Models and Simulations 5, Helsinki, Finland, 14-16 June 2012
The emergence of systems biology is marked by a revival of mathematical modeling approaches to causal-mechanistic explanations and associated experimental practices in molecular biology. From a philosophical standpoint, this ‘mathematical turn’ in biology constitutes an excellent opportunity to investigate the relationship between deductive-nomological and causal-mechanistic accounts of scientific explanation. I argue that mathematical models in systems biology integrate substantial knowledge of molecular mechanisms with the application of laws, modeling and analysis strategies borrowed from chemistry, cybernetics and systems theory in order to yield quantitative mechanistic explanations. Mechanism schemas obtained by abstracting high-resolution biochemical details act as bridges between molecular mechanistic explanations and mathematical models of networks. In turn, mathematical models account for poorly understood aspects of biological phenomena, most notably minute quantitative-dynamic features. Thus, in actual scientific practice, deductive-nomological and casual-mechanistic approaches to explanation are not mutually exclusive, but complementary. Furthermore, mathematical models can reveal unsuspected ‘black boxes’ and motivate revisions of mechanistic explanations. This interplay between mechanistic explanations and their mathematical counterparts constitutes a progressive research approach that generates explanations of novel phenomena, and reveal strange properties of molecular mechanisms that have thus far escaped our attention.