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Mechanistic modelling of presynaptic dynamical systems
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Norman, Christopher Alexander (2022) Mechanistic modelling of presynaptic dynamical systems. PhD thesis, University of Warwick.
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WRAP_Theses_Norman_2022.pdf - Submitted Version - Requires a PDF viewer. Download (96Mb) | Preview |
Official URL: http://webcat.warwick.ac.uk/record=b3847759
Abstract
The rapid and diverse signals passed between neurons are the result of complex molecular interactions at their synaptic connections. The dynamics of synaptic proteins are central to all information processing in the brain, yet their precise arrangement and co-operation during physiological activity is unclear. This thesis presents novel mathematical and computational modelling tools which elucidate the activity of synaptic protein structures, advancing our understanding of synapses as the brain’s fundamental computational unit. Chapter 1 introduces the biomechanics of synapses as they are currently understood, and the stochastic Markov chains which are used in this thesis to model them. To efficiently generate Monte Carlo predictions from these Markov chain models, a specialisation of Gillespie’s stochastic simulation algorithm was derived, as described in chapter 2. This method was used to demonstrate a mechanistic link between stimulation frequency and long-lasting asynchronous release at a central synapse. In order to query the organisation of proteins at the interface of synaptic vesicles and the cell membrane, a modular, mechanistic modelling framework that could account for a range of different protein architectures was designed. Chapter 3 describes the construction of these models and their predictions for how different calcium-sensitive proteins synergistically regulate vesicle fusion. Finally, in chapter 4, a model of the activity of calcium channels bearing the migraine mutation S218L was constrained using their key dynamical characteristics. This was combined with vesicle fusion modelling to provide a valuable framework that directly links disruptions of channel function with synaptic transmission. These simulations were used to predict previously unreported synaptic behaviours under S218L which contribute to its overall pathology.
| Item Type: | Thesis (PhD) | ||||
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| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology |
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| Library of Congress Subject Headings (LCSH): | Neural transmission -- Mathematical models, Neural transmission -- Data processing, Markov processes | ||||
| Official Date: | April 2022 | ||||
| Dates: |
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| Institution: | University of Warwick | ||||
| Theses Department: | Mathematics for Real-World Systems Centre for Doctoral Training | ||||
| Thesis Type: | PhD | ||||
| Publication Status: | Unpublished | ||||
| Supervisor(s)/Advisor: | Timofeeva, Yulia ; Volynski, Kirill | ||||
| Format of File: | |||||
| Extent: | xii, 192 leaves : colour illustrations, charts | ||||
| Language: | eng |
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