Publications are ordered by year and then alphabetically by author name. Click on titles to access the paper.

    2024

  1. Derivation and travelling wave analysis of phenotype-structured haptotaxis models of cancer invasion.
    Lorenzi, T., Macfarlane, F.R., Painter, K.J.
    European Journal of Mathematics. Published online 2024:1-33.

    2. 2022

  2. Modelling rheumatoid arthritis: A hybrid modelling framework to describe pannus formation in a small joint.
    Macfarlane, F.R., Chaplain, M.A.J., Eftimie, R.
    Immunoinformormatics. 100014.

  3. The impact of phenotypic heterogeneity on chemotactic self-organisation.
    Macfarlane, F.R., Lorenzi, T., Painter, K.J.
    Bulletin of Mathematical Biology, 84, 143.

  4. Individual-based and continuum models of phenotypically heterogeneous growing cell populations.
    Macfarlane, F.R., Ruan, X., Lorenzi, T.
    AIMS Bioengineering, 9(1), 68-92.

    5. 2021

  5. A single-cell mathematical model of SARS-CoV-2 induced pyroptosis and the effects of anti-inflammatory intervention.
    Hamis, S.J., Macfarlane, F.R.
    AIMS Mathematics, 6(6), 6050-6086.

    6. 2020

  6. From a discrete model of chemotaxis with volume-filling to a generalized Patlak–Keller–Segel model.
    Bubba, F., Lorenzi, T., Macfarlane, F.R.
    Proceedings of the Royal Society A, 476, 20190871.

  7. Rapid community-driven development of a SARSCoV-2 tissue simulator.
    SARS-CoV-2 Tissue Simulation Coalition: Getz, M., Wang, Y., An, G., Becker, A., Cockrell, C., Collier, N., Craig, M., Davis, C.L., Faeder, J., Ford Versypt, A.N., Gianlupi, J.F., Glazier, J., Hamis, S.J., Heiland, R., Hillen, T., Hou, D., Aminul Islam, M., Jenner, A., Kortoglu, F., Liu, B., Macfarlane, F.R., Maygrundter, P., Morel, P.A., Narayanan, A., Ozik, J., Pienaar, E., Rangamani, P., Shoemaker, J.E., Smith, A.M., Macklin, P.
    Rapid Action Preprint

  8. Discrete and Continuum Models for the Evolutionary and Spatial Dynamics of Cancer: A Very Short Introduction Through Two Case Studies.
    Lorenzi, T., Macfarlane, F.R., Villa, C.
    In: Mondaini R. (eds) Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment. BIOMAT 2019. Springer, Cham.

  9. A hybrid discrete-continuum approach to model Turing pattern formation.
    Macfarlane, F.R., Chaplain, M.A.J., Lorenzi, T.
    Mathematical Biosciences and Engineering, 17(6), 7442-7479.

    10. 2019

  10. Bridging the gap between individual-based and continuum models of growing cell populations.
    Chaplain, M.A.J., Lorenzi, T., Macfarlane, F.R.
    Journal of Mathematical Biology, 80, 343–371.

  11. Quantitative predictive modelling approaches to understanding rheumatoid arthritis: A brief review.
    Macfarlane, F.R., Chaplain, M.A.J., Eftimie, R.
    Cells, 9(1), 74.

  12. A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours.
    Macfarlane, F.R., Chaplain, M.A.J., Lorenzi, T.
    Journal of Theoretical Biology, 480, 43-55.

    13. 2018

  13. Modelling the immune response to cancer: An individual-based approach accounting for the difference in movement between inactive and activated T cells.
    Macfarlane, F.R., Lorenzi, T., Chaplain, M.A.J.
    Bulletin of Mathematical Biology, 80(6), 1539-1562.

    14. 2016

  14. Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for pancreatic cancer.
    Walker, R., Navas, P.E., Friedman, S.H., Galliani, S., Karolak, A., Macfarlane, F.R., Noble, R., Poleszczuk, J., Russell, S., Rejniak, K.A. and Shahmoradi, A.
    White paper as part of the 2015 Integrated Mathematical Oncology Workshop