1 Nersessian, N.J. & Patton, C.: “Model-based reasoning in interdisciplinary engineering” in Handbook of the Philosophy of Technology and Engineering Sciences, A. Meijers, ed. (Amsterdam: Elsevier, in press, 2009), pp. 687-718. Model-based Reasoning in Interdisciplinary Engineering Nancy J. Nersessian – nancyn@gatech.edu & Christopher Patton – cpatton@gatech.edu School of Interactive Computing Georgia Institute of Technology 1. Introduction Research in biomedical engineering often confronts the problem that it is both impractical and unethical to carry out experiments directly on animals or human subjects. In our studies of two pioneering biomedical engineering research laboratories we have found a common investigative practice in this interdisciplinary field is to design, build, and redesign in vitro systems, which parallel selected features of in vivo systems. The researchers refer to their in vitro models as “devices.” When biological and engineering components are brought together in an investigation, researchers refer to this as a “model-system.” As one respondent stated: “when everything comes together I would call it a ‘model-system’ […]I think you would be very safe to use that [notion] as the integrated nature, the biological aspect coming together with an engineering aspect, so it’s a multifaceted modeling system I think that’s very good terminology to describe that.” Another researcher aptly referred to the processes of constructing and manipulating these model-systems as “putting a thought into the bench top and seeing whether it works or not.” The “bench top” refers not to the flat table surface but comprises all the locales where experimentation takes place. These instantiated “thoughts” (mental models), are physical models (devices) that represent what researchers deem to be salient properties and behaviors of biological systems. They are structural, behavioral, or functional analogs of in vivo phenomena of interest. The devices are also systems themselves, with engineering constraints that often impose simplifications and idealizations unrelated to the biological systems they model. In the following analysis we will examine some of these multifaceted systems in the problem-solving practices of the laboratories, especially as they figure in experimental situations. In each case we will examine how manipulating devices and model-systems enables a form of inference - “model-based reasoning” - different from logical inference through manipulating propositional representations. Our analysis derives from a 5-year investigation of the research practices of two laboratories; one conducts tissue engineering, the other, neural engineering. These are hybrid engineering and science environments. The hybrid nature of these laboratories is reflected in the bio-engineered model-systems developed by the laboratories and in the characteristics of the researcher-students who are part of a program aimed explicitly at producing interdisciplinary, integrative thinkers in bio-engineering. The laboratories and learning settings are designed to move beyond the traditional model of collaboration among engineers, biologists, and medical