Dr. Gevertz: Publications

Jana Gevertz

Professor	Email: gevertz {at} tcnj {dot} edu
The College of New Jersey	Office: Science Complex P246
Department of Mathematics & Statistics	Phone: 609-771-3314

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Research Areas

Modeling and Optimizing Cancer Treatment Dynamics
Model-Informed Experimental Design
Virtual Clinical Trials
Other Projects: Mathematical Biology, Biostatistics, Computational Mathematics
Publications Related to Pedagogy, the Mathematics Community, and Career Advice

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Modeling and Optimizing Cancer Treatment Dynamics

The question of how to optimize a cancer treatment protocol is a highly nontrivial problem that involves much more than simply selecting the right subset of m drugs from a set of N drugs. Even once the m drugs have been identified, decisions must be made regarding the dose of, how long to give, and when to give, each drug. The number of possible combinations is simply too large to be tested experimentally, making mathematical modeling the ideal tool for optimizing protocol design so that efficacy is maximized while the likelihood of developing treatment resistance and toxic side effects is minimized. I have several projects that explore tumor response to treatment, and how that response can be optimally or robustly controlled.

Related Publications

J.L. Gevertz, J.M. Greene, S. Prosperi, N. Comandante-Lou and E.D. Sontag, 2025. Understanding therapeutic tolerance through a mathematical model of drug-induced resistance. npj Systems Biology and Applications 11: 30.
I. Kareva and J.L. Gevertz, 2024. Cytokine storm mitigation for exogenous immune agonists. Mathematics of Control, Signals, and Systems. 36: 329-350.
J.L. Gevertz and I. Kareva, 2024. Mitigating non-genetic resistance to checkpoint inhibition based on multiple states of immune exhaustion. npj Systems Biology and Applications 10: 14.
E. Nikolopoulou, S. Eikenberry, J.L. Gevertz and Y. Kuang, 2021. Mathematical modeling of an immune checkpoint inhibitor and its synergy with an immunostimulant. Discr. Contin. Dyn. Sys. Series B 26: 2133.
J.M. Greene, J.L. Gevertz and E.D. Sontag, 2019. Mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment. JCO Clin. Cancer Inform. 3: 1-20.
J.L. Gevertz and J.R. Wares, 2018. Developing a minimally structured mathematical model of cancer treatment with oncolytic viruses and dendritic cell injections. Comp. Math. Meth. Med. 2018: 8760371.
J.L. Gevertz, 2016. Microenvironment-mediated modeling of tumor response to vascular-targeting drugs. Adv. Exp. Med. Biol. 936:191-208.
J. Perez-Velazquez, J.L. Gevertz, A. Karolak and K.A. Rejniak, 2016. Microenvironmental niches and sanctuaries: a route to acquired resistance. Adv. Exp. Med. Biol. 936: 149-164.
A.B. Shah, K.A. Rejniak and J.L. Gevertz, 2016. Limiting the development of anti-cancer drug resistance in a spatial model of micrometastases. Mathem. Biosci. Eng. 13: 1185-1206.
J.R. Wares, J.J. Crivelli, C.O. Yun, I.K. Choi, J.L. Gevertz and P.S. Kim, 2015. Treatment strategies for combining immunostimulatory oncolytic virus therapeutics with dendritic cell injections. Mathem. Biosci. Eng. 12: 1237-1256.
J.L. Gevertz, Z. Aminzare, K. Norton, J. Perez-Velazquez, A. Volkening and K.A. Rejniak, 2015. Emergence of anti-cancer drug resistance: Exploring the importance of the microenvironmental niche and tumor heterogeneity through a spatial model. In "Applications of Dynamical Systems in Biology and Medicine”, IMA Volumes in Mathematics and its Applications, vol 158, Springer-Verlag, A. Radunskaya and T. Jackson (Eds.).
J.L. Gevertz, 2012. Optimization of vascular-targeting drugs in a computational model of tumor growth. Phys. Rev. E 85: 041914.
J.L. Gevertz, 2011. Computational modeling of tumor response to vascular-targeting therapies: I. Validation. Comp. Math. Meth. Med. 2011: 830515.

Related Talks

"Mitigating non-genetic resistance to checkpoint inhibition based on multiple states of exhaustion" at the Mathematical Modelling of Cancer Treatments, Resistance, Optimization Workshop held at the Fields Institute in September of 2024.

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Model-Informed Experimental Design

As exciting as the results from mathematical models can be, depending on how the data are structured and when they are collected, a variety of potential models (or model parametrizations) can often near equally well describe the same dataset. This phenomenon naturally leads to concern about having enough data to trust that the underlying assumptions of one’s model are “sufficiently correct” to answer the question of interest. Much of my more recent work has focused on developing methodologies to assess the reliability and robustness of model predictions in the context of noisy and incomplete data.

Related Publications

J.L. Gevertz and I. Kareva, 2024. Minimally sufficient experimental design using identifiability analysis. npj Systems Biology and Applications 10: 2.
J.L. Gevertz and I. Kareva, 2023. Guiding model-driven combination dose selection using multi-objective synergy optimization. CPT: Pharmacometrics & Systems Pharmacology 12: 1698-1713.
S.D. Cardenas, C.J. Reznik, R. Ranaweera, F. Song, C.H. Chung, E.J. Fertig and J.L. Gevertz, 2022. Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer. npj Systems Biology and Applications 8: 32.
M.C. Luo, E. Nikolopoulou and J.L. Gevertz, 2022. From fitting the average to fitting the individual: a cautionary tale for mathematical modelers. Frontiers in Oncology 12: 793908.

Related Talks

"Model driven experimental design with applications to cancer immunotherapy" at the Workshop on Mechanisitc Learning as a combination of Machine Learning and Modeling in Mathematical Oncology held at the Banff International Research Station for Mathematical Innovation and Discovery in January 2025.
"Guiding model-driven combination dose selection using multi-objective synergy optimization" at the Frontiers in Computational and Mathematical Medicine Workshop held at the Fields Institute in September of 2024.

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Virtual Clinical Trials

I am interested in using quantitative tools to better understand differential treatment response across cancer patients, and to identify strategies for those who do not respond well to standard-of-care protocols. One of the approaches for achieving these goals is to conduct “virtual clinical trials”. Clinical trials, which are the primary way that researchers find out if a new treatment is safe and effective, rely on human volunteers. Virtual clinical trials, on the other hand, use computer-based models to predict and quantify the uncertainty of the effects of therapy on disease progression.

Related Publications

J.L. Gevertz and J.R. Wares, 2024. Assessing the role of patient generation techniques in virtual clinical trial outcomes. Bulletin of Mathematical Biology 86: 119.
A Surendran, J. Le Sauteur-Robitaille, D. Kleimeier, J. Gevertz, K. Wilkie, A.L. Jenner and M. Craig, 2024. Approaches to generating virtual patient cohorts with applications in oncology. In Springer publication Personalised Medicine meets Artificial Intelligence, Springer Cham, A. Cesario, M.D'Oria, C. Auffray and G. Scambia (Eds.).
M. Craig, J.L. Gevertz, I. Kareva and K.P. Wilkie, 2023. A practical guide for the generation of model-based virtual clinical trials. Frontiers in Systems Biology 3: 1174647.
S. Barish, M.F. Ochs, E.D. Sontag and J.L. Gevertz, 2017. Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy. Proc. Natl. Acad. Sci., 114: E6277-E6286.

Related Talks

"Virtual expansion of populations for the analysis of robustness of therapies" at the Mathematical Modelling Approaches to Virtual Clinical Trials Workshop held virtually at the Career Innovation Hub sponsored by the Banff International Research Station in May of 2022.

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Other Projects: Mathematical Biology, Biostatistics, Computational Mathematics

Related Publications

J.R. Wares, J. Dong, J.L. Gevertz, A. Radunskaya, K. Viner, D. Wiebe, and S. Solomon, 2021. Predicting the impact of placing an overdose prevention site in Philadelphia: a mathematical modeling approach. Harm Reduction Journal 18: 110.
J. Gevertz, J.M. Greene, C.H. Sanchez-Tapia and E.D. Sontag, 2021. A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing. J. Theor. Biol. 510: 110539.
J.L. Gevertz and C. Wang, 2016. Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches. Stat. Appl. Genet. Mol. Biol. 15: 321-347.
J.L. Gevertz and S. Torquato, 2009. Growing heterogeneous tumors in silico. Phys. Rev. E 80: 051910.
J.L. Gevertz and S. Torquato, 2009. Mean survival times of absorbing triply periodic minimal surfaces. Phys. Rev. E 80: 011102.
J.L. Gevertz, G. Gillies and S. Torquato, 2008. Simulating tumor growth in confined heterogeneous environments. Phys. Biol. 5: 036010.
J.L. Gevertz and S. Torquato, 2008. A novel three-phase model of brain tissue microstructure. PLoS Comput. Biol. 4: e1000152.
J.L. Gevertz and S. Torquato, 2006. Modeling the effects of vasculature evolution on early brain tumor growth. J. Theor. Biol. 243: 517-531.
J.L. Gevertz, S. Dunn and C.M. Roth, 2005. Mathematical models of real-time PCR kinetics. Biotech. Bioeng. 92: 346-355.
J. Gevertz, H.H. Gan and T. Schlick, 2005. In vitro RNA random pools are not structurally diverse: a computational analysis. RNA 11: 853-863.

Related Talks

"What is a model, and how does it help us understand the COVID-19 pandemic?" at a virtual panel entitled A Discussion on COVID-19 Research sponsored by TCNJ Office of Alumni Engagement in February of 2021.

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Publications Related to Pedagogy, the Mathematics Community, and Career Advice

J.L. Gevertz, 2023. Synergizing teaching and research at primarily undergraduate institutions through student research. Notices of the American Mathematical Society 70: 598-600.
J.L. Gevertz and J.R. Wares, 2020. Fostering diversity in top-rated pure mathematics graduate programs. Notices of the AMS 67: 678-682.
J.L. Gevertz, P.S. Kim and J.R. Wares, 2017. Mentoring undergraduate interdisciplinary mathematics research students: junior faculty experiences. PRIMUS 27: 352-369.
J.C. Beier, J.L. Gevertz and K.E. Howard, 2015. Building context with tumor growth modeling projects in differential equations. PRIMUS 25: 297-325.: 051910.