Articles in Review
  1. J. Nardini, C. Pugh, H. Byrne. Statistical and Topological Summaries Aid Disease Detection for Segmented Retinal Vascular Images. arXiv 2202.09708.
Refereed Articles
  1. 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.
  2. 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.2020.0987Open access version (arXiv 2011.08255).
  3. 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.
  4. 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
  5. 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
  6. 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.
  7. 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
  8. J. Nardini, D. M. Bortz. The Influence of Numerical Error on an Inverse Problem Methodology in PDE Models. Inverse Problems 35 (6) 065003, 2019DOI: 10.1088/1361-6420/ab10bb
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.