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Computational modelling of molecular nexopathies

Georgiadis, Konstantinos; (2020) Computational modelling of molecular nexopathies. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Neurodegenerative diseases are an ever-increasing health problem, requiring substantial human and financial resources. They are caused by pathogenic proteins, which accumulate and spread in the brain's neural network, causing neuronal loss and brain atrophy. However, the mechanisms that govern pathogenic protein accumulation, spread, and toxic effects are still poorly understood, and many competing hypotheses regarding them have been presented by researchers. A better understanding of these mechanisms can help inform which hypotheses are more likely to be true, improve prognosis tools, and assist in drug development. Clinically, brain atrophy follows specific spatiotemporal patterns in each neurodegenerative disease, and each disease is linked to specific pathogenic proteins. This observation led to the `molecular nexopathies' hypothesis, which states that clinical phenotypes can be predicted if the specific pathogenic protein variant and the neural network characteristics are both known. However, little computational work has been done that links pathogenic protein mechanisms, the brain's neural network, and clinical phenotypes. In this thesis, I developed computational models for a variety of hypotheses regarding pathogenic protein mechanisms of accumulation, spread, and toxic effects on the brain, which occur at the neuronal scale, while linking them to neuroimaging data, which is acquired at the brain scale. After running simulations with the modelled mechanisms within a neural network, I compared simulation results over time against empirical data for Alzheimer's disease and three genetic variants of frontotemporal dementia. For each disease, the model that best fitted its atrophy progression was found, discovering differences among diseases with regards to what degree each mechanism played a role. I also analysed how each mechanism affected disease progression, discovered each disease's seed location, and found mechanisms that showed potential as candidate targets for therapies, in particular, increasing the firing frequency of neurons.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Computational modelling of molecular nexopathies
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10090889
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