2023- Postdoctoral Fellow, University of Iceland
2022-2023 Postdoctoral Associate, University of Minnesota and Ellison Institute for Transformative Medicine
2022 Ph.D., Industrial and Systems Engineering, University of Minnesota
2022 M.Sc., Mathematics, University of Minnesota
2010 B.Sc., Mathematics, University of Iceland
While cancer has traditionally been considered a genetic disease, it is now increasingly recognized that non-genetic mechanisms such as DNA methylation and histone modifications play an important role in tumor initiation, progression, and the evolution of drug resistance. These non-genetic processes frequently operate on faster timescales than genetic mutations and can therefore serve as a substrate for natural selection, shaping tumor evolution even in the absence of new genetic events. Anti-cancer treatment can also directly induce cancer cells to adopt non-genetic drug-tolerant ‘persister’ states, setting the stage for the evolution of resistance and eventual treatment failure.
Characterizing the biological mechanisms underlying persistence and the evolutionary process through which resistance emerges has proven challenging. My work uses mathematical modeling to help shed light on these processes and to understand how they may be most effectively targeted. For example, we have shown that longitudinal cell count data alone can be used to detect drug-induced persistence in the absence of any prior biological evidence, and to estimate the rate at which the cancer cells adopt the persister phenotype [1]. We have also shown how this information can be used to design dosing schedules that outperform conventional maximum tolerated dose therapy [2].
In future work, I aim to integrate mathematical modeling more closely with experimental methods to develop a framework for understanding and targeting drug-induced persistence across cancer types. I am also interested in developing models that can support clinical decision making in settings where longitudinal data are sparse and tumor dynamics are only partially observed.
- Gunnarsson, E.B., Magnússon, B.V., and Foo, J. (2025). Optimal dosing of anti-cancer treatment under drug-induced plasticity. npj Systems Biology and Applications, 11, 98, doi.org/10.1038/s41540-025-00571-5.
- Wu, C., Gunnarsson, E.B., Foo, J., and Leder, K. (2025). A statistical framework for detecting therapy-induced resistance from drug screens. npj Systems Biology and Applications, 11, 88, doi.org/10.1038/s41540-025-00560-8.
- Gunnarsson, E.B. and Foo, J. (2025). Mathematical models of resistance evolution under continuous and pulsed anti-cancer therapies. In Cancer Systems Biology: Translational Mathematical Oncology, Oxford University Press. Editors: Ravi Salgia, Mohit Kumar Jolly, Prakash Kulkarni, and Govindan Rangarajan. ISBN: 9780192867636.
- Gunnarsson, E.B., Leder, K., and Zhang, X. (2025). Limit theorems for the site frequency spectrum of neutral mutations in an exponentially growing population. Stochastic Processes and their Applications, 182, 104565, doi.org/10.1016/j.spa.2025.104565.
- Gunnarsson, E.B., Kim, S., Choi, B., Schimd, J.K., Kaura, K., Lenz, H-J, Mumenthaler, S., and Foo, J. (2024). Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework. PLoS Computational Biology, 20(8), e1012256, doi.org/10.1371/journal.pcbi.1012256.
- Gunnarsson, E.B., Foo, J., and Leder, K. (2023). Statistical inference of the rates of cell proliferation and phenotypic switching in cancer. Journal of Theoretical Biology, 568, 111497, doi.org/10.1016/j.jtbi.2023.111497.
- Gunnarsson, E.B., Leder, K., and Foo, J. (2021). Exact site frequency spectra of neutrally evolving tumors: A transition between power laws reveals a signature of cell viability. Theoretical Population Biology, 142, 67-90, doi:10.1016/j.tpb.2021.09.004.
- Gunnarsson, E.B., De, S., Leder, K., and Foo, J. (2020). Understanding the role of phenotypic switching in cancer drug resistance. Journal of Theoretical Biology, 490, 110162, doi:10.1016/j.jtbi.2020.110162.