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Title: Stochastic modelling and inference for mitochondria in health and disease
Author: Aryaman, Juvid
ISNI:       0000 0004 7658 7039
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Mitochondria are crucial organelles for complex cellular life: their dysfunction causes multiple devastating heritable diseases, and is associated with healthy ageing. Cells possess a population of mitochondrial DNAs (mtDNA) which are continuously turned over. Consequently, mtDNA mutations may arise and expand throughout the cell. Here, we aim to use stochastic modelling to deepen our understanding of mitochondrial populations, using inference techniques to connect with data, to provide rational hypotheses for biomedical interventions. Mitochondria are not static organelles: they form dynamic networks through organellar fusion and fission. We provide the first analytically-derived bridge between mitochondrial genetics and network dynamics. We show that the rate of increase in cell-to-cell variability in mitochondrial mutant load, as well as the rate of de novo mutation generation, is modulated by the fraction of mitochondria which are unfused from the mitochondrial network. We suggest that cells respond to rising mutant load of a particular mtDNA mutant by initially reducing in volume to maintain the density of unmutated mtDNAs and reduce power demand. When a minimum cell volume is reached, we suggest that cells then switch to power supply production through the induction of bioenergetic pathways. A non-linear relationship between cell size and mitochondrial functionality has recently been experimentally observed. We suggest that a metabolic scaling argument, combined with a simple model of cell death, plausibly accounts for the observed non-linearity. Finally, we explore how replication errors in nuclear DNA during neurodevelopment may seed regions of pathologically mutated neurons in the adult brain. By modelling neurodevelopment as a deterministic branching process, we infer the mutation rate during neurodevelopment from a recent dataset. We extrapolate from our model to suggest that pathological islands consisting of approximately 10^5 neurons may be common.
Supervisor: Jones, Nick ; Johnston, Iain ; Barahona, Mauricio Sponsor: BBSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral