Bioprocess development for biosurfactant mediated plastic degradation
PhDs and postgraduate research
Funded PhD Project (UK and EU Students Only)
Centre for Enzyme Innovation (CEI), School of Biological Sciences
14 July 2019
This project is now closed. The details below are for information purposes only. View our current projects here.
This fully-funded studentship is available to UK and EU students only and covers tuition fees and an annual maintenance grant of £15,009 (RCUK 2019/20 rate). The supervisors are Dr Pattanathu Rahman, Dr Claudio Angione (Teesside University) and Dr Sam Robson.
This studentship is based in the newly developed Centre for Enzyme Innovation. The Centre focuses on the discovery, engineering and deployment of enzymes with potential application to the circular economy.
The project will focus on:
Bioprocess Optimisation and Development in Microbioreactors and scale-up
Incorporating non-native pathways and to implement innovative and computational-based metabolic design in bacteria
Regulate the flux rate for the enzymes catalyzing each step in the genome-scale metabolic network
Predict the conditions that are optimally using different carbon sources for biosurfactant production
Access Computer modelling to create more efficient bacteria, which can secrete the enzyme to degrade plastics
Acceleration of Enzymatic Degradation of PET by Surface Coating with biosurfactants.
Determination of biosurfactant mediated plastic degradation
Biosurfactants increase surface area of hydrophobic water-insoluble substances, increase the water bioavailability of such substances and change the properties of the bacterial cell surface. Because of their potential advantages, biosurfactants are widely used in many industries such as agriculture, food production, chemistry, cosmetics and pharmaceutics. Surface-active agents can enhance enzyme activity against solid PET substrates by more than 100-fold.
Computational methods for metabolic engineering are helpful to model and optimise biological models that can solve one of the planet's most-pressing environmental problems such as plastics. This computational model can then be used to engineer bacteria to efficiently manufacture these industrially relevant compounds such as enzymes and soaps for degrading plastics.
Exploring and characterising computationally the metabolism of Pseudomonas putida at genome-scale and in a condition-specific fashion will elucidate optimal growth medium and metabolic interventions to optimise the utilisation of aromatic carbon sources for bioremediation and biotechnological purposes.
In addition to a full program of training provided by the Graduate School, specialist training will be provided for a range of in-house instruments and techniques. Matlab, Python, R. Training and tutorials will be provided on metabolic modelling using computational methods.
- The project requires a candidate with a good first degree (minimum 2.1 or equivalent) in Biology, Microbiology, Biochemistry, Bioinformatics, Biotechnology, Computational Biology or a related subject, and a desire to excel as a disciplined scientist within a cohesive research team.
- Potential applicants with a Masters-level qualification, or equivalent experience in a relevant field, are strongly encouraged to apply.
- English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
- We are looking for a talented student with a strong background in Microbiology, Cloning and expertise in Bioinformatics.
How to apply
We’d encourage you to contact Dr Pattanathu Rahman (email@example.com) or Dr Sam Robson (firstname.lastname@example.org) to discuss your interest before you apply, quoting both the project code and the project title.
When you are ready to apply, you can use our online application form and select ‘ Biological Sciences’ as the subject area. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.
Our ‘How to Apply’ page offers further guidance on the PhD application process.
Please note, when applying for this project, please quote the project code: BIOL4580219.