James R. Faeder, Ph.D.

  • Associate Professor
  • Department of Computational Biology

Education & Training

  • Ph.D. in Chemical Physics from University of Colorado, 1998
  • A.B. in Chemistry from Harvard University, 1998

Research Interest Summary

algorithms and software for modeling biological regulatory processes that integrate specific knowledge about protein-protein interactions and apply these to improve our mechanistic understanding of biological systems.

Research Categories

Research Interests

My lab is interested in developing mathematical models of biological regulatory processes that integrate specific knowledge about protein-protein interactions. Together with collaborators at Los Alamos National Laboratory, we have developed a simulation framework called BioNetGen that allows rule-based specification of biochemical reaction networks and provides both deterministic and stochastic modeling capabilities. Current research includes the development of specific models of signal transduction and the development of new stochastic simulation algorithms that will greatly broaden the scope of models that can be developed. Other research areas include model reduction, parameter estimation and uncertainty analysis, and automated model construction from databases of protein interactions.

Representative Publications

Sneddon, M. W., Faeder, J. R. & Emonet, T. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat. Methods 8, (2011).

Sekar, J. A. P. & Faeder, J. R. Rule-based modeling of signal transduction: a primer. Methods Mol. Biol. 880, 139–218 (2012).

Miskov-Zivanov, N., Turner, M. S., Kane, L. P., Morel, P. A. & Faeder, J. R. The duration of T cell stimulation is a critical determinant of cell fate and plasticity. Sci. Signal. 6, ra97 (2013).

Donovan, R. M., Sedgewick, A. J., Faeder, J. R. & Zuckerman, D. M. Efficient stochastic simulation of chemical kinetics networks using a weighted ensemble of trajectories. J. Chem. Phys. 139, 115105 (2013).

Chylek, L. A. et al. Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems. Wiley Interdiscip. Rev. Syst. Biol. Med. 6, 13–36 (2013).

Hogg, J. S., Harris, L. A., Stover, L. J., Nair, N. S. & Faeder, J. R. Exact hybrid particle/population simulation of rule-based models of biochemical systems. PLoS Comput. Biol. 10, e1003544 (2014).

 Full List of Publications