Models in Systems Medicine

Open access


Systems medicine is a promising new paradigm for discovering associations, causal relationships and mechanisms in medicine. But it faces some tough challenges that arise from the use of big data: in particular, the problem of how to integrate evidence and the problem of how to structure the development of models. I argue that objective Bayesian models offer one way of tackling the evidence integration problem. I also offer a general methodology for structuring the development of models, within which the objective Bayesian approach fits rather naturally.

Adams, E. W. 1998. A Primer of Probability Logic. CSLI Publications, Stanford.

Baumgartner, M.; and Gebharter, A. 2016. Constitutive relevance, mutual manipulability, and fat-handedness. British Journal for the Philosophy of Science 67(3): 731–56.

Boogerd, F. C.; Bruggeman, F. J.; Hofmeyr, J.-H. S.; and Westerhoff, H. V. (eds.) 2007. Systems Biology: Philosophical Foundations. Elsevier, Amsterdam.

Brigandt, I. 2013. Systems biology and the integration of mechanistic explanation and mathematical explanation. Studies in History and Philosophy of Biological and Biomedical Sciences 44(4A): 477–92.

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.

Casini, L.; Illari, P. M.; Russo, F.; and Williamson, J. 2011. Models for prediction, explanation and control: recursive Bayesian networks. Theoria, 26(1): 5–33.

Clarke, B.; Gillies, D.; Illari, P.; Russo, F.; and Williamson, J. 2013. The evidence that evidence-based medicine omits. Preventative Medicine 57(6): 745–7.

Clarke, B.; Gillies, D.; Illari, P.; Russo, F.; and Williamson, J. 2014a. Mechanisms and the evidence hierarchy. Topoi 33(2): 339–60.

Clarke, B.; Leuridan, B.; and Williamson, J. 2014b. Modelling mechanisms with causal cycles. Synthese 191(8): 1651–81.

Craver, C. F. 2007. Explaining the Brain. Oxford University Press.

Danks, D. 2002. Learning the causal structure of overlapping variable sets. In Discovery Science: Proceedings of the 5th International Conference, ed. by S. Lange, K. Satoh and C. H. Smith, 178–91. Berlin. Springer.

Darwiche, A. 2009. Modeling and Reasoning with Bayesian Networks. Cambridge University Press, New York.

Dowe, P. 2000. Causality and explanation: review of Salmon. British Journal for the Philosophy of Science 51: 165–74.

Galas, D. J.; and Hood, L. 2009. Systems biology and emerging technologies will catalyze the transition from reactive medicine to predictive, personalized, preventive and participatory (P4) medicine. Interdisciplinary Bio Central 1(6): 1–5.

Gammerman, A. (ed.) 1999. Causal models and intelligent data management. Springer, Berlin.

Glymour, C.; and Cooper, G. F. (eds.) 1999. Computation, Causation, and Discovery. MIT Press, Cambridge MA.

Green, S. 2013. When one model is not enough: combining epistemic tools in systems biology. Studies in History and Philosophy of Biological and Biomedical Sciences 44(4A) :170–80.

Gruta, N. L. L.; and Turner, S. J. 2014. T cell mediated immunity to influenza: mechanisms of viral control. Trends in Immunology 35(8): 396–402.

Hoehndorf, R.; Dumontier, M.; Gennari, J. H.; Wimalaratne, S.; de Bono, B.; Cook, D. L.; and Gkoutos, G. V. 2011. Integrating systems biology models and biomedical ontologies. BMC Systems Biology 5(124) :1–16.

Illari, P. M.; and Williamson, J. 2012. What is a mechanism? Thinking about mechanisms across the sciences. European Journal for Philosophy of Science 2: 119–35.

Jaynes, E. T. 1957. Information theory and statistical mechanics. The Physical Review 106(4): 620–30.

Koller, D. and Friedman, N. 2009. Probabilistic graphical models. MIT Press, Cambridge, MA.

Kyriakopoulou, C.; and Mulligan, B. 2010. From systems biology to systems medicine. European Commission, DG Research, Directorate of Health workshop report.

Landes, J.; Osimani, B.; and Poellinger, R. 2018. Epistemology of causal inference in pharmacology: towards a framework for the assessment of harms. European Journal for Philosophy of Science 8(1): 3–39.

Landes, J.; and Williamson, J. 2016. Objective Bayesian nets from consistent datasets. In Proceedings of the 35th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, volume 1757 of American Institute of Physics Conference Proceedings, ed. by A. Giffin and K. H. Knuth. Potsdam, NY.

Lefaudeux, D. 2014. U-BIOPRED toolbox for fingerprint and handprint generation. Biomax Symposium 2014: Translating systems medicine into practice.

Leuridan, B. 2012. Three problems for the mutual manipulability account of constitutive relevance in mechanisms. British Journal for the Philosophy of Science 63(2): 399–427.

Machamer, P.; Darden, L.; and Craver, C. 2000. Thinking about mechanisms. Philosophy of Science 67: 1–25.

MacLeod, M.; and Nersessian, N. J. 2013. Coupling simulation and experiment: The bimodal strategy in integrative systems biology. Studies in History and Philosophy of Biological and Biomedical Sciences 44(4A): 572–84.

McKim, V. R.; and Turner, S. 1997. Causality in Crisis? Statistical Methods and the Search for Causal Knowledge in the Social Sciences. University of Notre Dame Press, Notre Dame.

Neapolitan, R. E. 2004. Learning Bayesian Networks. Pearson/Prentice Hall, Upper Saddle River NJ.

Novere, N. L.; Hucka, M.; Mi, H.; Moodie, S.; Schreiber, F.; Sorokin, A.; Demir, E.; Wegner, K.; Aladjem, M. I.; Wimalaratne, S. M.; Bergman, F. T.; Gauges, R.; Ghazal, P.; Kawaji, H.; Li, L.; Matsuoka, Y.; Villeger, A.; Boyd, S. E.; Calzone, L.; Courtot, M.; Dogrusoz, U.; Freeman, T. C.; Funahashi, A.; Ghosh, S.; Jouraku, A.; Kim, S.; Kolpakov, F.; Luna, A.; Sahle, S.; Schmidt, E.; Watterson, S.; Wu, G.; Goryanin, I.; Kell, D. B.; Sander, C.; Sauro, H.; Snoep, J. L.; Kohn, K.; and Kitano, H. 2009. The systems biology graphical notation. Nature Biotechology 27(8): 735–41.

O’Malley, M. A.; and Soyer, O. S. 2012. The roles of integration in molecular systems biology. Studies in History and Philosophy of Biological and Biomedical Sciences 43(1): 58–68.

Parkkinen, V. P.; Wallmann, C.; Wilde, M.; Clarke, B.; Illari, P.; Kelly, M.P.; Norell, C.; Russo, F.; Shaw, B.; and Williamson, J. 2018. Evaluating Evidence of Mechanisms in Medicine: Principles and Procedures. Springer.

Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo CA.

Pearl, J. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge.

Russo, F.; and Williamson, J. 2007. Interpreting causality in the health sciences. International Studies in the Philosophy of Science 21(2): 157–70.

Salmon, W. C. 1984. Scientific Explanation and the Causal Structure of the World. Princeton University Press, Princeton NJ.

Sobradillo, P.; Pozo, F.; and Agusti, A. 2011. P4 medicine: the future around the corner. Archivos de Bronconeumologia 47(1): 35–40.

Spirtes, P.; Glymour, C.; and Scheines, R. 1993. Causation, Prediction, and Search. MIT Press, Cambridge MA, 2nd edition, 2000.

Tillman, R.; Danks, D.; and Glymour, C. 2008. Integrating locally learned causal structures with overlapping variables. In Advances in Neural Information Processing Systems, volume 21, ed. by D. Koller, D. Schuurmans, Y. Bengio and L. Bottou, 1665–72. La Jolla, CA. The NIPS Foundation.

Tillman, R.; and Spirtes, P. 2011. Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, ed. by G. Gordon, D. Dunson and M. Dudík, 3–15. Fort Lauderdale, FL. Journal of Machine Learning Research 15.

Vandamme, D.; Fitzmaurice, W.; Kholodenko, B.; and Kolch, W. 2013. Systems medicine: helping us understand the complexity of disease. Quarterly Journal of Medicine 106(10): 891–5.

Wilde, M.; and Williamson, J. 2016. Models in medicine. In Routledge Companion to Philosophy of Medicine, ed. by M. Solomon, J. Simon and H. Kincaid, 271–84. Routledge: New York and London.

Williamson, J. 2005a. Bayesian Nets and Causality: Philosophical and Computational Foundations. Oxford University Press: Oxford.

Williamson, J. 2005b. Objective Bayesian nets. In We Will Show Them! Essays in Honour of Dov Gabbay, volume 2, ed. by S. Artemov, H. Barringer, A. S. d’Avila Garcez, L. C. Lamb and J. Woods, 713–30. College Publications, London.

Williamson, J. 2010. In Defence of Objective Bayesianism. Oxford University Press, Oxford.

Williamson, J. 2013a. How can causal explanations explain? Erkenntnis 78: 257–75.

Williamson, J. 2013b. Why frequentists and Bayesians need each other. Erkenntnis 78(2): 293–318.

Williamson, J. 2017. Lectures on Inductive Logic. Oxford University Press: Oxford.


International Journal of Philosophy

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 73 73 73
PDF Downloads 77 77 77