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(BBSRC DTP CASE) Intelligent Engineering of Bacterial Genomes


Project Description

The goal of this project is to establish methodology for the intelligent engineering of biological hosts for the production of high-value drugs and chemicals.. The project provides the opportunity for an ambitious candidate to develop cutting edge skills at the interface of biology, engineering, bioinformatics, and machine learning: the essential tools of the next generation of bioengineers.
The project combines three research areas pioneered by the supervising team, in a unique interdisciplinary approach [1–4]: advanced genome editing; predictive computational modelling; and statistical learning strategies. It exploits capabilities in high-throughput robotics, large-scale screening and machine learning available in the participating groups.
Strain optimization is by far the most time-consuming and expensive bottleneck in the pathway to commercialization of any compound produced by engineered microbes. An intelligent integrated approach to designing microbial factories for the high-level overproduction of bio-derived materials would make a significant contribution to accelerating the way to market across a large number of biotechnology applications.
The application case targeted in this project focuses on creating an optimized Escherichia coli host for the efficient production of polyketide drugs (antimicrobials and anticancer agents). The work will include three closely interlinked work packages, which will interact through an iterative design – build – test – learn cycle in several rounds during the lifetime of the project.
Work package 1: Mutagenesis-driven screening and genome sequencing to identify engineering targets. Large-scale random mutagenesis followed by functional screening and next-generation sequencing of high-performance strains will be used as a powerful way to identify recurring beneficial mutations in structural genes and regulators, which are otherwise difficult to detect.
Work package 2: Model-driven disruption using genome editing. This work package will use available high-quality genome-scale models of E. coli metabolism [5] to predict a set of enzyme-coding genes as targets for disruption or overexpression. These will be integrated with the identified regulatory and structural gene targets from WP1. Engineered strains will be realized in a modular design of experiments, using high-throughput genome editing, and the resulting strain collection will be rapidly phenotyped by metabolomics and targeted analytics in a panel of relevant growth conditions.
Work package 3: Statistical learning to optimize genome editing strategies. We will employ machine learning approaches developed in collaboration with the industrial partner, Cambridge Consultants Ltd., to identify successful editing strategies (taking into account metabolic and regulatory interactions) and guide the next iteration of mutagenesis, strain design and genome editing.
Candidates should have an interest in further developing both their existing wet-lab and programming skills (Python, R).

http://www.cambridgeconsultants.com

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the BBSRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website View Website

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

[1] Breitling R, Takano E (2015) Synthetic biology advances for pharmaceutical production. Curr Opin Biotechnol. 35:46-51.
[2] Carbonell P, Currin A, Jervis AJ, Rattray NJ, Swainston N, Yan C, Takano E, Breitling R (2016) Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle. Nat Prod Rep. 33(8):925-32.
[3] Cummings M, Breitling R, Takano E (2014) Steps towards the synthetic biology of polyketide biosynthesis. FEMS Microbiol Lett. 2014 Feb;351(2):116-25.
[4] Tsigkinopoulou A, Hawari A, Uttley M, Breitling R (2018) Defining informative priors for ensemble modelling in systems biology. Nature Protocols 13:2643-63.
[5] O'Brien EJ, Monk JM, Palsson BO (2015) Using Genome-scale models to predict biological capabilities. Cell 161(5):971-87.

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