The emergence of systems biology is marked by a revival of mathematical modeling in life sciences, hinting towards more theoretical alternatives to current causal-mechanistic explanations and experimental practices in molecular biology. I investigate the possibility and benefits of an integration of mathematical modeling in molecular biology. More specifically, I aim to gain a better understanding of (i) how complex systems of molecular mechanisms can be modeled in a computationally efficient way in order to make possible quantitative predictions; and (ii) how mathematical models can provide new insights about the causal processes responsible for producing biological phenomena. I argue that mechanism schemas obtained by abstracting high-resolution biochemical details act as bridges between molecular mechanistic explanations and mathematical models of networks. Mechanism schemas determine the structure of networks, thus generating a structural similarity between mechanisms and networks, while abstraction is important in order to generate networks simple enough to allow for computable solutions. In turn, quantitative models elucidate poorly understood aspects of molecular mechanisms, most notably minute quantitative and dynamic aspects of the biological phenomenon generated by molecular mechanisms. Furthermore, quantitative models reveal novel and strange properties of molecular mechanisms, supporting the view that molecular biology and systems biology elucidate different aspects of molecular mechanisms.
Tudor Baetu holds degrees in Biology and Philosophy of Biology. He obtained a M.Sc. degree in Molecular Biology from McGill University (2001, Expression of Cytokine and Apoptotic Genes: a role for NF-κB in the regulation of TNF-α Related Apoptosis Inducing Ligand (TRAIL) expression), where he worked on a project concerning the regulation of immune responses in cancer and HIV infection. He finished his Ph.D. in Philosophy at the Université de Montréal (2009), under the supervision of Prof. Yvon Gauthier. In his dissertation (Strategies of Empirical Justification in Experimental Science), he investigated the experimental constraints on the formulation and confirmation of hypotheses using genetics as a study case. From 2008 to 2011 he worked at the University of Maryland on a project concerning the evolution of the concept of the gene from classical genetics, to molecular biology, to present-day genomics.