Publications

Submitted Articles
  1. M.V. Ciocanel, J. Nardini, K. Flores, E. Rutter, S. Sindi, A. Volkening. Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning (ME-EQL). arXiv 2506.08916.
  2. E. Rohr, J. Nardini. A novel sensitivity analysis method for agent-based models stratifies in-silico tumor spheroid simulations. arXiv 2506.00168.
  3.  A. Wenzel, P. Haughey, K. Nguyen, J. Nardini, J. Haugh, K. Flores. Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data. bioRxiv 2025.06. 13.659586.
Refereed Articles
  1. A. Malik, K. Nguyen, J. Nardini, K. Flores, C. Krona, S. Nedlander. Mathematical Modeling of Multicellular Tumor Spheroids Quantifies Inter-Patient and Inter-Tumor Heterogeneity. NPJ Systems Biology & Applications 11 (20) 2025. DOI: 10.1038/s41540-025-00492-3.
  2. J. Nardini. Forecasting and predicting agent-based model data with biologically-informed neural networks. Bulletin of Mathematical Biology 86 (130) 2024. DOI: 10.1007/s11538-024-01357-2.
  3. K. Nguyen, C. Jameson, S. Baldwin, J. Nardini, R. Smith, J. Haugh, K. Flores. Quantifying fluidization patterns in mesenchymal cell populations using topological data analysis and agent-base modeling. Mathematical Biosciences 370 April 2024. DOI: 10.1016/j.mbs.2024.109158.
  4. J. Nardini, C. Pugh, H. Byrne. Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images. Microcirculation 30 (4) 2023. DOI: 10.1111/micc.12799
  5. J. Nardini, B. Stolz, K. Flores, H. Harrington, H. Byrne. Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis.  PLoS Computational Biology 17 (6) 2021. DOI: 10.1371/journal.pcbi.1009094.
  6. J. Nardini, R. Baker, M. Simpson, K. Flores. Learning differential equation models from stochastic agent-based model simulations. Journal of the Royal Society Interface 18 (176) 2021. DOI: 10.1098/rsif.2020.0987. Open access version (arXiv 2011.08255).
  7. J. Lagergren, J. Nardini, R. Baker, M. Simpson, K. Flores. Biologically-informed neural networks guide mechanistic modeling from sparse experimental data. PLoS Computational Biology 16 (12) 2020. DOI: 10.1371/journal.pcbi.1008462.
  8. J. Nardini, J. Lagergren, A. Hawkins-Daarud, L. Curtin, B. Morris, E. Rutter, K. Swanson, K. Flores. Learning Equations from Biological Data with Limited Time Samples. Bulletin of Mathematical Biology. 82 (119) 2020. DOI: 10.1007/s11538-020-00794-z
  9. R. Everett, K. Flores, N. Henscheid, J. Lagergren, K. Larripa, D. Li, J. Nardini, P. Nguyen, E. B. Pittman, E. Rutter. A tutorial Review of Mathematical Techniques for Quantifying Tumor Heterogeneity. Mathematical Biosciences and Engineering 17 (4) 2020. DOI: 10.3934/mbe.2020207
  10. 5. J. Lagergren, J. Nardini, G. M. Lavigne, E. M. Rutter, K. B. Flores, Learning partial differential equations for biological transport models from noisy spatiotemporal data. Proceedings of the Royal Society A 476 (2234), 2020 DOI: 10.1098/rspa.2019.0800.
  11. D. Bhaskar, A. Manhart, J. Milzman, J. Nardini, K. Storey, C. Topaz, L. Ziegelmeier, Analyzing Collective Behavior with Machine Learning and Topology. Chaos: An Interdisciplinary Journal of Nonlinear Science. 29 (12), 123125, 2019 DOI: 10.1063/1.5125493
  12. J. Nardini, D. M. Bortz. The Influence of Numerical Error on an Inverse Problem Methodology in PDE Models. Inverse Problems 35 (6) 065003, 2019. DOI: 10.1088/1361-6420/ab10bb
  13. J. Nardini, D. M. Bortz. Investigation of a Structured Fisher’s Equation with Applications in Biochemistry. SIAM J. Appl. Math. Vol. 78, No. 3: pp. 1712-1736. 2018. DOI: 10.1137/16M1108546.
  14. J. Nardini, D. Chapnick, X. Liu, D. M. Bortz. Modeling keratinocyte wound healing dynamics: cell-cell adhesion promotes sustained collective migration. J. Theor. Biol., 7 July 2016, 103-117. DOI: 10.1016/j.jtbi.2016.04.015.
  15. K. Adoteye, R. Baraldi, K. Flores, J. Nardini, H. T. Banks, W. C. Thompson. Correlation of parameter estimators for models admitting multiple parameterizations. Int. J. Pure Appl. Math., 105(3) 497, 2015. DOI: 10.12732/ijpam.v105i3.16.
  16. T. Huffman, K. Link, J. Nardini, L. Poag, K. Flores, H.T. Banks, B. Biasco, J. Jungfleisch, J. Diez. A mathematical model of RNA3 recruitment in the replication cycle of brome mosaic virus. Int. J. Pure Appl. Math., 92(1) 27, 2014. DOI: 10.12732/ijpam.v92i1.3.
  17. H.T. Banks, A. Choi, T. Huffman, J. Nardini, L. Poag, W.C. Thompson. Quantifying CFSE label decay in flow cytometry data. Appl. Math. Lett., 26(5) 571, 2013. DOI: 10.1016/j.aml.2012.12.010.