Dr. Faeder received an A.B. degree in Chemistry from Harvard College in 1991 and his Ph.D. in Chemical Physics from the University of Colorado at Boulder in 1998. The main focus of his thesis work and subsequent postdoctoral research with Drs. Branka Ladanyi at Colorado State University and David Tannor at the Weizmann Institute of Science was to understand how solute-solvent interactions govern chemical dynamics. Methods employed included classical molecular dynamics simulations, mixed quantum-classical simulations, and exact quantum simulations using wavepackets. Results from simulations were compared with experimental measurements obtained using various forms of time-dependent nonlinear spectroscopy on the femtosecond and picsosecond timescales.
In 1999, Dr. Faeder moved to Los Alamos National Laboratory where he was a Director-funded Postdoctoral Fellow with the Cell Signaling Team headed by Dr. Byron Goldstein, which was dedicated to the quantitative modeling of signal transduction in biological systems. Although this shift to biology represented a major change in research direction, Dr. Faeder was drawn by the similarity between the protein-protein interactions that drive complex formation in signal transduction and the atomic interactions that govern molecular dynamics. This led Dr. Faeder to seek methods for simulating the dynamics of cell signaling processes that would not be hindered by the combinatorial complexity that bogs down standard approaches to modeling the chemical kinetics of highly heterogeneous systems. Together with Drs. Michael Blinov and William Hlavacek, he developed the BioNetGen modeling language and software, which employs an object-oriented and rule-based description of the molecular interactions that drive chemical kinetics of biological systems. By allowing the modeler to describe molecular interactions in compact form as rules, BioNetGen frees the modeler from the tedious process of enumerating the multitude of signaling complexes that can arise and from making ad hoc assumptions to avoid consideration of these complexes. BioNetGen has been applied to model a number of critical signal transduction systems, including the immune-recognition and growth factor receptors. The rule-based approach has also proven useful in modeling the fate of isotopically-labeled compounds in large metabolic networks.
Dr. Faeder's group is a diverse, multidisciplinary team whose current research focuses on the development of faster simulation algorithms, with the ultimate aim being to enable simulation of the entire network of signaling interactions within a cell. Achievement of this goal will also require the development of new software frameworks to facilitate the assembly and management of comprehensive, rule-based models, through the formalization of existing biological knowledge. In addition, these models pose a major challenge for existing methods of parameter identification and estimation. The major impetus for these developments is provided by applications of rule-based modeling to specific systems of biomedical relevance in collaboration with groups conducting experiments.