Kevin Flores

Nguyen, K. C., Jameson, C. D., Baldwin, S. A., Nardini, J. T., Smith, R. C., Haugh, J. M., & Flores, K. B. (2024). Quantifying collective motion patterns in mesenchymal cell populations using topological data analysis and agent-based modeling. MATHEMATICAL BIOSCIENCES, 370. https://doi.org/10.1016/j.mbs.2024.109158

Nguyen, K., Rutter, E. M., & Flores, K. B. (2023). Estimation of Parameter Distributions for Reaction-Diffusion Equations with Competition using Aggregate Spatiotemporal Data. BULLETIN OF MATHEMATICAL BIOLOGY, 85(7). https://doi.org/10.1007/s11538-023-01162-3

Lagergren, J., Pavicic, M., Chhetri, H. B., York, L. M., Hyatt, D., Kainer, D., … Streich, J. (2023). Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in<i> Populus</i><i> trichocarpa</i>. PLANT PHENOMICS, 5. https://doi.org/10.34133/plantphenomics.0072

Nguyen, K., Li, K., Flores, K., Tomaras, G. D., Dennison, S. M., & McCarthy, J. M. (2023). Parameter estimation and identifiability analysis for a bivalent analyte model of monoclonal antibody-antigen binding. ANALYTICAL BIOCHEMISTRY, 679. https://doi.org/10.1016/j.ab.2023.115263

Warrier, S., Rutter, E. M., & Flores, K. B. (2022). Multitask neural networks for predicting bladder pressure with time series data. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 72. https://doi.org/10.1016/j.bspc.2021.103298

Zhang, M., Flores, K. B., & Tran, H. T. (2021). Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 69. https://doi.org/10.1016/j.bspc.2021.102923

Nardini, J. T., Baker, R. E., Simpson, M. J., & Flores, K. B. (2021). [Review of Learning differential equation models from stochastic agent-based model simulations]. JOURNAL OF THE ROYAL SOCIETY INTERFACE, 18(176). https://doi.org/10.1098/rsif.2020.0987

McDaniel, M., Flores, K. B., & Akpa, B. S. (2021, July 19). Predicting Inter-individual Variability During Lipid Resuscitation of Bupivacaine Cardiotoxicity in Rats: A Virtual Population Modeling Study. DRUGS IN R&D, Vol. 7. https://doi.org/10.1007/s40268-021-00353-4

Peace, A., Frost, P. C., Wagner, N. D., Danger, M., Accolla, C., Antczak, P., … Wang, H. (2021). Stoichiometric Ecotoxicology for a Multisubstance World. BIOSCIENCE, 71(2), 132–147. https://doi.org/10.1093/biosci/biaa160

Nardini, J. T., Stolz, B. J., Flores, K. B., Harrington, H. A., & Byrne, H. M. (2021). Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis. PLOS COMPUTATIONAL BIOLOGY, 17(6). https://doi.org/10.1371/journal.pcbi.1009094

Everett, R., Flores, K. B., Henscheid, N., Lagergren, J., Larripa, K., Li, D., … Rutter, E. M. (2020). [Review of A tutorial review of mathematical techniques for quantifying tumor heterogeneity]. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 17(4), 3660–3709. https://doi.org/10.3934/mbe.2020207

Lagergren, J. H., Nardini, J. T., Baker, R. E., Simpson, M. J., & Flores, K. B. (2020). Biologically-informed neural networks guide mechanistic modeling from sparse experimental data. PLOS COMPUTATIONAL BIOLOGY, 16(12). https://doi.org/10.1371/journal.pcbi.1008462

Lagergren, J., Flores, K., Gilman, M., & Tsynkov, S. (2021). Deep Learning Approach to the Detection of Scattering Delay in Radar Images. JOURNAL OF STATISTICAL THEORY AND PRACTICE, 15(1). https://doi.org/10.1007/s42519-020-00149-w

Saberi-Bosari, S., Flores, K. B., & San-Miguel, A. (2020). Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock. BMC BIOLOGY, 18(1). https://doi.org/10.1186/s12915-020-00861-w

Nardini, J. T., Lagergren, J. H., Hawkins-Daarud, A., Curtin, L., Morris, B., Rutter, E. M., … Flores, K. B. (2020). Learning Equations from Biological Data with Limited Time Samples. BULLETIN OF MATHEMATICAL BIOLOGY, 82(9). https://doi.org/10.1007/s11538-020-00794-z

Lagergren, J. H., Nardini, J. T., Michael Lavigne, G., Rutter, E. M., & Flores, K. B. (2020). Learning partial differential equations for biological transport models from noisy spatio-temporal data. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 476(2234). https://doi.org/10.1098/rspa.2019.0800

San Miguel, A., Ramirez, J., & Flores, K. (2020, April). Lifelong Analysis of Key Aging Genes as Determinants of Lifespan in <it>C</it>. <it>elegans</it>. FASEB JOURNAL, Vol. 34. https://doi.org/10.1096/fasebj.2020.34.s1.00160

Rutter, E. M., Lagergren, J. H., & Flores, K. B. (2018). Automated Object Tracing for Biomedical Image Segmentation Using a Deep Convolutional Neural Network. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, Vol. 11073, pp. 686–694. https://doi.org/10.1007/978-3-030-00937-3_78

Rutter, E. M., Langdale, C. L., Hokanson, J. A., Hamilton, F., Tran, H., Grill, W. M., & Flores, K. B. (2018). Detection of Bladder Contractions From the Activity of the External Urethral Sphincter in Rats Using Sparse Regression. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(8), 1636–1644. https://doi.org/10.1109/tnsre.2018.2854675

Rutter, E. M., Banks, H. T., & Flores, K. B. (2018). Estimating intratumoral heterogeneity from spatiotemporal data. JOURNAL OF MATHEMATICAL BIOLOGY, 77(6-7), 1999–2022. https://doi.org/10.1007/s00285-018-1238-6

Lagergren, J., Reeder, A., Hamilton, F., Smith, R. C., & Flores, K. B. (2018). Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model. Bulletin of Mathematical Biology, 80(6), 1578–1595. https://doi.org/10.1007/s11538-018-0421-7

Rutter, E. M., Banks, H. T., LeBlanc, G. A., & Flores, K. B. (2017). Continuous Structured Population Models for Daphnia magna. Bulletin of Mathematical Biology, 79(11), 2627–2648. https://doi.org/10.1007/s11538-017-0344-8

Banks, H. T., Flores, K. B., Langlois, C. R., Serio, T. R., & Sindi, S. S. (2017). Estimating the rate of prion aggregate amplification in yeast with a generation and structured population model. Inverse Problems in Science and Engineering, 26(2), 257–279. https://doi.org/10.1080/17415977.2017.1316498

Hamilton, F., Lloyd, A. L., & Flores, K. B. (2017). Hybrid modeling and prediction of dynamical systems. PLoS Computational Biology, 13(7).

Banks, H. T., Collins, E., Flores, K., Pershad, P., Stemkovski, M., & Stephenson, L. (2017). Statistical error model comparison for logistic growth of green algae (Raphidocelis subcapitata). Applied Mathematics Letters, 64, 213–222. https://doi.org/10.1016/J.AML.2016.09.006

Wang, L.-Z., Wu, F., Flores, K., Lai, Y.-C., & Wang, X. (2016). Build to understand: synthetic approaches to biology. Integrative Biology, 8(4), 394–408. https://doi.org/10.1039/c5ib00252d

Banks, H. T., Flores, K. B., & Sindi, S. S. (2016). On analytical and numerical approaches to division and label structured population models. APPLIED MATHEMATICS LETTERS, 60, 81–88. https://doi.org/10.1016/j.aml.2016.04.009

Stemkovski, M., Baraldi, R., Flores, K. B., & Banks, H. T. (2016). Validation of a mathematical model for green algae (Raphidocelis Subcapitata) growth and implications for a coupled dynamical system with Daphnia magna. Applied Sciences-Basel, 6(5).

Adoteye, K., Baraldi, R., Flores, K., Nardini, J., Banks, H. T., & Thompson, W. C. (2015). Correlation of parameter estimators for models admitting multiple parametrizations. International Journal of Pure and Applied Mathematics, 105(3), 497–522. https://doi.org/10.12732/ijpam.v105i3.16

Adoteye, K., Banks, H. T., Flores, K. B., & LeBlanc, G. A. (2015). Estimation of time-varying mortality rates using continuous models for Daphnia magna. APPLIED MATHEMATICS LETTERS, 44, 12–16. https://doi.org/10.1016/j.aml.2014.12.014

Banks, H. T., Flores, K. B., Hu, S., Rosenberg, E., Buzon, M., Yu, X., & Lichterfeld, M. (2015). Immuno-modulatory strategies for reduction of HIV reservoir cells. JOURNAL OF THEORETICAL BIOLOGY, 372, 146–158. https://doi.org/10.1016/j.jtbi.2015.02.006

Banks, H. T., Baraldi, R., & Flores, K. B. (2015). Optimal design for minimizing uncertainty in dynamic equilibrium systems. Eurasian Journal of Mathematical and Computer Applications, 3(1), 23–47.

Adoteye, K., Banks, H. T., & Flores, K. B. (2015). Optimal design of non-equilibrium experiments for genetic network interrogation. APPLIED MATHEMATICS LETTERS, 40, 84–89. https://doi.org/10.1016/j.aml.2014.09.013

Adoteye, K., Banks, H. T., Cross, K., Eytcheson, S., Flores, K. B., LeBlanc, G. A., … Stokely, S. (2015). Statistical validation of structured population models for Daphnia magna. Mathematical Biosciences, 266, 73–84. https://doi.org/10.1016/j.mbs.2015.06.003

Sadd, B. M., Barribeau, S. M., Bloch, G., Graaf, D. C., Dearden, P., Elsik, C. G., … Waterhouse, R. M. (2015). The genomes of two key bumblebee species with primitive eusocial organization. Genome Biology, 16.

Banks, H. T., Baraldi, R., Cross, K., Flores, K., Mcchesney, C., Poag, L., & Thorpe, E. (2015). UNCERTAINTY QUANTIFICATION IN MODELING HIV VIRAL MECHANICS. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 12(5), 937–964. https://doi.org/10.3934/mbe.2015.12.937

Baraldi, R., Cross, K., McChesney, C., Poag, L., Thorpe, E., Flores, K. B., & Banks, H. T. (2014). Uncertainty quantification for a model of HIV-1 patient response to antiretroviral therapy interruptions. 2014 american control conference (acc), 2753–2758.

Huffman, T., Link, K., Nardini, J., Poag, L., Flores, K., Banks, H. T., … Diez, J. (2013). A mathematical model of RNA3 recruitment in the replication cycle of Brome Mosaic Virus. International Journal of Pure and Applied Mathematics, 89(2), 251–274. https://doi.org/10.12732/ijpam.v89i2.9

Flores, K. B. (2013). A structured population modeling framework for quantifying and predicting gene expression noise in flow cytometry data. APPLIED MATHEMATICS LETTERS, 26(7), 794–798. https://doi.org/10.1016/j.aml.2013.03.003

Everett, R. A., Zhao, Y., Flores, K. B., & Kuang, Y. (2013). DATA AND IMPLICATION BASED COMPARISON OF TWO CHRONIC MYELOID LEUKEMIA MODELS. MATHEMATICAL BIOSCIENCES AND ENGINEERING, 10(5-6), 1501–1518. https://doi.org/10.3934/mbe.2013.10.1501

Flores, K. B., Wolschin, F., & Amdam, G. V. (2013). The Role of Methylation of DNA in Environmental Adaptation. Integrative and Comparative Biology, 53(2), 359–372. https://doi.org/10.1093/icb/ict019

Flores, K., Wolschin, F., Corneveaux, J. J., Allen, A. N., Huentelman, M. J., & Amdam, G. V. (2012). Genome-wide association between DNA methylation and alternative splicing in an invertebrate. BMC Genomics, 13(1), 480. https://doi.org/10.1186/1471-2164-13-480

Flores, K. B., & Amdam, G. V. (2011). Deciphering a methylome: what can we read into patterns of DNA methylation? Journal of Experimental Biology, 214(19), 3155–3163. https://doi.org/10.1242/jeb.059741

Flores, K., & Hadeler, K. P. (2010). The random walk of Azospirillum brasilense. Journal of Biological Dynamics, 4(1), 71–85. https://doi.org/10.1080/17513750902773914