@article{myers_droz_rogers_tran_flores_chan_knechtle_jackson_luo_chambers_et al._2025, title={Modeling BK Virus Infection in Renal Transplant Recipients}, volume={17}, ISSN={["1999-4915"]}, url={https://doi.org/10.3390/v17010050}, DOI={10.3390/v17010050}, abstractNote={Kidney transplant recipients require a lifelong protocol of immunosuppressive therapy to prevent graft rejection. However, these same medications leave them susceptible to opportunistic infections. One pathogen of particular concern is human polyomavirus 1, also known as BK virus (BKPyV). This virus attacks kidney tubule epithelial cells and is a direct threat to the health of the graft. Current standard of care in BK virus-infected transplant recipients is reduction in immunosuppressant therapy, to allow the patient’s immune system to control the virus. This requires a delicate balance; immune suppression must be strong enough to prevent rejection, yet weak enough to allow viral clearance. We seek to model viral and immune dynamics with the ultimate goal of applying optimal control methods to this problem. In this paper, we begin with a previously published model and make simplifying assumptions that reduce the number of parameters from 20 to 14. We calibrate our model using newly available patient data and a detailed sensitivity analysis. Numerical results for multiple patients are given to show that the newer model reflects observed dynamics well.}, number={1}, journal={VIRUSES-BASEL}, author={Myers, Nicholas and Droz, Dana and Rogers, Bruce W. and Tran, Hien and Flores, Kevin B. and Chan, Cliburn and Knechtle, Stuart J. and Jackson, Annette M. and Luo, Xunrong and Chambers, Eileen T. and et al.}, year={2025}, month={Jan} }
 @article{banks_banks_myers_laubmeier_bommarco_2020, title={Lethal and sublethal effects of toxicants on bumble bee populations: a modelling approach}, volume={29}, ISSN={["1573-3017"]}, DOI={10.1007/s10646-020-02162-y}, abstractNote={Abstract Pollinator decline worldwide is well-documented; globally, chemical pesticides (especially the class of pesticides known as neonicotinoids) have been implicated in hymenopteran decline, but the mechanics and drivers of population trends and dynamics of wild bees is poorly understood. Declines and shifts in community composition of bumble bees (Bombus spp .) have been documented in North America and Europe, with a suite of lethal and sub-lethal effects of pesticides on bumble bee populations documented. We employ a mathematical model parameterized with values taken from the literature that uses differential equations to track bumble bee populations through time in order to attain a better understanding of toxicant effects on a developing colony of bumble bees. We use a delay differential equation (DDE) model, which requires fewer parameter estimations than agent-based models while affording us the ability to explicitly describe the effect of larval incubation and colony history on population outcomes. We explore how both lethal and sublethal effects such as reduced foraging ability may combine to affect population outcomes, and discuss the implications for the protection and conservation of ecosystem services.}, number={3}, journal={ECOTOXICOLOGY}, author={Banks, J. E. and Banks, H. T. and Myers, N. and Laubmeier, A. N. and Bommarco, R.}, year={2020}, month={Apr}, pages={237–245} }
 @article{stojsavljevic_pinter_lauko_myers_2019, title={PARAMETER IDENTIFICATION AND SENSITIVITY ANALYSIS FOR A PHYTOPLANKTON COMPETITION MODEL}, volume={77}, ISSN={["1552-4485"]}, DOI={10.1090/qam/1514}, abstractNote={Phytoplankton live in a complex environment with two essential resources, light and nutrients, forming various gradients. Light supplied from above is never homogeneously distributed in a body of water due to refraction and absorption from biomass present in the ecosystem and other sources. Nutrients in turn are typically supplied from below mixed-up by diffusion from the benthic region. Here we present a model of two phytoplankton species competing in a deep freshwater lake for light and two nutrients, one of which is assumed to be preferred. The model is comprised of a system of non- linear, non-local partial differential equations with appropriate boundary conditions. The parameter space of the model is analyzed for parameter identifiability - the ability for a parameter’s true value to be recovered through optimization, and for global sensitivity - the influence a parameter has on model response. The results of these analyses are interpreted within their biological context.}, number={1}, journal={QUARTERLY OF APPLIED MATHEMATICS}, author={Stojsavljevic, Thomas and Pinter, Gabriella and Lauko, Istvan and Myers, Nicholas}, year={2019}, month={Mar}, pages={1–18} }
 @article{banks_baraldi_catenacci_myers_2016, title={Parameter Estimation Using Unidentified Individual Data in Individual Based Models}, volume={11}, ISSN={["1760-6101"]}, DOI={10.1051/mmnp/201611602}, abstractNote={In physiological experiments, it is common for measurements to be collected from multiple subjects.Often it is the case that a subject cannot be measured or identified at multiple time points (referred to as unidentified individual data in this work but often referred to as aggregate population data [5, Chapter 5]).Due to a lack of alternative methods, this form of data is typically treated as if it is collected from a single individual.This assumption leads to an overconfidence in model parameter values and model based predictions.We propose a novel method which accounts for inter-individual variability in experiments where only unidentified individual data is available.Both parametric and nonparametric methods for estimating the distribution of parameters which vary among individuals are developed.These methods are illustrated using both simulated data, and data taken from a physiological experiment.Taking the approach outlined in this paper results in more accurate quantification of the uncertainty attributed to inter-individual variability.}, number={6}, journal={MATHEMATICAL MODELLING OF NATURAL PHENOMENA}, author={Banks, H. T. and Baraldi, R. and Catenacci, J. and Myers, N.}, year={2016}, pages={9–27} }