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.
Miskov-Zivanov N, Turner MS, Kane LP, Morel PA, Faeder JR (2013) The duration of T cell stimulation is a critical determinant of cell fate and plasticity. Sci. Signal. 6, ra97. (link)
Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR (2014) Exact hybrid particle/population simulation of rule-based models of biochemical systems. PLOS Comp. Biol. 10, e1003544. (link)
Morel PA, Faeder JR, Hawse WF, Miskov-Zivanov N (2014) Modeling the T cell immune response: a fascinating challenge. J Pharmacokinet. Pharmacodyn. 41, 401-413. (link)
Bartol TM, Dittrich M, and Faeder JR(2015) MCell. In Encyclopedia of Computational Neuroscience, D. Jaeger and R. Jung, Eds. ISBN: 978-1-4614-7320-6, Springer New York, pp. 1673–1676. (link)
Chylek, LA, Harris, LA, Faeder, JR, Hlavacek, WS (2015) Modeling for (physical) biologists: an introduction to the rule-based approach. Physical Biology, in press.
Hawse, WF, Sheehan, RP, Miskov-Zivanov, N, Menk, AV, Kane, LP, Faeder, JR, Morel, PA (2015) Cutting Edge: Differential regulation of PTEN by TCR, Akt and FoxO1 controls CD4+ T cell fate decisions. J. Immunol., doi:10.4049/jimmunol.1402554. (link)