubMed  Google Scholar 

  4. Halberg F, Cornélissen G, Katinas G et al (2007) Chronomics and Genetics. Scr Med (Brno) 80:133–150

    Google Scholar 

  5. Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  CAS  PubMed  Google Scholar 

  6. Sigrist CJA, Cerutti L, de Castro E et al (2010) PROSITE, a protein domain database for functional characterization and annotation. Nucleic Acids Res 38:D161–D166

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Larkin MA, Blackshields G, Brown NP et al (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23:2947–2948

    Article  CAS  PubMed  Google Scholar 

  8. Rost B, Yachdav G, Liu J (2004) The PredictProtein server. Nucleic Acids Res 32:W321–W326

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Rose PW, Beran B, Bi C, Bluhm WF et al (2011) The RCSB protein data bank: redesigned web site and web services. Nucleic Acids Res 39:D392–D401

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Sherry ST, Ward MH, Kholodov M et al (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res 29:308–311

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Manolio TA, Brooks LD, Collins FS (2008) A HapMap harvest of insights into the genetics of common disease. J Clin Invest 118:1590–1605

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Bandla S, Pennathur A, Luketich JD et al (2012) Comparative genomics of esophageal adenocarcinoma and squamous cell carcinoma. Ann Thorac Surg 93:1101–1106

    Article  PubMed Central  PubMed  Google Scholar 

  13. Kanehisa M, Goto S, Hattori M et al (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34:D354–D357

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Croft D, O’Kelly G, Wu G et al (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39:D691–D697

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Keshava Prasad TS, Goel R, Kandasamy K et al (2009) Human protein reference database—2009 update. Nucleic Acids Res 37:D767–D772

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Barrett T, Edgar R (2006) Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol 411:352–369

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Krupp M, Marquardt JU, Sahin U et al (2012) RNA-Seq atlas—a reference database for gene expression profiling in normal tissue by next-generation sequencing. Bioinformatics 28:1184–1185

    Article  CAS  PubMed  Google Scholar 

  18. Uhlen M, Oksvold P, Fagerberg L et al (2010) Towards a knowledge-based human protein atlas. Nat Biotechnol 28:1248–1250

    Article  CAS  PubMed  Google Scholar 

  19. Wishart DS, Knox C, Guo AC et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–D610

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Fahy E, Sud M, Cotter D, Subramaniam S (2007) LIPID MAPS online tools for lipid research. Nucleic Acids Res 35:W606–W612

    Article  PubMed Central  PubMed  Google Scholar 

  21. Baek S-J, Yang S, Kang T-W et al (2013) MENT: methylation and expression database of normal and tumor tissues. Gene 518:194–200

    Article  CAS  PubMed  Google Scholar 

  22. Xin Y, Chanrion B, O’Donnell AH et al (2012) MethylomeDB: a database of DNA methylation profiles of the brain. Nucleic Acids Res 40:D1245–D1249

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Kuo H-C, Lin P-Y, Chung T-C et al (2011) DBCAT: database of CpG islands and analytical tools for identifying comprehensive methylation profiles in cancer cells. J Comput Biol 18:1013–1017

    Article  CAS  PubMed  Google Scholar 

  24. Gu F, Doderer MS, Huang Y-W et al (2013) CMS: a web-based system for visualization and analysis of genome-wide methylation data of human cancers. PLoS One 8:e60980

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Hackenberg M, Barturen G, Oliver JL (2011) NGSmethDB: a database for next-generation sequencing single-cytosine-resolution DNA methylation data. Nucleic Acids Res 39:D75–D79

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Cho SY, Chai JC, Park SJ et al (2013) EPITRANS: a database that integrates epigenome and transcriptome data. Mol Cells 36:472–475

    Article  CAS  PubMed  Google Scholar 

  27. Halachev K, Bast H, Albrecht F et al (2012) EpiExplorer: live exploration and global analysis of large epigenomic datasets. Genome Biol 13:R96

    Article  PubMed Central  PubMed  Google Scholar 

  28. Pescador N, Pérez-Barba M, Ibarra JM et al (2013) Serum circulating microRNA profiling for identification of potential type 2 diabetes and obesity biomarkers. PLoS One 8:e77251

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Sandhu H, Maddock H (2014) Molecular basis of cancer-therapy-induced cardiotoxicity: introducing microRNA biomarkers for early assessment of subclinical myocardial injury. Clin Sci 126:377–400

    Article  CAS  PubMed  Google Scholar 

  30. Kozomara A, Griffiths-Jones S (2013) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73

    Article  PubMed Central  PubMed  Google Scholar 

  31. Betel D, Wilson M, Gabow A et al (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res 36:D149–D153

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Hsu S-D, Chu C-H, Tsou A-P et al (2008) miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes. Nucleic Acids Res 36:D165–D169

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Hamosh A, Scott AF, Amberger JS et al (2005) Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33:D514–D517

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. George RA, Liu JY, Feng LL et al (2006) Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res 34:e130

    Article  PubMed Central  PubMed  Google Scholar 

  35. Kuchta K, Barszcz D, Grzesiuk E et al (2012) DNAtraffic—a new database for systems biology of DNA dynamics during the cell life. Nucleic Acids Res 40:D1235–D1240

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Kim D-N, Altschuler J, Strong C et al (2011) Conformational dynamics data bank: a database for conformational dynamics of proteins and supramolecular protein assemblies. Nucleic Acids Res 39:D451–D455

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Van der Kamp MW, Schaeffer RD, Jonsson AL et al (2010) Dynameomics: a comprehensive database of protein dynamics. Structure 18:423–435

    Article  PubMed Central  PubMed  Google Scholar 

  38. Frenkel-Morgenstern M, Cohen AA, Geva-Zatorsky N et al (2010) Dynamic proteomics: a database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells. Nucleic Acids Res 38:D508–D512

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Jo S, Kim T, Im W (2007) Automated builder and database of protein/membrane complexes for molecular dynamics simulations. PLoS One 2:e880

    Article  PubMed Central  PubMed  Google Scholar 

  40. Pizarro A, Hayer K, Lahens NF, Hogenesch JB (2013) CircaDB: a database of mammalian circadian gene expression profiles. Nucleic Acids Res 41:D1009–D1013

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Sunkin SM, Ng L, Lau C et al (2013) Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res 41:D996–D1008

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Sato A, Sekine Y, Saruta C et al (2008) Cerebellar development transcriptome database (CDT-DB): profiling of spatio-temporal gene expression during the postnatal development of mouse cerebellum. Neural Netw 21:1056–1069

    Article  PubMed  Google Scholar 

  43. Belmamoune M, Potikanond D, Verbeek FJ (2010) Mining and analysing spatio-temporal patterns of gene expression in an integrative database framework. J Integr Bioinform 7:128

    Google Scholar 

  44. Diez-Roux G, Banfi S, Sultan M et al (2011) A high-resolution anatomical atlas of the transcriptome in the mouse embryo. PLoS Biol 9:e1000582

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Gelly J-C, Orgeur M, Jacq C, Lelandais G (2011) MitoGenesisDB: an expression data mining tool to explore spatio-temporal dynamics of mitochondrial biogenesis. Nucleic Acids Res 39:D1079–D1084

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Seltmann S, Stachelscheid H, Damaschun A et al (2013) CELDA—an ontology for the comprehensive representation of cells in complex systems. BMC Bioinformatics 14:228

    Article  PubMed Central  PubMed  Google Scholar 

  47. Secrier M, Pavlopoulos GA, Aerts J, Schneider R (2012) Arena3D: visualizing time-driven phenotypic differences in biological systems. BMC Bioinformatics 13:45

    Article  PubMed Central  PubMed  Google Scholar 

  48. Reinhardt M, Elias J, Albert J et al (2008) EpiScanGIS: an online geographic surveillance system for meningococcal disease. Int J Health Geogr 7:33

    Article  PubMed Central  PubMed  Google Scholar 

  49. Stoma S, Fröhlich M, Gerber S, Klipp E (2011) STSE: spatio-temporal simulation environment dedicated to biology. BMC Bioinformatics 12:126

    Article  PubMed Central  PubMed  Google Scholar 

  50. Batista RTB, Ramirez DB, Santos RD et al (2007) EUCLIS—an information system for circadian systems biology. IET Syst Biol 1:266–273

    Article  CAS  PubMed  Google Scholar 

  51. Peleg M, Tu S (2006) Decision support, knowledge representation and management in medicine. Yearb Med Inform 45:72–80

    Google Scholar 

  52. Brazhnik O, Jones JF (2007) Anatomy of data integration. J Biomed Inform 40:252–269

    Article  PubMed Central  PubMed  Google Scholar 

  53. Lopes Rda S, Resende NM, Honorio-França AC, França EL (2013) Application of bioinformatics in chronobiology research. ScientificWorldJournal 2013:153839

    PubMed  Google Scholar 

  54. Kaul H, Ventikos Y (2013) Investigating biocomplexity through the agent-based paradigm. Brief Bioinform 2013:bbt077v1-bbt077

    Google Scholar 

  55. Sukumaran S, Jusko WJ, Dubois DC, Almon RR (2011) Light–dark oscillations in the lung transcriptome: implications for lung homeostasis, repair, metabolism, disease, and drug action. J Appl Physiol 110:1732–1747

    Article  PubMed Central  PubMed  Google Scholar 

  56. Salhab M, Keith LG, Laguens M et al (2006) The potential role of dynamic thermal analysis in breast cancer detection. Int Semin Surg Oncol 3:8

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  57. Chapa J, Bourgo RJ, Greene GL et al (2013) Examining the pathogenesis of breast cancer using a novel agent-based model of mammary ductal epithelium dynamics. PLoS One 8:e64091

    Article  PubMed Central  PubMed  Google Scholar 

  58. Samwald M, Coulet A, Huerga I et al (2012) Semantically enabling pharmacogenomic data for the realization of personalized medicine. Pharmacogenomics 13:201–212

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Authors and Affiliations

  1. PharmTao, 5672, 4601 Lafayette Street, Santa Clara, CA, 95056-5672, USA

    Qing Yan

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  1. Qing Yan
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Correspondence to Qing Yan .

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  1. PharmTao, Santa Clara, California, USA

    Qing Yan

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Yan, Q. (2014). Translational Bioinformatics Approaches for Systems and Dynamical Medicine. In: Yan, Q. (eds) Pharmacogenomics in Drug Discovery and Development. Methods in Molecular Biology, vol 1175. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0956-8_2

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  • DOI: https://doi.org/10.1007/978-1-4939-0956-8_2

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0955-1

  • Online ISBN: 978-1-4939-0956-8

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