Advertisement

Systems Biology in the Light of Uncertainty: The Limits of Computation

  • Miles MacLeod
Chapter
Part of the Boston Studies in the Philosophy and History of Science book series (BSPS, volume 327)

Abstract

In this chapter we explore basic mathematical and other constraints which limit the often novel uses of computation employed in modern computational system biology. These constraints generate substantial obstacles for one goal prominent in the field; namely, the goal of producing models valid for predictive uses in clinical and other contexts. However on closer examination many applications of computation and simulation in the field have more pragmatic or investigative goals in mind, suggesting an important role for rationalizing uses of computation in systems biology and elsewhere as investigative tools. We discuss the concept of an “investigative tool”, and what insights it might offer our understanding of modern computational strategies and the bases for them.

Keywords

Parameter Space System Biology Parameter Uncertainty Ensemble Method Investigative Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The research for this paper was supported by an US National Science Foundation (DRL097394084), as well as by a Postdoctoral Fellowship at this Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences and a position at the University of Twente. I would like to thank the editors of the volume in particular for their helpful advice in the development of this paper.

References

  1. Apgar, J. F., Witmer, D. K., White, F. M., & Tidor, B. (2010). Sloppy models, parameter uncertainty, and the role of experimental design. Molecular BioSystems, 6(10), 1890–1900.CrossRefGoogle Scholar
  2. Brown, K. S., Hill, C. C., Calero, G. A., Myers, C. R., Lee, K. H., Sethna, J. P., & Cerione, R. A. (2004). The statistical mechanics of complex signaling networks: nerve growth factor signaling. Physical Biology, 1(3), 184.CrossRefGoogle Scholar
  3. Carusi, A. (2014). Validation and variability: Dual challenges on the path from systems biology to systems medicine. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 48, 28–37.CrossRefGoogle Scholar
  4. Carusi, A., Burrage, K., & Rodríguez, B. (2012). Bridging experiments, models and simulations: An integrative approach to validation in computational cardiac electrophysiology. American Journal of Physiology-Heart and Circulatory Physiology, 303(2), H144–H155.CrossRefGoogle Scholar
  5. Chang, H. (2014). Epistemic activities and systems of practice: Units of analysis in philosophy of science after the practice turn. In L. Soler, S. Zwart, M. Lynch, & V. Israel-Jost (Eds.), Science after the practice turn in the philosophy, history, and social studies of science (p. 67). New York: Routledge.Google Scholar
  6. Gutenkunst, R. N., Waterfall, J. J., Casey, F. P., Brown, K. S., Myers, C. R., & Sethna, J. P. (2007). Universally sloppy parameter sensitivities in systems biology models. PLoS Computational Biology, 3(10), e189.CrossRefGoogle Scholar
  7. Hood, L., Heath, J. R., Phelps, M. E., & Lin, B. (2004). Systems biology and new technologies enable predictive and preventative medicine. Science, 306(5696), 640–643.CrossRefGoogle Scholar
  8. Humphreys, P. (2011). Computational science and its effects. In M. Carrier, & A. Nordmann (Eds.), Science in the context of application: Methodological change, conceptual transformation, cultural reorientation (pp. 131–142). Springer.Google Scholar
  9. Ideker, T., Galitski, T., & Hood, L. (2001). A new approach to decoding life: Systems biology. Annual Review of Genomics and Human Genetics, 2(1), 343–372.CrossRefGoogle Scholar
  10. Kitano, H. (2002). Looking beyond the details: A rise in system-oriented approaches in genetics and molecular biology. Current genetics, 41(1), 1–10.CrossRefGoogle Scholar
  11. Kuepfer, L., Peter, M., Sauer, U., & Stelling, J. (2007). Ensemble modeling for analysis of cell signaling dynamics. Nature Biotechnology, 25(9), 1001–1006.CrossRefGoogle Scholar
  12. Lenhard, J. (2007). Computer simulation: The cooperation between experimenting and modeling. Philosophy of Science, 74(2), 176–194.CrossRefGoogle Scholar
  13. Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 41(3), 253–262.CrossRefGoogle Scholar
  14. MacLeod, M. (2016). Heuristic approaches to models and modeling in systems biology. Biology and Philosophy, 31(3), 353–372.CrossRefGoogle Scholar
  15. MacLeod, M., & Nersessian, N. J. (2013a). Building simulations from the ground up: Modeling and theory in systems biology. Philosophy of Science, 80(4), 533–556.CrossRefGoogle Scholar
  16. MacLeod, M., & Nersessian, N. J. (2013b). Coupling simulation and experiment: The bimodal strategy in integrative systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 44(4), 572–584.CrossRefGoogle Scholar
  17. MacLeod, M., & Nersessian, N. J. (2014). Strategies for coordinating experimentation and modeling in integrative systems biology. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution, 322(4), 230–239.CrossRefGoogle Scholar
  18. MacLeod, M., & Nersessian, N. J. (2015). Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 49, 1–11.CrossRefGoogle Scholar
  19. Marder, E., & Taylor, A. L. (2011). Multiple models to capture the variability in biological neurons and networks. Nature Neuroscience, 14(2), 133–138.Google Scholar
  20. Parker, W. S. (2010a). Whose probabilities? Predicting climate change with ensembles of models. Philosophy of Science, 77(5), 985–997.CrossRefGoogle Scholar
  21. Parker, W. S. (2010b). Predicting weather and climate: Uncertainty, ensembles and probability. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 41(3), 263–272.CrossRefGoogle Scholar
  22. Petersen, A. C. (2006). Simulation uncertainty and the challenge of postnormal science. In J. Lenhard, G. Küppers, & T. Shinn (Eds.), Simulation: Pragmatic constructions of reality – Sociology of the sciences (pp. 173–185). Springer: Dordrecht.CrossRefGoogle Scholar
  23. Savageau, M. A. (1969). Biochemical systems analysis: I. Some mathematical properties of the rate law for the component enzymatic reactions. Journal of Theoretical Biology, 25(3), 365–369.CrossRefGoogle Scholar
  24. Tran, L. M., Rizk, M. L., & Liao, J. C. (2008). Ensemble modeling of metabolic networks. Biophysical Journal, 95(12), 5606–5617.CrossRefGoogle Scholar
  25. Turkheimer, F. E., Hinz, R., & Cunningham, V. J. (2003). On the undecidability among kinetic models: from model selection to model averaging. Journal of Cerebral Blood Flow & Metabolism, 23(4), 490–498.CrossRefGoogle Scholar
  26. Voit, E. O. (2000). Computational analysis of biochemical systems: A practical guide for biochemists and molecular biologists. Cambridge: Cambridge University Press.Google Scholar
  27. Voit, E. O. (2014). Mesoscopic modeling as a starting point for computational analyses of cystic fibrosis as a systemic disease. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1844(1), 258–270.CrossRefGoogle Scholar
  28. Voit, E. O., Qi, Z., & Kikuchi, S. (2012). Mesoscopic models of neurotransmission as intermediates between disease simulators and tools for discovering design principles. Pharmacopsychiatry, 45(S 01), S22–S30.CrossRefGoogle Scholar
  29. Wahl, S. A., Haunschild, M. D., Oldiges, M., & Wiechert, W. (2006). Unravelling the regulatory structure of biochemical networks using stimulus response experiments and large-scale model selection. IEE Proceedings-Systems Biology, 153(4), 275–285.CrossRefGoogle Scholar
  30. Westerhoff, H. V., & Kell, D. B. (2007). The methodologies of systems biology. In F. Boogerd, F. J. Bruggeman, J.-H. S. Hofmeyer, & H. V. Westerhoff (Eds.), Systems biology: Philosophical foundations (pp. 23–70). Amsterdam: Elsevier.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of TwenteEnschedeThe Netherlands

Personalised recommendations