Abstract
Modelizing biology and life has always been a challenge to the modern scientific method due to the complexity of the components and interactions of a biological organization from the cell to a whole human body. Information technology (IT) offers the ability to treat and organize large amounts of data and leads to a paradigm of integration—in opposition to reduction—to explain biological systems and phenomenon. Quantitative datasets of DNA, RNA, proteins, and metabolites provide an unprecedented starting point to understand the effects of perturbations on a cell and, with addition of clinical tests and imaging, the effect on the whole body. The informational view of biology defines biological information—biomarker—as a given data integrated in a network. This leads to a “systems” approach to physiology and pathophysiology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Systems biology. Wikipedia. 2018.
Sauer U, Heinemann M, Zamboni N. Getting closer to the whole picture. Science. 2007;316:550. https://doi.org/10.1126/science.1142502.
Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117(4):500–44.
Noble D. Cardiac action and pacemaker potentials based on the Hodgkin-Huxley equations. Nature. 1960;188:495–7. https://doi.org/10.1038/188495b0.
@ISBUSA. What is systems biology. Institute for Systems Biology; Seattle, 2018.
Rosen R. Systems theory and biology. In: Mesarović MD, editor. Proceedings of the 3rd systems symposium, Cleveland, Oct 1966. Springer, New York; 1968. xii + 403 p., illus. $16. 1968. https://doi.org/10.1126/science.161.3836.34
Zewail A. Physical biology: from atoms to medicine. London: Imperial College Press. p. 339.
Human Genome Project. Wikipedia. 2018.
Zeng BJ. On the holographic model of human body. In: 1st national conference of comparative studies traditional Chinese medicine and west medicine, medicine and philosophy, 1992.
Kamada T. System biomedicine: a new paradigm in bio-medical engineering. Jpn J Med Electron Biol Eng. 1991;29(Supplement):1–1.
National Research Council Committee on a New Biology for the 21st Century: Ensuring the United States Leads the Coming Biology R. The National Academies Collection: Reports funded by National Institutes of Health. A new biology for the 21st century: ensuring the United States leads the coming biology revolution. Washington, DC: National Academies Press (US)National Academy of Sciences; 2009.
National Research Council Committee on AFfDaNToD. The National Academies Collection: Reports funded by National Institutes of Health. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press (US)National Academy of Sciences; 2011.
Schadt EE, Linderman MD, Sorenson J, et al. Computational solutions to large-scale data management and analysis. Nat Rev Genet. 2010;11(9):647–57. https://doi.org/10.1038/nrg2857. [published Online First: 2010/08/19].
Flores M, Glusman G, Brogaard K, et al. P4 medicine: how systems medicine will transform the healthcare sector and society. Per Med. 2013;10(6):565–76. https://doi.org/10.2217/pme.13.57.
Sagner M, McNeil A, Puska P, et al. The P4 health spectrum – a predictive, preventive, personalized and participatory continuum for promoting healthspan. Prog Cardiovasc Dis. 2017;59(5):506–21. https://doi.org/10.1016/j.pcad.2016.08.002. [published Online First: 2016/08/23].
Bengoechea JA. Infection systems biology: from reactive to proactive (P4) medicine. Int Microbiol. 2012;15(2):55–60. https://doi.org/10.2436/20.1501.01.158. [published Online First: 2012/08/01].
Tian Q, Price ND, Hood L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J Intern Med. 2012;271(2):111–21. https://doi.org/10.1111/j.1365-2796.2011.02498.x. [published Online First: 2011/12/07].
Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J. 2018;9(1):77–102. https://doi.org/10.1007/s13167-018-0128-8. [published Online First: 2018/03/09].
Fox S, Duggan M. Health online 2013. Washington, DC: Pew Internet & American Life Project; 2013. p. 1.
Picton G. Study shows promise in automated reasoning, hypothesis generation over complete medical literature 2018. Available from: https://www.bcm.edu/news/research/automated-reasoning-hypothesis-generation.
Watts D, Newman M, Barabási, A. The structure and dynamics of networks Princeton studies in complexity Mathematics -Applied (Paperback and eBook). 2006.
Yan Q. Translational bioinformatics and systems biology methods for personalized medicine. 1st ed: PharmTao, Santa Clara, CA, USA; 2018.
Iris F. Biological modeling in the discovery and validation of cognitive dysfunctions biomarkers. In: Turck C, editor. Biomarkers for psychiatric disorders. Boston: Springer; 2008.
Dodd MJ, Miaskowski C, Lee KA. Occurrence of symptom clusters. JNCI Monogr. 2018;2004(32):76–8. https://doi.org/10.1093/jncimonographs/lgh008.
R. C. Partage de données biomédicales : modèles, sémantique et qualité. BioInformatique. Biologie Systémique. Université Pierre et Marie Curie – Paris VI: Université Pierre et Marie Curie – Paris VI.
Fishbain D, Gao JR, Lewis J, et al. Examination of symptom clusters in acute and chronic pain patients. Pain Physician. 2014;17(3):E349–57. [published Online First: 2014/05/23].
Garcia-Olmos L, Salvador CH, Alberquilla A, et al. Comorbidity patterns in patients with chronic diseases in general practice. PLoS One. 2012;7(2):e32141. https://doi.org/10.1371/journal.pone.0032141. [published Online First: 2012/02/24].
Cho DY, Kim YA, Przytycka TM. Chapter 5: Network biology approach to complex diseases. PLoS Comput Biol. 2012;8(12):e1002820. https://doi.org/10.1371/journal.pcbi.1002820. [published Online First: 2013/01/10].
Suderman M, Hallett M. Tools for visually exploring biological networks. Bioinformatics. 2007;23(20):2651–9. https://doi.org/10.1093/bioinformatics/btm401. [published Online First: 2007/08/28].
Diaz-Beltran L, Cano C, Wall DP, et al. Systems biology as a comparative approach to understand complex gene expression in neurological diseases. Behav Sci (Basel). 2013;3:253–72.
Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010;5(6):463–6. https://doi.org/10.1097/COH.0b013e32833ed177. [published Online First: 2010/10/28].
Wehling M. Translational medicine: science or wishful thinking? J Transl Med England. 2008;6:31.
Yan Q. Toward the integration of personalized and systems medicine: challenges, opportunities and approaches. 2010 Pers Med. 2011;8(1):1–4. https://doi.org/10.2217/pme.10.77.
Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5.
Durako AR, Mishkel G. Creating the virtual cardiac surgical home: implementation of an app based enhanced recovery after surgery platform to improve outcomes and patient engagement. J Am Coll Cardiol. 2018;71(11):A2121.
Satagopam V, Gu W, Eifes S, et al. Integration and visualization of translational medicine data for better understanding of human diseases. Big Data. 2016;4(2):97–108. https://doi.org/10.1089/big.2015.0057. [published Online First: 2016/07/22].
Bui AAT, Van Horn JD. Envisioning the future of ‘big data’ biomedicine. J Biomed Inform. 2017;69:115–7. https://doi.org/10.1016/j.jbi.2017.03.017.
Inria. OrphaMine – Plateforme de fouille de données pour les maladies rares – Inria. 2018.
Manos D. Rocking the baseline: Verily, Duke, and stanford aim to make medicine more predictive with a new baseline study. Clinical OMICs. 2017;4(3):3–4.
Wray NR, et al. Common disease is more complex than implied by the core gene omnigenic model. Cell. 2018;173(7):1573–80.
Philippi S, Köhler J. Addressing the problems with life-science databases for traditional uses and systems biology. Nat Rev Genet. 2006;7(6):482. https://doi.org/10.1038/nrg1872.
Vogt H, Hofmann B, Getz L. The new holism: P4 systems medicine and the medicalization of health and life itself. Med Health Care Philos. 2016;19(2):307–23. https://doi.org/10.1007/s11019-016-9683-8. [published Online First: 2016/01/29].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
André, A., Vignaux, JJ. (2019). Precision Medicine. In: André, A. (eds) Digital Medicine. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-98216-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-98216-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98215-1
Online ISBN: 978-3-319-98216-8
eBook Packages: MedicineMedicine (R0)