Cell factories are clearly starting to play an increasingly important role in the production of fine or commodity chemicals. However, the more complex the molecule of interest, the harder it is to actually engineer a high-producing cell factory. Numerous genes affect production of the final product and modifying many steps and testing the large number of resulting strains still gives metabolic engineers trouble.
RNA-based sensors offer a very exciting new way to screen large libraries, since the sensor can be used to detect the molecule of interest and that signal can be translated into a screenable or selectable result. At Biosyntia we use this technology to speed up cell factory development.
This project will involve synthetic biology aspects of developing a tuneable RNA-based sensor for the amino acid tryptophan (in collaboration with TUD), which will then be applied in a cell-factory setting to sense actual production. Metabolic engineering strategies will be used to modify the tryptophan biosynthetic pathway and the sensor will be used to screen for a high-yielding cell factory. The work will involve techniques such as FACS, advanced cloning techniques and construction and screening of libraries, metabolic engineering, use of high-throughput robotic assays, and small scale fermentations. The fellow will also get a firsthand look at the bio-business aspects of how RNA-based sensors can reduce development costs and timelines.
We would like to explore the possibilities and opportunities RNA based sensors could offer to facilitate microbial screening and strain construction. The project will be part of the WP3 program. Based on a good overview of the current status of the field we would like to develop specifically biosensors for acetyl-CoA flux measurements in fungi related to industrial relevant product pathways.
Project 10: Characterisation of product yield heterogeneity of cell factories during fermentation, Technical University Denmark
A major obstacle during large scale fermentations is that heterogeneity develops within the population leading to suboptimal yields. Subpopulations that do not produce as much of the desired chemical take over the fermentation, since they are more fit than highly producing cells. We wish to characterize these processes from a population level and a single cell level using sensitivity biosensors and single cell genomics. Our goal is to understand the general paths that lead to such drift and develop new approaches to counteracting this evolutionary challenge.