The main objective of my research program is to elucidate the complex epistemic relationships between mechanistic explanations in molecular biology and associated wet- lab experimental practices, and newly developed systems biology models and associated bioinformatics approaches. More specifically, I aim to gain a better understanding of how complex systems of molecular mechanisms can be modeled in a computationally efficient way in order to make possible novel predictions about the overall behavior of cells and organisms over extended periods of time, as well as predictions about disease progression and other dynamic aspects of biological phenomena; and how mathematical models of disease and other biological phenomena can provide new insights about the causal processes responsible for producing these diseases and phenomena. In addition to providing better predictions about disease progression and unwanted side-effects of treatments, the integration of mathematical modeling in molecular biology may also reveal thus far unsuspected causal factors, the investigation of which will eventually lead to the development of new treatments, new experimental techniques and practical applications. I am particularly interested in elucidating the connection between novel, quantitative models of genomic contributions to phenotypes, such as gene regulatory networks (GRNs) and abstract/schematic representations of mechanisms of genome expression. Using gene molecular networks as study cases, I aim to investigate how knowledge of molecular mechanisms contributes to the models devised by systems biologists, and vice versa: what kind of knowledge about molecular mechanisms can be extracted from quantitative models derived from the analysis of large bodies of genomic, transcriptomic, and proteomic data. Several GRNs have been elucidated in great detail, and, at least in some cases, there is a substantial available about the biochemical details of the molecular mechanisms underlying them. At the same time, several GRN modeling strategies, as well as quantitative models of actual GRNs are available in the scientific literature. Thus, GRNs constitute a suitable study case for investigating the relationship between molecular mechanisms and more mathematical models associated with systems biology.