Novel semi-supervised Bayesian learning to rapidly screen new oligonucleotide drugs for impurities.
This is an exciting opportunity to join Compass’ 4-year programme with integrated training in the statistical and computational techniques of Data Science. You will be part of a dynamic cohort of PhD researchers hosted in the historic Fry Building, which has recently undergone a £35 million refurbishment as the new home for Bristol’s School of Mathematics.
This fully-funded 4 year studentship covers:
- tuition fees at UK rate
- tax-free stipend of up £19,609 per year for living expenses and
- equipment and travel allowance to support research related activities.
This opportunity is open to UK, EU, and international students.
AstraZeneca is a global, science-led biopharmaceutical business whose innovative medicines are used by millions of patients worldwide. Oligonucleotide-based therapies are advanced novel interventions with the potential to provide a step-change in treatment for many. Nevertheless, as oligonucleotides are large complex molecules they are currently very difficult to profile for impurities, as the analysis is labour intensive and the data complexity is high.
About the Project
The aim of this PhD is to develop Bayesian data science methodology that does this automatically, accurately, and delivers statistical measures of certainty. The challenge is a mathematical one, and no chemistry, biology or pharmacological background is expected of the student. More specifically, we have large batches of mass spectrometry data that will enable us to learn how to characterise the known oglionucleotide signal and deconvolute it from a number of known and unknown impurities longitudinally, in a semi-supervised learning framework. This will allow us to confirm the overall consistency of the profile, identify any change patterns, trends over batches, and any correlation between impurities.
The end goal is to establish a data analytics pipeline and embed it as part of routine analysis in AstraZeneca, so impurities can be monitored more closely and more precisely. The knowledge can then be used to identify possible issues in manufacturing and improve process chemistry by pinpointing impurities associated with different steps of the drug synthesis. This project would also improve the overall understanding of oligonucleotides and therefore, serve as a key step towards establishing an advanced analytical platform.
The PhD will be supervised by statistical data scientist Prof Andrew Dowsey at Bristol in collaboration with AstraZeneca. Prof Dowsey’s group has extensive expertise and experience in Bayesian mass spectrometry analytics (e.g. Nature Comms Biology 2019; Nature Scientific Reports 2016) and leads the development of the seaMass suite of tools for quantification and statistical analyses in mass spectrometry.
Application Deadline is 5.00pm Friday 18 June 2021. Please quote ‘Compass/ AstraZeneca’ in the funding section of the application form and in your Personal Statement to ensure your application is reviewed correctly. Please follow the Compass application guidance.
Interviews are expected to be held in the week commencing 12 July.
We are excited to share Dr Daniel Lawson’s (Compass CDT Co-Director) latest video where he will tell you about the Data Science behind Bristol’s COVID Modelling.
To acknowledge the variety of sectors where data science research is relevant, in March 2021, the Compass students are undertaking a series of workshops led by the Bristol Doctoral College to explore Skills for Interdisciplinary Research. Using the Vitae framework for researcher development, our colleague at BDC will introduce Compass students to the following topics:
Workshop 1: What is a doctorate? A brief history of doctorates in the UK, how they have changed in the past two decades, why CDTs?, what skills are needed now for a doctorate?
Workshop 2: Interdisciplinarity – the foundations. A practical case study on interdisciplinary postgraduate research at Bristol.
Workshop 3: Ways of knowing, part 1 – Positivism and ‘ologies! Deconstructing some of the terminology around knowledge and how we know what we know. Underpinning assumption – to know your own discipline, you need to step outside of it and see it as others do.
Workshop 4: Ways of knowing, part 2 – Social constructionism and qualitative approaches to research. In part 1 of ways of knowing, the ideal ‘science’ approach is objective and the researcher is detached from the subject of study; looking at other approaches where the role of research is integral to the research.
Workshop 5: Becoming a good researcher – research integrity and doctoral students. A look at how dilemmas in research can show us how research integrity is not just a case of right or wrong.
Workshop 6: Getting started with academic publishing. An introduction on the scholarly publishing pressure in contemporary research and it explores what that means in an interdisciplinary context.
This month, the Cohort 2 Compass students have started work on their mini projects and are establishing the direction of their own research within the CDT.
Supervised by the Institute for Statistical Science:
Anthony Stevenson will be working with Robert Allison on a project entitled Fast Bayesian Inference at Extreme Scale. This project is in partnership with IBM Research.
Conor Crilly will be working with Oliver Johnson on a project entitled Statistical models for forecasting reliability. This project is in partnership with AWE.
Euan Enticott will be working with Matteo Fasiolo and Nick Whiteley on a project entitled Scalable Additive Models for Forecasting Electricity Demand and Renewable Production. This project is in partnership with EDF.
Annie Gray will be working with Patrick Rubin-Delanchy and Nick Whiteley on a project entitled Exploratory data analysis of graph embeddings: exploiting manifold structure.
Ed Davis will be working with Dan Lawson and Patrick Rubin-Delanchy on a project entitled Graph embedding: time and space. This project is in partnership with LV Insurance.
Conor Newton will be working with Henry Reeve and Ayalvadi Ganesh on a project entitled Decentralised sequential decision making and learning.
The following projects are supervised in collaboration with the Institute for Statistical Science (IfSS) and our other internal partners at the University of Bristol:
Dan Ward will be working with Matteo Fasiolo (IfSS) and Mark Beaumont from the School of Biological Sciences on a project entitled Agent-based model calibration via regression-based synthetic likelihood. This project is in partnership with Improbable
Georgie Mansell will be working with Haeran Cho (IfSS) and Andrew Dowsey from the School of Population Health Sciences and Bristol Veterinary School on a project entitled Statistical learning of quantitative data at scale to redefine biomarker discovery. This project is in partnership with Sciex.
Shannon Williams will be working with Anthony Lee (IfSS) and Jeremy Phillips from the School of Earth Sciences on a project entitled Use and Comparison of Stochastic Simulations and Weather Patterns in probabilistic volcanic ash hazard assessments.
Sam Stockman will be working with Dan Lawson (IfSS) and Maximillian Werner from the School of Geographical Sciences on a project entitled Machine Learning and Point Processes for Insights into Earthquakes and Volcanoes