My research focuses on the use of innovative modelling and simulation methodologies for solving important problems in biology and physiology. A particular focus is on variability and heterogeneity. I have had a long and varied academic life. Academic highlights include
Federation Fellow of the ARC: 2004-2008.
Shared position between Oxford University and QUT: 2007-2015
ACEMS Deputy Director Kerrie Mengersen is leading a team of researchers to try to save the jaguar population in Peru. Her project combines the use of statistics, mathematics, and virtual reality technology to help leaders make informed decisions about the threatened species, and the land it depends on.
Viral pathogens pose a continuous and shifting biological threat to military readiness and national security overall in the form of infectious disease with pandemic potential. Today’s limited vaccines and other antivirals are often circumvented by quickly mutating viruses that evolve to develop resistance to treatments that are carefully formulated to act only specific strains of a virus.
Everybody is different, and every body is different. Significant variability is a common feature of all of the physiological systems that compose the function of the human body, and understanding this variability is critical to explaining differences in susceptibility to pathological conditions, and also to explaining how medical treatments can potentially succeed or fail even when applied to treat the same condition.
Burrage, K., Burrage P., Leier A., & Marquez-Lago T.
(2017). A Review of Stochastic and Delay Simulation Approaches in Both Time and Space in Computational Cell Biology.
(Holcman, D., Ed.).Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology. 241–261. doi: 10.1007/978-3-319-62627-7_11
Invited talks, refereed proceedings and other conference outputs
Burrage, P., Burrage K., & MacNamara S.
(2019). Integrated Approaches for Stochastic Chemical Kinetics. 17th International Conference of Numerical Analysis and Applied Mathematics.
Burrage, K., Burrage P., & MacNamara S.
(2019). Reflectionless models via Bessel Functions. ANZIAM Conference 2019.
Lawson, B. A. J., Jakes D., Burrage K., Burrage P., Drovandi C. C., & Bueno-Orovio A.
(2019). Perlin noise for automatic generation of complex spatial patterns: an application to cardiac fibrosis. ANZIAM Conference 2019.
Burrage, P., Donovan D., Thompson B., & Burrage K.
(2015). Populations of Models, Experimental Designs and Coverage of Parameter Space by Latin Hypercube and Orthogonal Sampling. International Conference On Computational Science, ICCS 2015 - Computational Science at the Gates of Nature. doi: 10.1016/j.procs.2015.05.383
Psaltis, S. T. P., Burrage K., & Farrell T.
(2015). Mathematical modelling of gas production in a Coal Seam Gas (CSG) field. Eleventh International Conference on CFD in the Minerals and Process Industries.
Donovan, D., Burrage K., Burrage P., McCourt TA., Thompson B., & Yazici EŞ.
(2018). Estimates of the coverage of parameter space by Latin Hypercube and Orthogonal Array-based sampling. Applied Mathematical Modelling. 57, 553-564. doi: 10.1016/j.apm.2017.11.036
Tang, M., Li T., Gandhi N. S., Burrage K., & Gu YT.
(2017). Heterogeneous nanomechanical properties of type I collagen in longitudinal direction. Biomechanics and modeling in mechanobiology. 16(3), 1023–1033. doi: 10.1007/s10237-016-0870-6
Bueno-Orovio, A., & Burrage K.
(2017). Exact solutions to the fractional time-space Bloch–Torrey equation for magnetic resonance imaging. Communications in Nonlinear Science and Numerical Simulation. 52, 91–109. doi: 10.1016/j.cnsns.2017.04.013
Zheng, M., Liu F., Liu Q., Burrage K., & Simpson M. J.
(2017). Numerical solution of the time fractional reaction–diffusion equation with a moving boundary. Journal of Computational Physics. 338, 493–510. doi: 10.1016/j.jcp.2017.03.006
Bueno-Orovio, A., Teh I., Schneider J. E., Burrage K., & Grau V.
(2016). Anomalous diffusion in cardiac tissue as an index of myocardial microstructure. IEEE Transactions on Medical Imaging. 35(9), 2200-2207. doi: 10.1109/TMI.2016.2548503
Chen, S., Liu F., Jiang X., Turner I., & Burrage K.
(2016). Fast finite difference approximation for identifying parameters in a two-dimensional space-fractional nonlocal model with variable diffusivity coefficients. SIAM Journal on Numerical Analysis. 54(2), 606-624. doi: 10.1137/15M1019301
Zhou, X., Bueno-Orovio A., Orini M., Hanson B., Hayward M., Taggart P., et al.
(2016). In vivo and in silico investigation into mechanisms of frequency dependence of repolarization alternans in human ventricular cardiomyocytes. Circulation Research. 118(2), 266-278. doi: 10.1161/CIRCRESAHA.115.307836
Gemmell, P., Burrage K., Rodríguez B. R., & Quinn T. A.
(2016). Rabbit-specific computational modelling of ventricular cell electrophysiology: Using populations of models to explore variability in the response to ischemia. Progress in Biophysics and Molecular Biology. 121(2), 169-184. doi: 10.1016/j.pbiomolbio.2016.06.003
Pueyo, E., Dangerfield C. E., Britton O. J., Virág L., Kistamás K., Szentandrássy N., et al.
(2016). Experimentally-based computational investigation into beat-to-beat variability in ventricular repolarization and its response to ionic current inhibition. PLOS ONE. 11(3), doi: 10.1371/journal.pone.0151461
Niu, Y., Burrage K., & Chen L.
(2016). Modelling biochemical reaction systems by stochastic differential equations with reflection. Journal of Theoretical Biology. 396, 90-104. doi: 10.1016/j.jtbi.2016.02.010
Muszkiewicz, A., Britton O. J., Gemmell P., Passini E., Sánchez C., Zhou X., et al.
(2016). Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm. Progress in Biophysics & Molecular Biology.. 120(1-3), 115-127. doi: 10.1016/j.pbiomolbio.2015.12.002
Cusimano, N., Bueno-Orovio A., Turner I., Burrage K., & Talkachova A.
(2015). On the Order of the Fractional Laplacian in Determining the Spatio-Temporal Evolution of a Space-Fractional Model of Cardiac Electrophysiology. PLOS ONE. 10(12), doi: 10.1371/journal.pone.0143938
Barrio, M., Burrage K., & Burrage P.
(2015). Stochastic linear multistep methods for the simulation of chemical kinetics. The Journal of Chemical Physics. 142(6), 64101. doi: 10.1063/1.4907008
Walmsley, J., Mirams G. R., Pitt-Francis J., Rodriguez B., & Burrage K.
(2015). Application of stochastic phenomenological modelling to cell-to-cell and beat-to-beat electrophysiological variability in cardiac tissue. Journal of Theoretical Biology. 365, 325-336. doi: 10.1016/j.jtbi.2014.10.029
Niu, Y., Burrage K., & Zhang C.
(2015). Multi-scale approach for simulating time-delay biochemical reaction systems. IET Systems Biology. 9(1), 31-38. doi: 10.1049/iet-syb.2013.0023
Székely, T., Burrage K., Mangel M., Bonsall M. B., & Santos F. C.
(2014). Stochastic Dynamics of Interacting Haematopoietic Stem Cell Niche Lineages. PLoS Computational Biology. 10(9), doi: 10.1371/journal.pcbi.1003794