The ambition of this project is to establish a generic methodology for the intelligent engineering of biological hosts for the production of high-value chemicals and materials.
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 “pre-fabricated” host strains for the high-level overproduction of industrially relevant chemical classes would make a major contribution to overcoming this bottleneck and accelerating the way to market for 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.
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.
Model-driven disruption using genome editing. This work package will use available high-quality genome-scale models of E. coli metabolism  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.
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.
The project provides comprehensive interdisciplinary training at the interface of biology, engineering and bioinformatics, essential for the next generation of biotechnology scientists.
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.
Contact for further Information
For more details contact Professor R Breitling ([email protected]
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, Baker SM, Breitling R (2017) Respectful Modeling: Addressing uncertainty in dynamic system models for molecular biology. Trends Biotechnol. in press.
5. O'Brien EJ, Monk JM, Palsson BO (2015) Using Genome-scale models to predict biological capabilities. Cell 161(5):971-87.