Project Outline

Engineering microbial cell factories using synthetic biology enables the production of new therapeutics or sustainable routes to the manufacture of high value chemicals from agricultural by-products. However, at present, engineering production strains is a time intensive and expensive process due to multiple, interacting challenges. Key challenges include (i) difficulties in predicting the impact of novel pathways on the host’s metabolism and wider physiology, (ii) the build-up of toxic pathway intermediates, and (iii) poor long-term performance. In addition to these cellular constraints, microbial cell factories are subjected to industrial constraints (including heterogeneity, fermentation strategy, and cost) which further limit performance in commercial settings.

We tackle these roadblocks to the industrialization of synthetic biology by developing predictive mathematical multi-scale models of microbial growth and fermentation strategy to inform our engineering of biological systems. We are using these methods to design and build autonomous genetic control systems which integrate stress responses to dynamically balance growth and biomanufacturing processes. We have the following active interdisciplinary projects with experimental groups and also welcome students who get in touch with their own proposals. Projects in the group are highly flexible and the ratio of theoretical to experimental work will be tailored to suit the candidate’s interests and background.

Option 1 Optimising membrane protein production.

Structural studies and drug screening require high yields of membrane proteins; however, heterologous expression is a complex multistep process as proteins are synthesised, fold and export. Working with Dr Doug Browning and Dr Alan Goddard from Aston University, this project will determine the key limiting streps for membrane production and the key host stress responses triggered by over production. We will use this information to design and build genetic control systems which enable cells to autonomously tune production to ensure healthy growth.

Option 2 Engineering gene circuits in yeasts.

Engineering synthetic gene regulatory networks lets us guide cellular behaviour. Working in collaboration with Prof. John McCarthy at Warwick, in this project we will engineer novel transcriptional and post-transcriptional gene circuits with improved performance (e.g. improved dynamic ranges). Using cutting edge quantitative methods (including single cell RNAseq) and our high throughput robotics platform we will characterise the robustness of these systems’ performance across different media and growth perturbations. We will use these data to develop and validate predictive models of these systems.

Option 3 Controlling metabolic flux to manage cellular toxicity.

Natural products from fungi or plants are precursors to many drugs but yields from natural systems are often low. However, engineering the production of these compounds by key industrial organisms (e.g. E. coli, S. cerevisiae) is often challenging due the accumulation of toxic intermediates. In this project, working with Dr Fabrizio Alberti at Warwick, we will explore how to design gene circuits which integrate cellular stress responses and modulate metabolic productivity to minimise the accumulation of toxic intermediates. We will work with scale-up experts to assess the efficacy of these systems in industrially relevant multi-litre scale conditions.

Wider training. Students will receive the comprehensive interdisciplinary training needed for modern biotech research including mathematical modelling and programming, molecular biology and metabolic analysis. Students can also benefit from our links with industrial partners who will provide commercial insights and feedback throughout the project. Depending on the scope of the specific project, students may be able undertake short placements in the labs of industrial partners.

References

Example work. Darlington et al. (2018) “Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes”, Nature Communications, 9, e695. Doi 10.1038/s41467-018-02898-6.

Background review. Boo et al. (2019) “Host-aware synthetic biology”, Current Opinion in Systems Biology, 14, pp. 66-72. doi 10.1016/j.coisb.2019.03.001.

Background review. Hartline et al. (2021) “Dynamic control in metabolic engineering: Theories, tools, and applications”, Metabolic Engineering, 63, pp. 126-140. doi 10.1016/j.ymben.2020.08.015.

Techniques

• Mathematical skills. Dynamical modelling, flux balance analysis, optimisation, other techniques as needed from Systems and Control Engineering.
• Programming languages. Python, MATLAB (with initial and advanced training provided).