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Bayesian Parametrisation ofIn Silico Tumour Models
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Technological progress in recent decades has allowed researchers to utilise accurate but computationally demanding models. One example of this development is the adoption of the multi-scale modelling technique for simulating various tissues. These models can then be utilised to test the efficacy of new drugs, e.g., for cancer treatment. Though multi-scale models can produce accurate representations of complex systems, their parameters often cannot be measured directly and have to be inferred using experimental data, which is a challenge yet to be solved. The goal of this work is to investigate the possibility of parametrising a specific high-performance tumour growth model using a likelihood-free method called Approximate Bayesian Computation (ABC). The first objective is to understand the effect that parameters of the model have on its behaviour. Then, by using the insights gained from the first step, define a set of summary statistics and a distance metric capable of capturing the impact of parameter variations on the growth of simulated tumours. Finally, assess the landscapes of the parameter space by utilising the statistics and the metric. The obtained results indicate that some of the parameters can be inferred by applying an ABC-style method, which motivates to further investigate the prospect of applying ABC for parametrising the model in question. However, the computational costs of such techniques are expected to be high, putting its execution time in the order of weeks, thus requiring future performance improvements of the model and highly efficient implementations of the parametrisation procedure.

Place, publisher, year, edition, pages
2018. , p. 53
Series
IT ; 18057
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-382536OAI: oai:DiVA.org:uu-382536DiVA, id: diva2:1307370
Educational program
Master Programme in Computational Science
Supervisors
Examiners
Available from: 2019-04-26 Created: 2019-04-26 Last updated: 2019-04-26Bibliographically approved

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CiteExportLink to record
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