James Greene

Research

My research is in mathematical modeling, where I generally combining theory and data to understand biological phenomena. Much of my work can be organized around the following themes:

Drug resistance · Collective dynamics · Diabetes and immune modeling · Epidemiological modeling · Other publications

1. Drug resistance and mathematical oncology

One of my primary interests is in understanding drug resistance. To this end, I have developed mechanistic models of cancer cell populations to distinguish selection of pre-existing resistant cells from treatment-induced adaptation. This work combines identifiability analysis, parameter inference, stochastic modeling, and control theory to determine what can be learned from population-level measurements, as well as generate experimentally testable treatment hypotheses. Current directions include the dynamics of persister populations, time-dependent dose response curves, combination therapy, antibiotic resistance evolution in spatially structured populations, and the design of treatment schedules.

Related publications

  1. J. L. Gevertz, J. M. Greene, S. Prosperi, N. Comandante-Lou, and E. D. Sontag. “Understanding therapeutic tolerance through a mathematical model of drug-induced resistance.” npj Systems Biology and Applications, vol. 11, 2025. DOI
  2. J. M. Greene, C. Sanchez-Tapia, and E. D. Sontag. “Mathematical details on a cancer resistance model.” Frontiers in Bioengineering and Biotechnology, vol. 8, 2020. DOI
  3. J. M. Greene, J. L. Gevertz, and E. D. Sontag. “Mathematical approach to differentiate spontaneous and induced evolution to drug resistance during cancer treatment.” JCO Clinical Cancer Informatics, vol. 3, 2019. DOI
  4. J. M. Greene, C. Sanchez-Tapia, and E. D. Sontag. “Control structures of drug resistance in cancer chemotherapy.” in Proceedings of the 2018 IEEE Conference on Decision and Control, 2018. DOI
  5. A. Silva, M. C. Silva, P. Sudalagunta, A. Distler, T. Jacobson, A. Collins, T. Nguyen, J. Song, D. T. Chen, L. Chen, C. Cubitt, C. Baz, L. Perez, R. Gatenby, W. Dalton, J. M. Greene, E. Sontag, R. Gillies, D. Rebatchouk, and K. H. Shain. “An ex vivo platform for the prediction of clinical response in multiple myeloma.” Cancer Research, vol. 77, no. 12, 2017. DOI
  6. J. M. Greene, D. Levy, S. P. Herrada, M. M. Gottesman, and O. Lavi. “Mathematical modeling reveals that changes to local cell density dynamically modulate baseline variations in cell growth and drug response.” Cancer Research, vol. 76, no. 10, 2016. DOI
  7. J. M. Greene, D. Levy, K. L. Fung, P. S. Souza, M. M. Gottesman, and O. Lavi. “Modeling intrinsic heterogeneity and growth of cancer cells.” Journal of Theoretical Biology, vol. 367, 2015. DOI
  8. O. Lavi, J. M. Greene, D. Levy, and M. M. Gottesman. “Simplifying the complexity of resistance heterogeneity in metastasis.” Trends in Molecular Medicine, vol. 20, no. 3, 2014. DOI
  9. J. M. Greene, O. Lavi, M. M. Gottesman, and D. Levy. “The impact of cell density and mutations in a model of multidrug resistance in solid tumors.” Bulletin of Mathematical Biology, vol. 76, 2014. DOI
  10. O. Lavi, J. M. Greene, D. Levy, and M. M. Gottesman. “The role of cell density and intratumoral heterogeneity in multidrug resistance.” Cancer Research, vol. 73, no. 24, 2013. DOI
  11. J. L. Gevertz, J. M. Greene, and E. D. Sontag. “Validation of a mathematical model of cancer incorporating spontaneous and induced evolution to drug resistance.” bioRxiv, 2019. DOI

2. Collective dynamics and learning dynamical systems

Many biological systems involve large numbers of interacting agents whose governing equations often exhibit high degrees of symmetry via pairwise interactions. My collaborators and I exploit these symmetries to learn interaction kernels and environmental forces directly from observed trajectory data. A related direction develops general methods for inferring equations for dynamical systems when the degree and precise structure of stochasticity are not known in advance. Examples include singular diffusion structures, mixed deterministic–stochastic dynamics, state-dependent noise structure, and extensions toward discontinuous jump processes.

Related publications

  1. J. M. Greene, E. Tadmor, and M. Zhong. “The emergence of lines of hierarchy in collective motion of biological systems.” Physical Biology, vol. 20, no. 5, 2023. DOI
  2. Z. Guo, J. M. Greene, and M. Zhong. “Learning stochastic dynamical systems with structured noise.” arXiv, 2025. DOI

3. Diabetes and immune modeling

I develop mechanistic models of glucose, insulin, and glucagon regulation, with particular interest in the onset and progression of type 1 diabetes. This work links physiological feedback, immune cell dynamics, parameter inference, and treatment response.

Related publications

  1. M. Dalton, E. Asante-Asamani, and J. M. Greene. “Antigen presenting cells determine response to regulatory T cell therapy in type 1 diabetes.” npj Systems Biology and Applications, accepted, 2026.
  2. M. Dalton, E. Asante-Asamani, and J. M. Greene. “A simple mechanistic model for insulin–glucose–glucagon dynamics and its implications for diabetes management.” Mathematical Biosciences and Engineering, vol. 23, no. 6, 2026. DOI

4. Epidemiological modeling

I have developed mechanistic and data-driven models to understand how interventions, behavioral responses, and population heterogeneity shape the course of epidemics. Much of this work was motivated by the COVID-19 pandemixc and examined the timing of social distancing, testing and isolation, reopening strategies, and the design of control policies that reduce peak infection values.

Related publications

  1. J. M. Greene and E. D. Sontag. “Minimizing the infected peak utilizing a single lockdown: A technical result regarding equal peaks.” in Proceedings of the 2022 American Control Conference, 2022. DOI
  2. M. Dalton, P. Dougall, F. L. Amoah Darko, W. Annan, E. Asante-Asamani, S. Bailey, J. M. Greene, and D. White. “Modeling optimal reopening strategies for COVID-19 and its variants by keeping infections low and fixing testing capacity.” PLoS One, vol. 17, no. 11, 2022. DOI
  3. J. L. Gevertz, J. M. Greene, C. H. Sanchez-Tapia, and E. D. Sontag. “A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing.” Journal of Theoretical Biology, vol. 510, 2021. DOI
  4. M. Sadeghi, J. M. Greene, and E. D. Sontag. “Universal features of epidemic models under social distancing guidelines.” Annual Reviews in Control, vol. 51, 2021. DOI
  5. S. Dodamgodage, D. Senarathna, T. Sathiyakumar, S. Andreescu, J. M. Greene, S. Sur, and S. Mondal. “Univariate and interaction effects of risk factors on COVID-19 in the United States during the pre-vaccination period using a negative binomial regression model.” under review, 2025.

Other publications

My broader collaborations include synthetic biology, statistical modeling and inference, and biomaterials.

Journal articles and proceedings

  1. S. Poudyal, A. Lindquist, N. Smullen, V. York, A. Lotfi, J. M. Greene, and M. Meysami. “Unveiling wildfire dynamics: A Bayesian county-specific analysis in California.” J, vol. 7, no. 3, 2024. DOI
  2. M. Meysami, V. Kumar, M. Pugh, S. T. Lowery, S. Sur, S. Mondal, and J. M. Greene. “Utilizing logistic regression to compare risk factors in disease modeling with imbalanced data: A case study in vitamin D and cancer.” Frontiers in Oncology, vol. 13, 2023. DOI
  3. V. H. Nagaraj, J. M. Greene, A. M. Sengupta, and E. D. Sontag. “Translation inhibition and resource balance in the TX–TL cell-free gene expression system.” Synthetic Biology, vol. 2, no. 1, 2017. DOI

Manuscripts under review

  1. R. Fatima, A. S. Imadh, J. M. Greene, and B. Almeida. “Mold surface chemistry and evaporation kinetics govern hydrogel gelation behavior.” under review, 2026.

Full CV