Abstract
Angiogenesis, the growth and formation of capillaries form pre-existing vessels, is involved in various disease pathologies including brain cancer. Brain tumours are highly vascularised as they require new blood vessels to maintain oxygen and nutrition supply for their growth and survival. In recent years, molecular biology as well as imaging techniques have uncovered several aspects of angiogenesis and the vessel assembly process, based on which novel agents have been developed to target and counteract tumour-induced angiogenesis. However, many the guiding principles and molecular processes guiding angiogenic processes remain unknown and reliant on quantitative, computational models. Multiscale cellular automata models of angiogenesis have previously been employed but as new data become available, these generalized models can berefined to investigate particular aspects of angiogenic processes in different tissue environments.
In this project, I developed multiscale multicellular models of angiogenic sprouting simulating cell migration and sprouting in response to VEGF and DLL4/NOTCH1 mediated endothelial tip cell selection in a neural tissue and vascularized tumour environment. The preliminary results I collected demonstrate that Compucell3D is a viable tool to emulate angiogenic
sprouting in neural tissue. I identified some parameters including cell adhesion and volume which dramatically affect qualitative cellular responses including cell migration in my model. Future work emerging from complementary projects in the host lab will aimed to refine our current SBML and include Jagged/NOTCH1-4 contribution. Our approach employs a mix of in vitro and in silico experimentation whereby experiments can be repeated in vitro and results can be compared to the synthetic data created through computational modelling and, if correlating, confirm and expand the theory behind cellular processes. The tool can be taken further in a translational context with the simulation of anti-angiogenic treatment as well as the consideration of variables including time
frame, dosage and tumour progression to investigate treatment strategies and aid in drug efficiency as well as planning clinical trials. Further, patient cells can be linked to experimental data in the future, and be used to predict likely treatment response and outcomes.
| Date of Award | 2020 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Lorenzo Veschini (Supervisor) & François Chesnais (Supervisor) |