Update Your ProfileEducation and Training
California State University, Long Beach, BS, Mathematics, 2012
University of California, Irvine, MS, Mathematics, 2016
University of California, Irvine, PhD, Mathematics, 2020
University of Michigan, Ann Arbor, Postdoctoral Assistant Professor, 2023
Johns Hopkins University, Postdoctoral Fellow, 2024
Biosketch
Dr. Bergman develops agent-based and mathematical models to investigate tumor-immune dynamics at the cell and tissue scale. His research integrates multiomics data to ground simulations in biological reality, with the goal of informing therapeutic interventions and advancing precision oncology.
Research/Clinical Keywords
agent-based models, surrogate models, mathematical oncology, tumor-immune interactions, cancer systems biology, computational biology, multi-scale modeling, spatial transcriptomics
Highlighted Publications
Bergman, D.R., Johnson, J., Naji, M., Booth, M., Lima da Rocha, H., Deshpande, A., Sidiropoulos, D.N., Lopez-Vidal, T., Heiland, R., Kagohara, L.T. and Anders, R.A., 2025. BIWT: a bioinformatics walkthrough for embedding spatial multiomics in agent-based models for virtual cells. Bioinformatics, p.btaf571.
Johnson, J.A., Bergman, D.R. , Rocha, H.L., Zhou, D.L., Cramer, E., Mclean, I.C., Dance, Y.W., Booth, M., Nicholas, Z., Lopez-Vidal, T. and Deshpande, A., 2025. Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories. Cell, 188(17), pp.4711-4733.
Bergman, D.R., Wang, Y., Trujillo, E., Fernald, A.A., Li, L., Pearson, A.T., Sweis, R.F. and Jackson, T.L., 2024. Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model. Frontiers in Immunology, 15, p.1358019.
Bergman, D.R., Norton, K.A., Jain, H.V., Jackson, T.L., 2024. Connecting Agent-based Models with High-dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach. Bulletin of Mathematical Biology, 86 (1), pp.1-28.
Research Interests
My research focuses on developing agent-based models (ABMs) to explore tumor–immune interactions and their modulation by therapeutic interventions. A central theme is integrating multiomics and spatially resolved data into these models to create biologically grounded simulations that capture the complexity of the tumor microenvironment. We are building workflows that interface omics-derived insights with the rules-based architecture of ABMs, enabling models to reflect patient-specific cellular behaviors and spatial organization.
These efforts aim to advance data-driven mechanistic modeling as a complement to AI approaches, providing predictive frameworks for spatiotemporal tumor evolution under diverse treatment regimens. Ultimately, this work seeks to lay the foundation for digital twin strategies in oncology, where computational models inform precision therapies and hypothesis generation.