My research focuses on how we can use mathematical and computational methods to generate virtual patients and virtual patient cohorts to examine the behaviours of diseases and predict alternate more effective therapies.
My research has looked at many different applications including cancer treatments using chemotherapies, immunotherapies and virotherapies, the SARS-CoV-2 infection and resulting patient responses and neuroimmunological diseases such as Multiple sclerosis.
AGENT-BASED MODELLING OF GLIOBLASTOMA FORMATION AND TREATMENT
Glioblastoma is a highly aggressive, invasive brain cancer that is difficult to cure by conventional therapies such as chemotherapy or radiation. This resistance to therapy is largely due to glioblastoma cells failing to undergo apoptosis (premature cell death). To investigate the cause of failure of differeent oncotherapies, we have been developing an agent-based model for glioblastoma formation and treatment using PhysiCell: an open-source platform.
Relevant publications:
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Jenner, A.L., Smalley, M., Goldman, D., Goins, W.F., Cobbs, C.S., Puchalski, R.B., Chiocca, E.A., Lawler, S., Macklin, P., Goldman, A. and Craig, M., 2022. Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy. iScience, 25(6).
IMMUNE RESPONSE TO SARS-COV-2 INFECTION
The primary distinction between severe and mild COVID-19 infections is the immune response. Disease severity and fatality has been observed to correlate with some clinical markers, however, the exact mechanism driving the dynamics that ultimately result in severe COVID-19 manifestation remain unclear. To delineate mechanisms regulating differential immune responses to SARS-CoV-2, we have been developing tissue- and systemic-level models of the immune response to infection with the goal of pinpointing what may be causing dysregulated immune dynamics in severe cases.
Relevant publications:
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Jenner, A.L., Aogo, R.A., Alfonso, S., Crowe, V., Deng, X., Smith, A.P., Morel, P.A., Davis, C.L., Smith, A.M. and Craig, M., 2021. COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes. PLoS pathogens, 17(7), p.e1009753.
TREATING MULTIPLE SCLEROSIS PATIENTS INFORMED BY MATHEMATICAL MODELLING
Every day, we use our bodies to move, think, talk and eat, but for people with multiple sclerosis (MS) these tasks can be virtually impossible. MS is a chronic disease which develops because the immune system has started to attack the nerve cells in the brain. This causes the degradation of parts of the brain and irreversible impairment in physical and mental activity. Unfortunately, this disease has no cure, and while considerable therapeutic advances against this disease have been achieved, MS will still progress within a patient. The goal of this research is to develop a way to model mathematically how immune cells are recruited into the brain and how they cross the brain blood barrier. By developing this framework, we will be able to provide researchers with a way of understanding the evolution of inflammation in a patient’s brain.
Weatherley, G., Araujo, R. P., Dando, S. J., & Jenner, A. L. 2023. Could mathematics be the key to unlocking the mysteries of multiple sclerosis?. Bulletin of Mathematical Biology, 85(8), 75.
Dallaston, M. C., Birtles, G., Araujo, R. P., & Jenner, A. L. 2023. The effect of chemotaxis on T-cell regulatory dynamics. Journal of Mathematical Biology, 87(6), 84.
OPTIMISING GEL-RELEASE MECHANICS OF ONCOLYTIC VIROTHERAPY AND CHEMOTHERAPY
Polymer and hydrogel implants are effective tools at delivering sustained and localised release of therapy. A challenge with these devices is determining the optimise release function for the implant and how to mechanistically achieve the optimal release. Using systems of ordinary differential equations and applying control theory and genetic algorithms, we are working towards determining the optimise release profile for certain implants (such as hydrogels releasing immunotherapy and polymer fibres releasing chemotherapy).
Relevant publications:
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Jenner, A., Kelly, W., Dallaston, M., Araujo, R., Parfitt, I., Steinitz, D., Pooladvand, P., Kim, P.S., Wade, S.J. and Vine, K.L., 2023. Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model. PLOS Computational Biology
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Jenner, A.L., Frascoli, F., Yun, C.O. and Kim, P.S., 2020. Optimising hydrogel release profiles for viro-immunotherapy using oncolytic adenovirus expressing IL-12 and GM-CSF with immature dendritic cells. Applied Sciences, 10(8), p.2872.
INDIVIDUALISING COMBINATION CANCER THERAPIES WITH VIRTUAL CLINICAL TRIALS
HETEROGENEITY IN BIOLOGICAL PROCESSES
A major challenge facing the developement of effective oncotherapies, is achieving robustness at the clinical trial level. Using in silico virtual patients matched to trial data, we in the process of simulating the effectiveness of individualising particular therapies and in turn the robustness of alternations to therapies. Computational quantitative modelling has the power to provide significant insight into potential therapies and patient specifics before a trial is underway.
Relevant publications:
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Cardinal, O., Burlot, C., Fu, Y., Crosley, P., Hitt, M., Craig, M. and Jenner, A.L., 2022. Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trails. bioRxiv.
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Jenner, A.L., Cassidy, T., Belaid, K., Bourgeois-Daigneault, M.C. and Craig, M., 2021. In silico trials predict that combination strategies for enhancing vesicular stomatitis oncolytic virus are determined by tumor aggressivity. Journal for immunotherapy of cancer, 9(2).
Biology is incredibly heterogeneous and that heterogeneity in processes from the molecularly, cellular all the way to the inter-human level can drive the differences we observe in everyday life. Many data sets have captured this heterogeneity, however, mathematical models still largely overlook this variation in model parameters. Recent work has been to see how we can better use the variability exhibited in experimental measurements to determine a distribution for parameters in a mathematical model that then infers biological level variability.
Relevant publications:
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Browning, A.P., Ansari, N., Drovandi, C., Johnston, A.P., Simpson, M.J. and Jenner, A.L., 2022. Identifying cell-to-cell variability in internalization using flow cytometry. Journal of the Royal Society Interface, 19(190), p.20220019.
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Drovandi, C., Lawson, B., Jenner, A.L. and Browning, A.P., 2022. Population Calibration using Likelihood-Free Bayesian Inference. arXiv preprint arXiv:2202.01962.