Managing Complexity: Challenges of Modeling in Integrative Systems Biology

Data Science Lecture Series


November 20, 2015
1:00pm to 2:30pm
190 Doe Library
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Over the last 10 years, there has been a rapid growth in analyses of computational modeling and simulation in the philosophy of science. Research on simulations has concentrated largely on simulations built using established background theories or theoretical models and the relations between these simulations and theory. Examples have been sourced mainly from the physical sciences, including astrophysics, nanophysics, and climate science. My research group’s five-year ethnographic investigations of modeling practices in integrative systems biology have revealed that not all equation-based modeling is theory driven.The modelers we have studied have no background body of laws and principles of the biological domain, which could provide the resources for constructing models. In the labs we investigated, engineers and applied mathematicians with little biological knowledge and usually no experimental experience attempt to model complex nonlinear biological networks for which the data are often sparse and are rarely adequate for applying a set mathematical framework. Models are strategic adaptations to a complex set of constraints system biologists are working under, ranging from data constraints to cognitive constraints to collaboration constraints. I argue that simulation in systems biology is not, as currently characterized, just for experimenting on systems in order to find out the consequences of a model but plays a fundamental role in incrementally building the model, enabling the modeler to learn the relevant known and unknown features of a system and to gain an understanding of and make inferences about its dynamics. Simulation’s roles as a cognitive resource make possible the construction of representations of complex systems without a theoretical basis. Through the building process, modeler and model become a coupled cognitive system, which enables a modeler with limited knowledge of biology to make fundamental biological discoveries, as we have witnessed.


Nancy J. Nersessian

Research Associate, Department of Psychology, Harvard University; Senior Visiting Fellow, Pittsburgh Center for Philosophy of Science

Nancy J. Nersessian is Regents’ Professor (Emerita), Georgia Institute of Technology. She currently is Research Associate, Department of Psychology, Harvard University and, for 2015-2016, the Senior Visiting Fellow, Center for Philosophy of Science, University of Pittsburgh. Her research focuses on the creative research practices of scientists and engineers, especially how modeling practices lead to fundamentally new ways of understanding the world. This research has been funded by NSF and NEH. She is a fellow of AAAS and the Cognitive Science Society and a foreign member of the Royal Netherlands Academy of Arts and Sciences. Her numerous publications include Creating Scientific Concepts (MIT, 2008, Patrick Suppes Prize in Philosophy of Science, 2011) and Science as Psychology: Sense-making and Identity in Science Practice (with L. Osbeck, K. Malone, W. Newstetter, Cambridge, 2011, William James Book Prize, 2012).