Applications are invited for a competition-funded three year PhD studentship to commence in October 2019.
The project will focus on:
• Incorporating non-native pathways and to implement innovative and computational-based metabolic design in Pseudomonas putida including oxidative catabolic pathways for lignin degradation.
• Mathematical techniques and model annotation will be used to constrain the allowable flux rate for the enzymes catalyzing each step in the genome-scale metabolic network.
• Design and apply machine learning and multi-level optimisation algorithms to predict the conditions that are optimally using lignin-derived carbon sources for optimal rhamnolipid biosynthesis.
• Conduct a genome-scale comparative analysis of the metabolomic and lipidomic landscape of selected Pseudomonas species.
• In silico design of Pseudomonas will be assessed using advanced sensitivity techniques and metabolic interventions for substantially enhanced rhamnolipid production.
The growing demand for rhamnolipids production owes to its wide range of industrial and biomedical applications, including pharmaceuticals, cosmetics and detergents. They are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a model organism with greater metabolic versatility and potential for industrial applications. P. putida is arguably the non-pathogenic chassis with the best potential for flexible production of rhamnolipids. Also, it is a versatile organism for incorporating non-native pathways and to implement innovative and computational-based metabolic design. Lignin-derived aromatic carbon sources can be metabolised by P. putida using oxidative catabolic pathways. One of these pathways, namely the β-ketoadipate pathway, has been recently integrated into the P. putida metabolic network (2). Due to the large size of the metabolic network, and the vast array of possible growth medium composition, the metabolic capabilities of P. putida growth on lignin-derived growth media, and their potential for rhamnolipids biosynthesis can be fully investigated only with multi-omics modelling, statistical, metabolic and biosynthetic engineering approaches. Mathematical techniques and model annotation will be used to constrain the allowable flux rate for the enzymes catalyzing each step in the genome-scale metabolic network. We will then design and apply machine learning and multi-level optimisation algorithms to predict the conditions that: (i) are optimally using lignin-derived carbon sources; (ii) show potential for optimal rhamnolipid biosynthesis. The proposed in silico design of Pseudomonas will be assessed using advanced sensitivity techniques, robustness and control analysis, with methods adapted from network theory to give insights into the model. Regression methods will be adopted to predict the behaviour of the microorganism in untested conditions. The ability to adapt to such conditions will be assessed by evaluating the changes in the proteins of the outer membrane, key players in the adaptation of Pseudomonas to environmental perturbations and in the production of rhamnolipids. Based on our initial results, which have suggested metabolic interventions for substantially enhanced rhamnolipid production, we will build condition-specific models by integrating proteomic and transcriptomic data. We will then apply multi-level optimisation algorithms to maximise rhamnolipid biosynthesis and growth rate (biomass), while simultaneously minimizing by-product formation. Exploring and characterising computationally the metabolism of P. 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.
Software requirements: Matlab, Python, R. Training and tutorials will be provided on metabolic modelling computational techniques.
General admissions criteria
The project requires a candidate with a good first degree (minimum 2.1 or equivalent) in Biology, 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.
We require English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
Specific candidate requirements
We are looking for a talented student with a strong background in Computational Biology, Genome scale modelling and expertise in Bioinformatics.
How to Apply
We’d encourage you to contact Dr Pattanathu Rahman ([email protected]
) or Dr Sam Robson ([email protected]
) 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 making 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.
When applying to the University, please quote project code: BIOL4580219
1. Occhipinti, A., Eyassu, F., Rahman, T. J., Rahman, P.K.S.M. & Angione, C. 31 Oct 2018 (Accepted for publication) In : PeerJ. Link: https://researchportal.port.ac.uk/portal/en/publications/in-silico-engineering-of-pseudomonas-metabolism-reveals-new-biomarkers-for-increased-biosurfactant-production(150338ff-39ea-4ba1-aeae-c4d25bd216e1).html
2. Rahman, PKSM & Sekhon Randhawa, KK 2015, 'Editorial: Microbiotechnology based surfactants and their applications' Frontiers in Microbiology, vol. 6, 01344. DOI: 10.3389/fmicb.2015.01344
3. Randhawa, KKS & Rahman, PKSM 2014, 'Rhamnolipid biosurfactants-past, present, and future scenario of global market' Frontiers in Microbiology, vol. 5, 454. DOI: 10.3389/fmicb.2014.00454
4. Rahman, P.K.S.M. and Angione, C. (2017). Condition-specific engineering of Pseudomonas metabolism. BBSRC- Report Link: http://cbmnetnibb.group.shef.ac.uk/outputs/case-studies/business-interaction-vouchers/condition-specific-engineering-of-pseudomonas-metabolism/