@article{murad_tran_banks_everett_rosenberg_2019, title={IMMUNOSUPPRESSANT TREATMENT DYNAMICS IN RENAL TRANSPLANT RECIPIENTS: AN ITERATIVE MODELING APPROACH}, volume={24}, ISSN={["1553-524X"]}, DOI={10.3934/dcdsb.2018274}, abstractNote={Finding the optimal balance between over-suppression and under-suppression of the immune response is difficult to achieve in renal transplant patients, all of whom require lifelong immunosuppression. Our ultimate goal is to apply control theory to adaptively predict the optimal amount of immunosuppression; however, we first need to formulate a biologically realistic model. The process of quantitively modeling biological processes is iterative and often leads to new insights with every iteration. We illustrate this iterative process of modeling for renal transplant recipients infected by BK virus. We analyze and improve on the current mathematical model by modifying it to be more biologically realistic and amenable for designing an adaptive treatment strategy.}, number={6}, journal={DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B}, author={Murad, Neha and Tran, H. T. and Banks, H. T. and Everett, R. A. and Rosenberg, Eric S.}, year={2019}, month={Jun}, pages={2781–2797} }
 @article{banks_hu_rosenberg_2017, title={A dynamical modeling approach for analysis of longitudinal clinical trials in the presence of missing endpoints}, volume={63}, ISSN={["0893-9659"]}, DOI={10.1016/j.aml.2016.07.002}, abstractNote={Randomized longitudinal clinical trials are the gold standard to evaluate the effectiveness of interventions among different patient treatment groups. However, analysis of such clinical trials becomes difficult in the presence of missing data, especially in the case where the study endpoints become difficult to measure because of subject dropout rates or/and the time to discontinue the assigned interventions are different among the patient groups. Here we report on using a validated mathematical model combined with an inverse problem approach to predict the values for the missing endpoints. A small randomized HIV clinical trial where endpoints for most of patients are missing is used to demonstrate this approach.}, journal={APPLIED MATHEMATICS LETTERS}, author={Banks, H. T. and Hu, Shuhua and Rosenberg, Eric}, year={2017}, month={Jan}, pages={109–117} }
 @article{kepler_banksa_davidian_rosenberg_2009, title={A model for HCMV infection in immunosuppressed patients}, volume={49}, ISSN={["1872-9479"]}, DOI={10.1016/j.mcm.2008.06.003}, abstractNote={We propose a model for HCMV infection in healthy and immunosuppressed patients. First, we present the biological model and formulate a system of ordinary differential equations to describe the pathogenesis of primary HCMV infection in immunocompetent and immunosuppressed individuals. We then investigate how clinical data can be applied to this model. Approximate parameter values for the model are derived from data available in the literature and from mathematical and physiological considerations. Simulations with the approximated parameter values demonstrates that the model is capable of describing primary, latent, and secondary (reactivated) HCMV infection. Reactivation simulations with this model provide a window into the dynamics of HCMV infection in (D-R+) transplant situations, where latently-infected recipients (R+) receive transplant tissue from HCMV-naive donors (D-).}, number={7-8}, journal={MATHEMATICAL AND COMPUTER MODELLING}, author={Kepler, G. M. and Banksa, H. T. and Davidian, M. and Rosenberg, E. S.}, year={2009}, month={Apr}, pages={1653–1663} }
 @article{rosenberg_davidian_banks_2007, title={Using mathematical modeling and control to develop structured treatment interruption strategies for HIV infection}, volume={88}, ISSN={["1879-0046"]}, DOI={10.1016/j.drugalcdep.2006.12.024}, abstractNote={The goal of this article is to suggest that mathematical models describing biological processes taking place within a patient over time can be used to design adaptive treatment strategies. We demonstrate using the key example of treatment strategies for human immunodeficiency virus type-1 (HIV) infection. Although there has been considerable progress in management of HIV infection using highly active antiretroviral therapies, continuous treatment with these agents involves significant cost and burden, toxicities, development of drug resistance, and problems with adherence; these latter complications are of particular concern in substance-abusing individuals. This has inspired interest in structured or supervised treatment interruption (STI) strategies, which involve cycles of treatment withdrawal and re-initiation. We argue that the most promising STI strategies are adaptive treatment strategies. We then describe how biological mechanisms governing the interaction over time between HIV and a patient's immune system may be represented by mathematical models and how control methods applied to these models can be used to design adaptive STI strategies seeking to maintain long-term suppression of the virus. We advocate that, when such mathematical representations of processes underlying a disease or disorder are available, they can be an important tool for suggesting adaptive treatment strategies for clinical study.}, journal={DRUG AND ALCOHOL DEPENDENCE}, author={Rosenberg, Eric S. and Davidian, Marie and Banks, H. Thomas}, year={2007}, month={May}, pages={S41–S51} }