Background
Polyunsaturated fatty acids (PUFAs) are essential for human nutrition, yet conventional marine and agricultural sources face ecological and supply constraints. Oleaginous yeasts provide scalable microbial platforms for sustainable PUFA production. Engineering these hosts for enhanced lipid accumulation typically relies on iterative push–pull metabolic modifications. Genome-scale metabolic models (GSMMs) are powerful tools for identifying bottlenecks, but standard models often fail to capture phase-dependent effects of gene expression changes. Enzyme-constrained GSMMs (ecGSMMs) address this limitation by incorporating proteomic data, enabling more accurate prediction of metabolic capacities under different growth phases.
PUFA accumulation can be lipotoxic during exponential growth, whereas nitrogen-limited phases favour lipid storage. Proteome reallocation under nitrogen limitation is driven by coupled transcriptional and translational changes. Pairing transcriptomics with proteomics can resolve these dynamics and reduce reliance on costly repeated proteomics across design–build–test–learn (DBTL) cycles. Integrating genomics (genome sequences, gene annotations and comparative genomics) with genome-scale modelling further allows systematic prediction of production efficiency across candidate hosts.
Hypothesis & Aims
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.
Success Criteria & Impact
Statistically significant improvements in PUFA titre and lipid content relative to baseline strains, together with validated genome-scale models that accurately predict production efficiency. The open-source framework will lower barriers to adopting protein-constrained and genomics-informed modelling in industrial biotechnology and identify transferable phase-specific targets for PUFA production in oleaginous yeasts.
This project is expected to start in September 2027.
Before you apply: You should contact the project supervisors to learn more about the project and discuss whether it may be suitable for you.
How to apply:
To be considered for this project, you must submit a formal application through our online application portal. If you already have an applicant account, use this link to open an application for FSE PhD President's Doctoral Scholarship. If you do not yet have an applicant account, follow the account setup instructions. Once your account is set up, use this link to open an application for this programme.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing your motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: Applicants are expected to hold (or about to obtain) a minimum upper second-class undergraduate honours degree (or equivalent) in a relevant subject area. Research experience in Synthetic Biology, Molecular Biology, Bioprocess Engineering and/or Chemical Engineering is desirable.
FSE_PDS
Funding Notes
Funding covers tuition fees, 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.
