EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Jordan Childs

I graduated from Newcastle University with a master's in Mathematics and Statistics in 2020, I then joined the CDT in autumn of 2020.  

I was interested in the CDT because of its focus on combining statistics and computer science, I was also interested in the link between the CDT and industry. I am excited to work with industry and develop skills that I otherwise wouldn't be able to, if I was part of a normal PhD programme. Outside of academics I am interested in art, specifically classical portraits and the artist Rembrandt. I also enjoy watching films and going to the cinema. 

PhD Title

Bayesian Inference for Stochastic Models of Mitochondrial DNA Population Dynamics
Mitochondria are organelles within a cell responsible for producing the majority of energy consumed by the cell. They are unusual in that they have their own genome, mitochondrial DNA (mtDNA), responsible for coding mitochondrial proteins. mtDNA differs from nuclear DNA in many ways. Notably, there are many copies of mtDNA within each mitochondria, and there can be many mitochondrion per cell. As such there can be many thousand copies of mtDNA in a given cell. mtDNA also, continuously replicate and degrade throughout the cell cycle. This continuous replication and degradation gives rise to mtDNA population dynamics if, via inheritance or de novo mutation, a mutate mtDNA molecule appears in the system. When the proportion of mutant mtDNA (mutation load) reaches a sufficiently high level the cells function can be disrupted. 
The mechanisms by which the mutation load reach a dangerous level are currently unknown. In this project we will study stochastic models of mtDNA population dynamics, developing new ones and comparing simulated results with large single data sets. For this we will use likelihood-free Bayesian inference requiring tens of thousands of simulations, as such we will take advantage of parallel and cloud computing and scientific programming languages.