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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Publications
*Indicates undergraduate co-author
A Surendran, J. Le Sauteur-Robitaille, D. Kleimeier, J. Gevertz, K. Wilkie, A.L. Jenner and M. Craig.
Approaches to generating virtual patient cohorts with applications in oncology
. To appear in Springer publication
Personalised Medicine meets Artificial Intelligence - Blitz Along the Paradigm Shift
.
I. Kareva and J.L. Gevertz, 2023.
Cytokine storm mitigation for exogenous immune agonists
.
Mathematics of Control, Signals, and Systems
. doi: 10.1007/s00498-023-00362-5
J.L. Gevertz and I. Kareva, 2023.
Guiding model-driven combination dose selection using multi-objective synergy optimization
.
CPT: Pharmacometrics & Systems Pharmacology
, 1-16. doi: 10.1002/psp4.12997.
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.
J.L. Gevertz, 2023.
Synergizing teaching and research at primarily undergraduate institutions through student research
.
Notices of the American Mathematical Society
70
: 598-600.
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.
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.
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. 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 J.R. Wares, 2020.
Fostering diversity in top-rated pure mathematics graduate programs
.
Notices of the AMS
67
: 678-682.
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.
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.
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.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.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.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.C. Beier, J.L. Gevertz and K.E. Howard, 2015.
Building context with tumor growth modeling projects in differential equations
.
PRIMUS
25
: 297-325.
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.
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.