Advertisement

Translational Bioinformatics and Systems Biology Approaches for Personalized Medicine

  • Qing Yan
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 662)

Abstract

Systems biology and pharmacogenomics are emerging and promising fields that will provide a thorough understanding of diseases and enable personalized therapy. However, one of the most significant obstacles in the practice of personalized medicine is the translation of scientific discoveries into better therapeutic outcomes. Translational bioinformatics is a powerful method to bridge the gap between systems biology research and clinical practice. This goal can be achieved through providing integrative methods to enable predictive models for therapeutic responses. As a media between bench and bedside, translational bioinformatics has the mission to meet challenges in the development of personalized medicine. On the biomedical side, translational bioinformatics would enable the identification of biomarkers based on systemic analyses. It can improve the understanding of the correlations between genotypes and phenotypes. It would enable novel insights of interactions and interrelationships among different parts in a whole system. On the informatics side, methods based on data integration, data mining, and knowledge representation can provide decision support for both researchers and clinicians. Data integration is not only for better data access, but also for knowledge discovery. Decision support based on translational bioinformatics means better information and workflow management, efficient literature and resource retrieval, and communication improvement. These approaches are crucial for understanding diseases and applying personalized therapeutics at systems levels.

Key words

Systems biology Pharmacogenomics Personalized medicine Translational Bio-informatics Outcomes Biomarkers Genotypes Phenotypes Data integration Data mining Knowledge representation Decision support Interactions Workflow 

References

  1. 1.
    Yan Q (2005) Pharmacogenomics and systems biology of membrane transporters. Mol Biotechnol 29:75–88CrossRefPubMedGoogle Scholar
  2. 2.
    Meyer UA (2004) Pharmacogenetics – five decades of therapeutic lessons from genetic diversity. Nat Rev Genet 5:669–676CrossRefPubMedGoogle Scholar
  3. 3.
    Madhavan S, Zenklusen JC et al (2009) Rembrandt: helping personalized medicine become a reality through integrative translational research. Mol Cancer Res 7:157–167CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Wang X, Liu L, Fackenthal J et al (2009) Translational integrity and continuity: personalized biomedical data integration. J Biomed Inform 42:100–112CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Greenes RA (2003) Decision support at the point of care: challenges in knowledge representation, management, and patient-specific access. Adv Dent Res 17:69–73CrossRefPubMedGoogle Scholar
  6. 6.
    Yan Q (2003) Bioinformatics and data integration in membrane transporter studies. Methods Mol Biol 227:37–60PubMedGoogle Scholar
  7. 7.
    American Medical Informatics Association (AMIA). AMIA strategic plan. Available at: http://www.amia.org/inside/stratplan/. Accessed July 2009
  8. 8.
    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 10:43–48CrossRefPubMedGoogle Scholar
  9. 9.
    Yan Q et al (2000) Preventing adverse drug events (ADEs): the role of computer information systems. Drug Inf J 34:1247–1260Google Scholar
  10. 10.
    Yan Q (2008) The integration of personalized and systems medicine: bioinformatics support for pharmacogenomics and drug discovery. Methods Mol Biol 448:1–19CrossRefPubMedGoogle Scholar
  11. 11.
    Aich P, Babiuk LA et al (2009) Biomarkers for prediction of bovine respiratory disease outcome. OMICS 13:199–209CrossRefPubMedGoogle Scholar
  12. 12.
    Wehling M (2008) Translational medicine: science or wishful thinking? J Transl Med 6:31CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Emerson JW, Dolled-Filhart M et al (2009) Quantitative assessment of tissue biomarkers and construction of a model to predict outcome in breast cancer using multiple imputation. Cancer Inform 7:29–40PubMedPubMedCentralGoogle Scholar
  14. 14.
    Chia S, Senatore F et al (2008) Utility of cardiac biomarkers in predicting infarct size, left ventricular function, and clinical outcome after primary percutaneous coronary intervention for ST-segment elevation myocardial infarction. JACC Cardiovasc Interv 1:415–423CrossRefPubMedGoogle Scholar
  15. 15.
    Khuseyinova N, Koenig W (2006) Biomarkers of outcome from cardiovascular disease. Curr Opin Crit Care 12:412–419CrossRefPubMedGoogle Scholar
  16. 16.
    Welsh P, Barber M et al (2009) Associations of inflammatory and haemostatic biomarkers with poor outcome in acute ischaemic stroke. Cerebrovasc Dis 27:247–253CrossRefPubMedGoogle Scholar
  17. 17.
    Knudsen LS, Klarlund M et al (2008) Biomarkers of inflammation in patients with unclassified polyarthritis and early rheumatoid arthritis. Relationship to disease activity and radiographic outcome. J Rheumatol 35:1277–1287PubMedGoogle Scholar
  18. 18.
    Ozkisacik EA, Discigil B et al (2006) Effects of cyclosporin a on neurological outcome and serum biomarkers in the same setting of spinal cord ischemia model. Ann Vasc Surg 20:243–249CrossRefPubMedGoogle Scholar
  19. 19.
    Hurks R, Peeters W et al (2009) Biobanks and the search for predictive biomarkers of local and systemic outcome in atherosclerotic disease. Thromb Haemost 101:48–54PubMedGoogle Scholar
  20. 20.
    Radulovic D, Jelveh S et al (2004) Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 3:984–997CrossRefPubMedGoogle Scholar
  21. 21.
    Camp RL, Dolled-Filhart M et al (2004) X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 10:7252–7259CrossRefPubMedGoogle Scholar
  22. 22.
    Yan Q (2008) Pharmacogenomics in drug discovery and development. Preface. Methods Mol Biol 448:v–viiPubMedGoogle Scholar
  23. 23.
    Peleg M, Tu S (2006) Decision support, knowledge representation and management in medicine. Yearb Med Inform, 72–80Google Scholar
  24. 24.
    Schreiber G, Akkermans H, Anjewierden A et al (2000) Knowledge engineering and management: the common KADS methodology. The MIT Press, Cambridge, MAGoogle Scholar
  25. 25.
    Yan Q (2010) Bioinformatics for transporter pharmacogenomics and systems biology: data integration and modeling with UML. Methods Mol Biol 637:23–45CrossRefPubMedGoogle Scholar
  26. 26.
    Daemen A, Gevaert O, Ojeda F et al (2009) A kernel-based integration of genome-wide data for clinical decision support. Genome Med 1:39CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Brazhnik O, Jones JF (2007) Anatomy of data integration. J Biomed Inform 40:252–269CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Hwang D, Rust AG, Ramsey S et al (2005) A data integration methodology for systems biology. Proc Natl Acad Sci USA 102:17296–17301CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
  30. 30.
    Hucka M, Finney A, Bornstein BJ et al (2004) Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. Syst Biol (Stevenage) 1:41–53CrossRefGoogle Scholar
  31. 31.
    Ruttenberg A, Clark T, Bug W et al (2007) Advancing translational research with the Semantic Web. BMC Bioinform 8(Suppl 3):S2CrossRefGoogle Scholar
  32. 32.
    Rassinoux AM (2008) Decision support, knowledge representation and management: structuring knowledge for better access. Findings from the yearbook 2008 section on decision support, knowledge representation and management. Yearb Med Inform 80–82Google Scholar
  33. 33.
    Moskovitch R, Martins SB, Behiri E et al (2007) A comparative evaluation of full-text, concept-based, and context-sensitive search. J Am Med Inform Assoc 14:164–174CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.PharmTaoSanta ClaraUSA

Personalised recommendations