Noise and Evolution in Ageing Cellular Power Stations

We have been investigating (genetic) variation in cellular power stations (mitochondria) using a mix of ideas from stochastic population processes/statistical genetics, statistical physics, statistical inference/machine learning and control theory. An article about our work is here: https://www.imperial.ac.uk/news/264393/cryptic-mitochondrial-dna-mutations-reveal-hidden/

Our goal is to understand human ageing and to inform therapies to combat disease. You can read more about our recent work in our blog: http://systems-signals.blogspot.co.uk/ or links via https://bsky.app/profile/syssig.bsky.social. We believe this is a particularly exciting area to study for two reasons. Not only does 1) mitochondrial (dys)function have deep connections to therapies for conditions like Parkinsons, Diabetes, ageing and Cancer (and we are working on these) it is 2) a topic that, though poorly understood, might be susceptible to the very basic (though mathematically nuanced) models that one constructs in theoretical and mathematical physics/ statistical genetics. This is an area where students can explore a new scientific direction since the medical promise and scientific opportunities easily exceed the number of theorists. Students can develop new evolutionary theory while also contributing to therapeutic design. The student will investigate the construction of basic models and make connections with existing data and the work of our national and international collaborators (Cambridge, UCL, Madrid, Vienna). The applicant will have an opportunity to participate in a multimillion pound 5 year project with the Cambridge experimental groups of Profs Patrick Chinnery and Maria Spillantini on Parkinson's disease and mitochondria.

We will place a substantial emphasis on single-cell sequencing and single-cell transcriptomics (these are among the most active areas of modern biomedicine): this involves aspects of machine learning/AI and bioinformatics. We will be building stochastic models that link to the results of our inference from the transcriptomics. We also link information about cellular state to cellular mutational state using tools from (Bayesian) Machine Learning.

Though the project has experimental collaborators the project does not require experiments by the student or any biological background; however theorists that want to try their hand at experiment are most welcome.

We are looking for passion and scientific intuition. We specifically welcome students from underrepresented groups and really do value a kind environment prioritising purposeful happiness.

You can learn about the research of the systems and signals group on our site:

https://www.imperial.ac.uk/people/nick.jones/research.html

and from our blog:

http://systems-signals.blogspot.co.uk/

or twitter:

https://bsky.app/profile/syssig.bsky.social

Our group is a member of Imperial’s EPSRC Centre for the Mathematics of Precision Healthcare:

http://www.imperial.ac.uk/mathematics-precision-healthcare

and I-X Imperial's new AI initiative:

Homepage - Imperial-X / https://www.imperial.ac.uk/ix-ai-in-science/

Further enquiries - contact with a CV detailing academic performance (i.e. including as detailed as possible information about grades/marks or equivalent):

Nick Jones (Imperial Mathematics) https://www.imperial.ac.uk/people/nick.jones