Causal Concepts Guiding Model Specification in Systems Biology

Open access


In this paper I analyze the process by which modelers in systems biology arrive at an adequate representation of the biological structures thought to underlie data gathered from high-throughput experiments. Contrary to views that causal claims and explanations are rare in systems biology, I argue that in many studies of gene regulatory networks modelers aim at a representation of causal structure. In addressing modeling challenges, they draw on assumptions informed by theory and pragmatic considerations in a manner that is guided by an interventionist conception of causal structure. While doubts have been raised about the applicability of this notion of causality to complex biological systems, it is here seen to be an adequate guide to inquiry.

Albert, Réka. 2007. Network inference, analysis, and modeling in systems biology. The Plant Cell 19: 3327–38.

Bansal, Mukesh; Belcastro, Vincenzo; Ambesi-Impiombato, Alberto; and di Bernardo, Diego. 2007. How to infer gene networks from expression profiles. Molecular Systems Biology 3: 1–10.

Braillard, Pierre-Alain. 2010. Systems biology and the mechanistic framework. History and Philosophy of Life Sciences 32: 43–62.

Cartwright, Nancy. 1993. Mark and probabilities: two ways to find causal structure. In Scientific Philosophy: Origins and Development, ed. by F. Stadler.Dordrecht: Kluwer.

Cartwright, Nancy. 2002. Against modularity, the causal markov condition and any link between the two: comments on hausman and woodward. British Journal for the Philosophy of Science 53: 411–53.

Cartwright, Nancy. 2007. Hunting Causes and Using Them: Approaches in Philosophy and Economics. New York: Cambridge University Press.

Cartwright, Nancy; Shomar, T.; and Suárez, M. 1995. The tool box of science. In Theories and models in scientific processes. Amsterdam: Rodopi.

Chickering, David Maxwell. 1996. Learning bayesian networks is NP-complete. In Learning from Data: Artificial Intelligence and Statistics V. New York: Springer-Verlag.

Chickering, David Maxwell; Heckerman, David; and Meek, Christopher. 2004. Large-sample learning of bayesian networks is NP-Hard. Journal of Machine Learning 5: 1287–330.

Craver, Carl. 2007. Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience. Oxford: Oxford University Press.

De Backer, Phillipe; De Waele, Danny; and Van Speybroeck, Linda. 2010. Ins and outs of systems biology vis-à-vis molecular biology: continuation or clear cut? Acta Biotheoretic 58: 15–49.

Douglas, Heather. 2000. Inductive risk and values in science. Philosophy of Science 67: 559–79.

Friedman, Nir; Linial, Michal; Nachman, Iftach; and Pe’er, De’er. 2000. Using bayesian networks to analyze expression data. Journal of Computational Biology 7: 601–20.

Hartemink, Alexander J.; Gifford, David K.; Jaakola, Tommi S.; and Young, Richard A. 2002. Combining location and expression data for principled discovery of genetic regulatory network models. In Pacific Symposium on Biocomputing 2002: Kauai, Hawaii, 3-7 January 2002: 437–49.

Hausman, Daniel W.; and Woodward, James. 1999. Independence, invariance and the causal markov condition. British Journal for the Philosophy of Science 50: 521–83.

Hausman, Daniel W.; and Woodward, James. 2004. Modularity and the causal markov condition: a restatement. British Journal for the Philosophy of Science 55: 147–61.

He, Feng; Balling, Rudi; Zeng, An-Ping. 2009. Reverse engineering and verification of gene networks: principles, assumptions and limitations of present methods and future perspectives. Journal of Biotechnology 144: 190–203.

Le Novère, Nicolas. 2015. Quantitative and logic modelling of molecular and gene networks. Nature Reviews: Genetics 16: 146–58.

Levins, Richard. 1966. The strategy of model building in population biology. American Scientist 54: 421–31.

Levy, Arnon; and Bechtel, William. 2013. Abstraction and the organization of mechanisms. Philosophy of Science 80: 241–61.

MacKay, David J. C. 1992. Bayesian interpolation. Neural Computation 4: 415–47.

MacLeod, Miles; and Nersessian, Nancy. J. 2013. Building simulations from the ground up: modeling and theory in systems biology. Philosophy of Science 80: 533–56.

MacLeod, Miles; and Nersessian, Nancy J. 2015. Modeling systems-level dynamics: understanding without mechanistic explanation in integrative systems biology. Studies in History and Philosophy of Science Part C—Biological and Biomedical Science 49: 1–11.

Markowetz, Florian; and Spang, Rainer. 2007. Inferring cellular networks—a review. BMC Bioinformatics 8 (Suppl 6) S5. <>

Matthiessen, Dana. 2015. Mechanistic explanation in systems biology: cellular networks. British Journal for the Philosophy of Science. <>

McMullin, Ernan. 1985. Galilean idealization. Studies in the History and Philosophy of Science 16: 247–73.

Mitchell, Sandra. 2008. Exporting causal knowledge in evolutionary and developmental biology. Philosophy of Science 75: 697–706.

Morgan, Mary; and Morrison, Margaret. 1999. Models as Mediators: Perspectives on Natural and Social Science. Cambridge: Cambridge University Press.

Opgen-Rhein, Rainer; and Strimmer, Korbinian. 2007. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Systems Biology 1. <>.

Pearl, Judea. 2000. Causality: Models, Reasoning, and Inference. New York: Cambridge University Press.

Peter, Isabelle S.; and Davidson, Eric H. 2015. Genomic Control Process: Development and Evolution. New York: Elsevier.

Sachs, Karen; Perez, Omar; Pe’er, Dana; Lauffenburger, Douglas A.; Nolan, Garry P. 2005. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308: 523–9.

Spirtes, Peter; Glymour, Clark; and Scheines, Richard. 1993. Causation, Prediction, and Search. New York: Springer-Verlag.

Woodward, James. 2003. Making Things Happen: A Theory of Causal Explanation. New York: Oxford University Press.

Woodward, James. 2010. Causation in biology: stability, specificity, and the choice of levels of explanation. Biology and Philosophy 25: 287–318.

Woodward, James. 2013. Mechanistic explanation: its scopes and limits. Proceedings of the Aristotelian Society S87: 39–65.

Weisberg, Michael. 2013. Simulation and Similarity: Using Models to Understand the World. New York: Oxford University Press.

Westerhoff, Hans V.; and Kell, Douglas B. 2007. The methodologies of systems biology. In Systems Biology: Philosophical Foundations. New York: Elsevier.

Winsberg, Eric. 2010. Science in the Age of Computer Simulation. Chicago: University of Chicago Press.

Wouters, Arno G. 2007. Design explanation: determining the constraints on what can be alive. Erkenntnis 67: 65–80.

Yu, Jing; Smith, V. Anne; Wang, Paul P.; Hartemink, Alexander J.; and Jarvis, Erich D. 2004. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20: 3594–603.


International Journal of Philosophy

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 52 52 52
PDF Downloads 39 39 39