My lab is interested in developing mathematical models of biological regulatory processes that integrate specific knowledge about protein-protein interactions. We develop and maintain 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.
I am also the Pitt co-director of the Joint Carnegie Mellon – University of Pittsburgh PhD Program in Computational Biology.
Sekar, JAP, and Faeder, JR (2017) An Introduction to Rule-based Modeling of Immune Receptor Signaling. In Systems Immunology: An Introduction to Modeling Methods for Scientists, J. Das and C. Jayaprakash, Eds. Taylor and Francis, ISBN: 978-1498717403. (preprint)
Gupta, S, et al. (2017) Spatial Stochastic Modeling with MCell and CellBlender, B. Munsky et al., Eds. q-bio Textbook, in press. (preprint)
Sekar, JAP, Tapia JJ, and Faeder JR (2017) Visualizing Regulation in Rule-based Models. bioRxiv doi: (preprint)
Sekar JAP, Hogg, JS, and Faeder JR (2017) Energy-based Modeling in BioNetGen. Proceedings – 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (link)
Donovan, RM et al. (2016) Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories. PLOS Computational Biology 12, e1004611. doi:10.1371/journal.pcbi.1004611. [In collaboration with D. M. Zuckerman] (link)
Hawse, WF, et al. (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. [In collaboration with P. A. Morel] (link)