Lab rotation project description
The mini project will focus on designing and developing genetically encoded biosensors relevant to synthetic metabolic pathways for production of added value chemicals. It will serve as an introduction to the available gene tools and analytic techniques to be deployed in PhD project.
The student will:
(i) receive a safety induction to cover safe laboratory practice within the confines of SBRC laboratories and equipment within the Centre;
(ii) be trained in the use of cultivation of E. coli and C. necator;
(iii) learn genetically encoded biosensor design and application;
(iv) be trained in fluorescence based assays for developing biosensor systems;
(v) be introduced in analytical techniques for gas and metabolite analysis, such as HPLC, GC, and MS.
LR1, LR2 and LR3
Non-genetic heterogeneity in isogenic cell populations can be caused by stochastic gene expression, uneven cell division and other factors (Raj & van Oudenaarden, 2008; Huh & Paulsson, 2011). Besides, it is believed that biological processes including those that control gene expression are not entirely accurate at the molecular level. The cell-to cell variation in gene expression, as well as other factors, contribute to the fluctuations at protein and, importantly, metabolite levels. Consequently, the biosynthetic ability of individual cells can be greatly affected and vary. This phenomenon has been verified by isolating subpopulations of both low- and high-performing variants in the isogenic population of microbial cultures (Xiao et al., 2016).
Metabolic heterogeneity has only been studied to a limited extent. This is mainly because of a lack of metabolite detection and quantification techniques at the single cell level. However, genetically encoded biosensor technology is now available to provide an alternative avenue for exploring the metabolite heterogeneity problem. This project will investigate the variation in cell-to-cell metabolite concentrations, how these fluctuations affect the performance of metabolic pathway systems such as beta-alanine biosynthesis (native pathway) and acetoin/butanediol or mannitol biosynthesis (synthetic pathways), and if this can be exploited for improved 3-aminopropionic acid, 2,3-butanediol or mannitol bioproduction.
Escherichia coli and Cupriavidus necator will be used as model organisms. Developed methodologies can potentially be extended to the yeast Saccharomyces cerevisiae. Switchable orthogonal gene expression systems built using synthetic biology approaches will be applied. This will comprise design and construction of inducible and repressible systems for controlling gene expression and will be based on the quantitative analysis of transcription factor (regulator), metabolite (ligand) and DNA interactions. The project will also focus on examining how the DNA-regulator-ligand complex formation contributes to the heterogeneity and how this manifests on the metabolic pathway performance. For this purpose, single cell analytical methods and imaging will be used. In parallel to the experimental work, the project will require use of computational modelling to provide understanding of the regulation at the systems level. This aim will be to investigate whether the cell-to-cell variation can be suppressed by using natural and synthetic genetic circuits (Brophy & Voigt, 2014). Finally, the project will exploit the non-genetic heterogeneity as a naturally inherent trait to enhance the biosynthetic performance and improve the bioproduction through selecting for high–performing cell subpopulations.
The project will combine tools of synthetic biology, use of analytical science technologies, systems biology and computational modelling to analyse metabolic heterogeneity and pathway performance. The theoretical and experimental data acquired in this PhD project will be of value to the fundamental and applied research in academia and industry.
References: Brophy, J.A.N., Voigt, C.A. (2014) Principles of genetic circuit design. Nature Methods, 11, 508-520; Huh, D., Paulsson, J. (2011) Non-genetic heterogeneity from stochastic partitioning at cell division. Nature Genetics, 43, 95-100; Raj, A., van Oudenaarden, A. (2008) Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences. Cell, 135, 216-226; Xiao, Y., et al. (2016) Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis. Nature Chemical Biology, 12, 339-344.