FSE PhD President's Doctoral Scholarship: Phase-optimised PUFA Production in Oleaginous Yeast via Transcriptomics-Guided Enzyme-Constrained Metabolic Modelling

Doctorat / PhD Département : Chemical Engineering Type de contrat : Full Time,Fixed-Term/Contract Date limite de candidature : 2026-12-18T00:00:00+00:00

This 4-year PhD studentship is open to both Home (UK) and international applicants. The successful candidate will receive an annual stipend at the UKRI minimum rate (currently £21,805 per year) plus £1,000/annum enhancement, and a research training support grant of up to £20,000 for the full 4-year programme. This project is in competition for funding with other projects. Usually, the projects that receives the best applicants will be successful. We hypothesise that defining empirical reaction-specific translational efficiency operators (TE = [E]/mRNA) within phase-specific ecGSMMs will enable resource-balanced cassette design and allow inference of enzyme abundances from RNA-seq data alone in subsequent iterations. Combined with genomic information and genome-scale modelling, this framework will predict production efficiency and identify phase-optimised targets. The project will: Estimate the mRNA expression levels required to achieve desired increases in reaction capacity. Categorise transcriptionally versus translationally bottlenecked genes under nitrogen limitation. Deliver predictive models of PUFA production efficiency across selected oleaginous yeasts. Methodology (DBTL) Design & Build Round 1: Curate a library of native phase-responsive promoters from selected oleaginous yeasts (examples include Yarrowia lipolytica , Rhodotorula toruloides or Lipomyces starkeyi ). Characterise promoter kinetics using RNA light-up aptamers. Subsequent rounds will design phase-specific expression cassettes informed by modelling outputs and assemble them using modular cloning toolkits appropriate to the chosen host(s). Test Perform controlled fermentations under exponential and nitrogen-limited conditions. Measure process kinetics, lipid titres (GC-MS), phase-resolved RNA-seq and LC-MS proteomics to calibrate TE operators. Later rounds will rely primarily on RNA-seq and process data. Genomic data (genome sequences, annotations and comparative analyses) will support model construction and strain selection. Learn Apply enzyme-constrained flux scanning based on enforced objective flux (ecFSEOF) and kinetic flux control coefficient analysis on protein-constrained GSMMs to identify systemic pathway demands and kinetic bottlenecks. Verify protein headroom for overexpression. In subsequent iterations, infer enzyme states from TE operators and new RNA-seq data, rank rate-limiting constraints via dual variables, and update models to guide further engineering. Genome-scale models will explicitly predict production efficiency (titre, yield and rate) under different genetic and process scenarios Before you apply: Please contact the main project supervisor, Dr Martin ,…