Possible Rotation Projects:
- Build or extend mechanistic models of cytokine-driven JAK-STAT signaling in human macrophages, connecting signaling dynamics to gene expression and to responses to selective JAK inhibitors.
- Develop kinetic models of alphavirus replication in host cells, including the role of type I interferon, to identify steps most vulnerable to antiviral intervention.
- Develop and apply methods that use cell-to-cell variability in single-cell data (for example flow cytometry) to calibrate mechanistic models more effectively.
Training Technologies Used:
- Rule-based modeling (BioNetGen / BNGL); deterministic (ODE) and stochastic simulation, including network-free (NFsim) and hybrid particle/population methods; spatial reaction-diffusion simulation (MCell); parameter estimation, model reduction and uncertainty analysis (for example PyBioNetFit and parallel tempering); machine learning; scientific programming in Python; and analysis of single-cell data such as flow cytometry and transcriptomics.
- A.B. in Chemistry, Harvard College, 1991
- Ph.D. in Chemical Physics, University of Colorado at Boulder, 1998
- Postdoctoral training: Colorado State University (Physical Chemistry, 1999); Weizmann Institute of Science, Feinberg Fellow (Chemical Physics, 2001); Los Alamos National Laboratory, Director-funded Fellow (Theoretical Biology and Biophysics, 2003)
Prado DS, Kutuva AR, Cattley RT, et al. STAT3 S727 phosphorylation drives pathogenic Th17 differentiation and neuroinflammation in autoimmune disease. J Neuroinflammation. 2025;22:246.
Larkin CI, Dunn MD, Shoemaker JE, Klimstra WB, Faeder JR. A detailed kinetic model of Eastern equine encephalitis virus replication in a susceptible host cell. PLoS Comput Biol. 2025;21(6):e1013082.
Cheemalavagu N, Shoger KE, Cao YM, Michalides BA, Botta SA, Faeder JR, Gottschalk RA. Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework. Cell Syst. 2024;15(1):37–48.e4.
Husar A, Ordyan M, Garcia GC, Yancey JG, Saglam AS, Faeder JR, Bartol TM, Kennedy MB, Sejnowski TJ. MCell4 with BioNetGen: a Monte Carlo simulator of rule-based reaction-diffusion systems with Python interface. PLoS Comput Biol. 2024;20(4):e1011800.
Schmucker R, Farina G, Faeder J, Fröhlich F, Saglam AS, Sandholm T. Combination treatment optimization using a pan-cancer pathway model. PLoS Comput Biol. 2021;17(12):e1009689.
Gupta S, Lee REC, Faeder JR. Parallel tempering with Lasso for model reduction in systems biology. PLoS Comput Biol. 2020;16(3):e1007669.
Harris LA, Hogg JS, Tapia JJ, et al. BioNetGen 2.2: advances in rule-based modeling. Bioinformatics. 2016;32(21):3366–3368.
Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR. Exact hybrid particle/population simulation of rule-based models of biochemical systems. PLoS Comput Biol. 2014;10(4):e1003544.
Sneddon MW, Faeder JR, Emonet T. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods. 2011;8(2):177–183.
Faeder JR, Hlavacek WS, Reischl I, Blinov ML, Metzger H, Redondo A, Wofsy C, Goldstein B. Investigation of early events in FcεRI-mediated signaling using a detailed mathematical model. J Immunol. 2003;170(7):3769–3781.
My lab develops mathematical and computational models of the cell signaling and regulatory networks that control how cells respond to their environment, with a long-standing focus on the immune system. We specialize in rule-based modeling, an approach my group helped pioneer that encodes detailed knowledge of protein-protein interactions and modifications into mechanistic kinetic models in a systematic and biochemically intuitive way. Much of this work is built on and around BioNetGen, an open-source software framework we originated and continue to maintain that is now among the most widely used platforms for rule-based modeling worldwide.
We use these models to understand and ultimately predict how signaling networks shape cell behavior in health and disease. Current projects include cytokine-driven JAK-STAT signaling in macrophages, in collaboration with immunologist Dr. Rachel Gottschalk, where we combine mechanistic models with machine learning to predict how signaling dynamics control gene expression and how cells respond to targeted drugs such as JAK inhibitors; kinetic models of viral replication inside host cells, in collaboration with virologist Dr. William Klimstra, including alphaviruses such as Eastern equine encephalitis virus and the role of type I interferon in controlling infection; and new methods that use single-cell proteomic data to calibrate mechanistic models, in collaboration with immunologist Dr. William Hawse, who studies how immune signaling drives pathogenic cell differentiation. A recurring theme is translating mechanistic insight into strategies for treating human disease.
Methodologically, we develop new simulation algorithms (including network-free and spatial reaction-diffusion methods), rigorous approaches for calibrating and reducing models against experimental data, and workflows that integrate mechanistic modeling with machine learning and single-cell measurements. Students in my lab train at the interface of computational modeling, immunology and software development, and typically work closely with experimental collaborators, learning to build models that both explain data and drive new experiments.
