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Dr. Hana M. Dobrovolny

Associate Professor Main: (817) 257-6379
Ph.D (Physics) Duke University, 2008 M.A (Physics) Bryn Mawr College, 2000 B.Sc (Physics and Mathematics) University of Winnipeg, 1997
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Personal webpage

Dr. Dobrovolny received a B.Sc in Physics and Mathematics from University of Winnipeg, a M.A in Physics from Bryn Mawr College and a Ph.D in Physics from Duke University. She discovered her interest in the interdisciplinary field of biophysics early in her career, pursuing projects researching neural signals for her Master’s degree and cardiac electrophysiology during her Ph.D. This was followed by a postdoctoral fellowship at Ryerson University where she began her research on infectious diseases. Dr. Dobrovolny joined the faculty at TCU in 2012.

Research Interests

Computational Biophysics

My research uses mathematical models and computer simulations to understand and predict the behaviour of biological systems. I am particularly interested in studying disease processes and potential therapies or cures. The experiments and clinical trials used to study many diseases are very costly and time-consuming and the data we get are usually quite limited, so it’s often difficult to get a clear picture of which biological processes are important in causing a disease. This also makes it difficult to study different treatment regimens. By the time a drug makes it to a clinical trial, usually only a couple of different dose/timing regimens are tested in humans; not because they were found to be the optimal regimens after a thorough examination of all the possibilities, but typically based on the educated guess of the researchers heading the trial. An accurate computer model of the disease can not only help us understand the underlying dynamics of the disease but will be extremely helpful in assessing potential treatments. Computers can simulate thousands of different dose/timing regimens and will help doctors choose optimal regimens to test in patients.

Viral infections

Viral infections affect millions of people every year. Most often, viral illnesses are not serious and resolve on their own, but some viruses can cause severe disease and death. I use mathematical models of the infection process to study the causes of severe influenza, the emergence of drug resistance, the role of the immune response in clearing the infection, and antiviral treatment. The long-term goal of this research is to develop an accurate model of the infection in humans which can then be used to test a wide variety of drug treatment protocols and to simulate drug or vaccine treatment in high risk patients, reducing the risk to these patients.


Cancer is a family of illnesses characterized by uncontrolled growth of cells. I use mathematical models to describe the growth of tumor cells and to make predictions about how treatment will affect these cells. In addition to characterizing chemotherapy treatment, we are using models to study treatment of cancer using cancer-killing viruses. This therapy has the potential to cure cancer with less side effects than current therapies.

Courses Taught

  • PHYS 10154 General Physics I
  • PHYS 10164 General Physics II
  • PHYS 10164 General Physics II Labs
  • PHYS 20474 Physics I
  • PHYS 20484 Physics II
  • PHYS 20484 Physics II Labs
  • PHYS 30491 Physics III Lab
  • PHYS 50753 Topics in Biophysics
  • PHYS 60403, Electrodynamics I
  • PHYS 60413 Electrodynamics II
  • PHYS 60803, Nonlinear Dynamics with Applications

Selected Publications

  1. B. Tuladhar, H.M. Dobrovolny, (2019) `Testing the limits of cardiac electrophysiology models through systematic variation of current,’ submitted to AIMS Mathematics.
  2. T. Rodriguez, H.M. Dobrovolny, (2019) `Quantifying the effect of trypsin and elastase on in vitro SARS infections,’ submitted to Scientific Reports.
  3. T. Rodriguez, G. Gonzalez-Parra, H.M. Dobrovolny, (2019) ‘Quantifying the effect of trypsin on in vitro influenza infections,’ submitted to Plos One.
  4. G. Gonzalez-Parra, H.M. Dobrovolny, (2019) `The rate of viral transfer between upper and lower respiratory tracts determines RSV illness duration,’ Journal of Mathematical Biology.
  5. L. Pinky, G. Gonzalez-Parra, H.M. Dobrovolny, (2019) `Effect of stochasticity on coinfection dynamics of respiratory viruses,’ BMC Bioinformatics, 20:191.
  6. L. Pinky, G. Gonzalez-Parra, H.M. Dobrovolny, (2019) `Superinfection and cell regeneration can lead to chronic viral coinfections’, Journal of Theoretical Biology 466:24-38
  7. K. Melville, T. Rodriguez, H.M. Dobrovolny, (2018) `Investigating different mechanisms of action in combination therapy for influenza,’ Frontiers in Pharmacology 9:1207.
  8. G. Gonzalez-Parra, H.M. Dobrovolny, (2018) `A quantitative assessment of dynamical differences of RSV infections in vitro and in vivo,’ Virology 523:129-139.
  9. G. Gonzalez-Parra, H.M. Dobrovolny, (2018) `Modeling of fusion inhibitor treatment of RSV in African green monkeys,’ J. Theor. Biol. 456:62-73.
  10. Gonzalez-Parra, F. De Ridder, D. Huntjens, D. Roymans, G. Ispas, H.M. Dobrovolny (2018) `A comparison of RSV and influenza in vitro kinetic parameters reveals differences in infecting time’, PLOS One 13(2):e0192645.
  11. L.A.C. Deecke, H.M. Dobrovolny, (2018) `Intermittent treatment of influenza’, J. Theor. Biol. 442:129-138.
  12. Gonzalez-Parra, H.M. Dobrovolny, D.F. Aranda, B.M. Chen-Charpentier, R. Guerrero (2018) `Quantifying rotavirus kinetics in the REH tumor cell line’, Virus Res. 244:53-63.
  13. H.M. Dobrovolny, C.A.A. Beauchemin (2017), `Modeling the emergence of influenza drug resistance: The roles of surface proteins, the immune response and antiviral mechanisms,’ Plos One 12(7): e0180582
  14. L. Pinky, H.M. Dobrovolny (2017), `The impact of cell regeneration on the dynamics of viral coinfection,’ Chaos 27:063109.
  15. J. Palmer, H.M. Dobrovolny, C.A.A. Beauchemin (2017), ‘The in vivo efficacy of neuraminidase inhibitors cannot be determined from the decay of influenza viral titers observed in treated patients,’ Sci. Rep. 7:40210
  16. L.Pinky, H.M. Dobrovolny (2016), `Coinfections of the respiratory tract: Viral competition for resources’, PLoS One 11(5):e0155589
  17. Gonzalez-Parra, T. Rodriguez, H.M. Dobrovolny (2016), `A comparison of methods for extracting influenza viral titer characteristics’, J. Virol. Meth, 231:14-24.
  18. Murphy, H. Jaafari, H.M. Dobrovolny (2016), ‘Differences in predictions of ODE models of tumor growth: A cautionary example’, BMC Cancer, 16(1):163
  19. G.C. Gonzalez-Parra, H.M. Dobrovolny (2015), `Assessing uncertainty in A2 respiratory syncytial virus (RSV) viral dynamics.’ Accepted in Comp. Math. Meth. Med.
  20. N.F. Beggs, H.M. Dobrovolny (2015), `Using mathematical models to determine the maximum efficacy of influenza drugs.’ J. Biol. Dyn. 9(S1):332-346
  21. H.M. DobrovolnyM.B. Reddy, M.A. Kamal, C.R. Rayner, C.A.A. Beauchemin (2013), `Assessing mathematical models of influenza infections using features of the immune response’ PLoS One 8(2):e57088.
  22. H.M. Dobrovolny, R. Gieschke, B.E. Davies, N.L. Jumbe, C.A.A. Beauchemin (2011) `Neuraminidase inhibitors for treatment of human and avian strain influenza: A comparative modeling study’ J. Theor. Biol. 269:234-244.
  23. H.M. Dobrovolny, M.J. Baron, R. Gieschke, B.E. Davies, N.L. Jumbe, and C.A.A. Beauchemin (2010) `Exploring cell tropism as a possible contributor to influenza infection severity.’ PLoS One, 5(11):e13811.