High level expression of recombinant proteins (rP) is centrally important to modern biotechnology and the production of protein therapeutics, biologics and antibodies. The increasingly important use of novel metabolic pathways in industrial microorganisms also depends on efficient recombinant protein expression of heterologous proteins. However, high-level recombinant protein expression imposes substantial stress on the host cell, significantly slowing growth rates and distorting host gene expression programmes. This is principally because the cell’s ribosome complement must be re-distributed between host cell protein synthesis and translation of mRNAs encoding the recombinant protein. This compromises efficient production of host proteins, distorting metabolism and reducing cellular growth rates.
In this project, using yeast Saccharomyces cerevisiae as a model system, we will investigate how and why this competition for ribosomes between host and recombinant mRNAs occurs and is regulated, to develop an understanding of the factors governing ribosome partitioning between host and rP expression ‘compartments’. We will measure ribosome distribution and loading onto mRNAs using RNA-seq methods, combined with fluorescently tagged ribosomal subunits, and use host-cell engineering to re-direct ribosome loading on mRNAs.
The project will have the key aim of developing new cellular expression platforms for recombinant protein, using synthetic biology engineering of host cells. We will use novel synthetic biology gene circuits to artificially regulate the loading of ribosomes onto recombinant protein mRNAs, to engineer new ways of regulating recombinant protein expression. In this way we will seek to optimise host cell growth, and mitigate cell stress responses. These synthetic circuits will be designed with the help of mathematical models of the circuitry, allowing in silico testing and optimisation of the synthetic circuit design.
Finally, we will integrate experimental measurements of ribosome loading with a whole-cell model developed by the Swain group , and fine-grained mathematical models of translation developed by the Stansfield group . Overall the project will provide advanced training in a range of leading edge methodologies, ranging from synthetic biology, molecular biology, genome editing using CRISPR-Cas9, next-generation RNA sequencing, and mathematical modelling of biological systems. Full training in mathematical modelling will be provided, and no advanced mathematical knowledge is required of applicants beyond an enthusiasm for the use of mathematics in science.
 Weisse et al (2015) Mechanistic links between cellular trade-offs, gene expression, and growth. Proc Natl Acad Sci USA 112: E1038-47. [PMID: 25695966]
 Gorgoni et al (2016) Genome-wide simulation of translation correctly predicts the gene targets of tRNA-specific regulation. Nucleic Acids Res. 44:9231-9244 [PMID: 27407108]