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The future of computational biomedicine: Complex systems thinking

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  • Joly, Marcel
  • Rondó, Patrícia H.C.

Abstract

“More is different” (Philip W. Anderson). Complex systems thinking become instrumental for the modern understanding basis of life sciences in general and, hence, medicine and public health. In this perspective paper, we discuss recent literature and invite readers to explore the utility of complex thinking to properly addressing the constrained-based analysis of high-profile open questions in biomedicine with straightforward implications on public health. Recommendations are then proposed to encourage new multidisciplinary teams to come together in a timely manner in response to novel challenges in the theoretical physiology arena. We conclude that there is the need for far greater attention to the issue of complexity to aptly cope with a new array of problems that would have been unthinkable just a few years ago.

Suggested Citation

  • Joly, Marcel & Rondó, Patrícia H.C., 2017. "The future of computational biomedicine: Complex systems thinking," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 132(C), pages 1-27.
    • Handle: RePEc:eee:matcom:v:132:y:2017:i:c:p:1-27
    DOI: 10.1016/j.matcom.2015.06.010
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    1. Scheirer, M.A. & Dearing, J.W., 2011. "An agenda for research on the sustainability of Public Health Programs," American Journal of Public Health, American Public Health Association, vol. 101(11), pages 2059-2067.
    2. Cole, S.W., 2013. "Social regulation of human gene expression: Mechanisms and implications for public health," American Journal of Public Health, American Public Health Association, vol. 103(SUPPL.1), pages 84-92.
    3. Hiroaki Kitano, 2002. "Computational systems biology," Nature, Nature, vol. 420(6912), pages 206-210, November.
    4. David D. Ho & Avidan U. Neumann & Alan S. Perelson & Wen Chen & John M. Leonard & Martin Markowitz, 1995. "Rapid Turnover of Plasma Virions and CD4 Lymphocytes in HIV-1 Infection," Working Papers 95-01-002, Santa Fe Institute.
    5. Sun, Y., 2014. "Rethinking public health: Promoting public engagement through a new discursive environment," American Journal of Public Health, American Public Health Association, vol. 104(1), pages 6-13.
    6. Bures, R.M. & Mabry, P.L. & Orleans, C.T. & Esposito, L., 2014. "Systems science: a tool for understanding obesity," American Journal of Public Health, American Public Health Association, vol. 104(7), pages 1156-1156.
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