AGENT-BASED MODELLING OF GLIOBLASTOMA FORMATION AND TREATMENT

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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

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 lymphopenia (low blood lymphocyte count) and increased levels of inflammatory cytokines and IL-6 (cytokine storm), damaging dysregulated macrophage responses, and T cell exhaustion due to limited recruitment. 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 developed 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.

STOCHASTIC MODELLING OF ACUTE MYELOID LEUKEMIA DEVELOPEMENT

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Acute myeloid leukemia (AML) is caused by genetic disruptions to the hematopoietic stem cell (HSC) population and results in overproduction of immature myeloid cells. The incomplete understanding of the epigenetic dynamics prior to AML limits our ability to manage and treat this disease and results in a poor survival rate. Using a multi-compartment Moran process model of the hematopoietic system, we are examining the influence of different mutations on hematopoiesis to predict how AML eventuates from certain driver mutations.

OPTIMISING GEL-RELEASE MECHANICS OF ONCOLYTIC VIROTHERAPY AND CHEMOTHERAPY

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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).

INDIVIDUALISING ONCOTHERAPY WITH VIRTUAL CLINICAL TRIALS

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BIOCHEMICAL NETWORK MODELLING

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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. 

Biology, at the microscale, is full of examples of networks that are perfectly robust in their ability to regulate perturbations and control outputs. These networks are fundamental to our ability to function as human beings and for the existence of the world around us. Through understanding these networks, we can begin to understand what key factors are determinants of disease development and progression and then can be used to tailor therapeutic drugs and intervention protocols. In the field of bio chemical networks, we are interesting in understanding what topologies and conditions are required to result in robust and adaptable networks.