Dr. Gevertz  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

  1. 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.

  2. M.C. Luo*, E. Nikolopoulou and J.L. Gevertz, 2022. From fitting the average to fitting the individual: a cautionary tale for mathematical modelersFrontiers in Oncology 12: 793908.

  3. 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.

  4. 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.

  5. 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 distancingJ. Theor. Biol510: 110539. 

  6. J.L. Gevertz and J.R. Wares, 2020. Fostering diversity in top-rated pure mathematics graduate programs. Notices of the AMS 67: 678-682.

  7. 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.

  8. 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.

  9. 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.

  10. J.L. Gevertz, P.S. Kim and J.R. Wares, 2017. Mentoring undergraduate interdisciplinary mathematics research students: junior faculty experiences. PRIMUS 27: 352-369.

  11. J.L. Gevertz, 2016. Microenvironment-mediated modeling of tumor response to vascular-targeting drugs. Adv. Exp. Med. Biol. 936:191-208.

  12. J. Perez-Velazquez, J.L. Gevertz, A. Karolak and K.A. Rejniak, 2016. Microenvironmental niches and sanctuaries: a route to acquired resistanceAdv. Exp. Med. Biol. 936: 149-164. 

  13. 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.

  14. 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.

  15. 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.

  16. 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.).

  17. 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.

  18. J.L. Gevertz, 2012. Optimization of vascular-targeting drugs in a computational model of tumor growthPhys. Rev. E 85: 041914. 

  19. J.L. Gevertz, 2011. Computational modeling of tumor response to vascular-targeting therapies: I. Validation. Comp. Math. Meth. Med. 2011: 830515.

  20. J.L. Gevertz and S. Torquato, 2009. Growing heterogeneous tumors in silico. Phys. Rev. E 80: 051910.   

  21. J.L. Gevertz and S. Torquato, 2009. Mean survival times of absorbing triply periodic minimal surfaces. Phys. Rev. E 80: 011102.

  22. J.L. Gevertz, G. Gillies and S. Torquato, 2008. Simulating tumor growth in confined heterogeneous environments. Phys. Biol. 5: 036010.

  23. J.L. Gevertz and S. Torquato, 2008. A novel three-phase model of brain tissue microstructure. PLoS Comput. Biol. 4: e1000152.

  24. J.L. Gevertz and S. Torquato, 2006. Modeling the effects of vasculature evolution on early brain tumor growth. J. Theor. Biol. 243: 517-531.

  25. J.L. Gevertz, S. Dunn and C.M. Roth, 2005. Mathematical models of real-time PCR kineticsBiotech. Bioeng. 92: 346-355.

  26. J. Gevertz, H.H. Gan and T. Schlick, 2005. In vitro RNA random pools are not structurally diverse: a computational analysisRNA 11: 853-863.