Bayesian Approaches for the Mechanistic Analysis of Protein Aggregation Kinetics

The formation of amyloid fibrils is a hallmark of a wide range of prevalent and devastating human disorders, including Alzheimer’s disease and Parkinson’s disease. With effective treatments generally lacking, these conditions not only cause tremendous personal suffering but also impose a substantial and growing socioeconomic burden worldwide. Understanding the mechanism of amyloid formation and how exactly this process is linked to pathology is crucial to inform drug discovery efforts and to ultimately tackle these diseases. In this work, I have demonstrated how mathematical modelling based on chemical kinetics can be used to overcome current challenges in deducing the microscopic mechanism of amyloid formation, in particular in the context of how external perturbations may affect this process. Furthermore, I extended this state-of-the-art approach to account for the stochastic nature of protein aggregation. Stochastic effects affect amyloid formation in two main ways, namely through (i) variability at the molecular level and (ii) the macroscopically apparent variation in the rates of repeated experiments. Bayesian statistics provides a principled, versatile, and robust theoretical framework to account for such variability. In addition, Bayesian inference allows for the incorporation of information gained in prior experiments and enables the estimation of uncertainty in the results obtained and conclusions drawn. Using Bayesian inference in concert with automated image analysis, I addressed point (i) and investigated how heterogeneity in the fibril population affects their ability to self-replicate. To address point (ii), I developed a novel Bayesian hierarchical model to appropriately treat and quantify the systematic variability between repeated experiments observed in most kinetic data sets in the field. Having such a conceptual framework is of key relevance in the context of disease, where data are generally sparser and more heterogeneous than in vitro and where even small variations relative to the aggregation time scale can have a significant effect on patient outcome and quality of life. Thus, my work lays the foundation for the effective and transparent mechanistic analysis of in vitro, in vivo, and ultimately patient protein aggregation data.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2022

Author dc:creator

• Zimmermann, Manuela

Advisor dc:contributor.advisor

• Knowles, Tuomas

• Alzheimer's disease
• amyloid formation
• Bayesian inference
• chemical kinetics
• mechanistic modelling
• protein aggregation
• secondary nucleation

Licence

• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

Author Identifier

0000-0002-3443-2050

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/344740