In our research, we are going to combine experimental and computational approaches to create efficient yeast-based cell factory platform strains for bio-based chemical production. We will focus on fundamental studies of energy efficiency and translational control and use this information to optimize biochemical production.
The research group will focus on three main areas:
1. Cell design and pathway optimization. We are going to use novel regulatory circuits and biosensors to optimize the production of biochemicals.
2. Translational control of protein abundances. We are going to study the initiation and elongation of translational efficiency to elucidate the diversity in translational control.
3. Energy efficiency and cellular maintenance. We aim to understand the differences in energetic efficiency between different strains and use this information to construct more efficient platform strains.
There are hundreds of different microbes in the human gut. Many could be useful as probiotics, but growing them in larger amounts is technically difficult. Most of these microbes are obligate anaerobes and do not tolerate oxygen. During this project we will develop a platform technology for growth medium optimization for anaerobic bacteria. This technology speeds up the discovery process and paves the way for the next generation probiotics.
Microbial cell factories that are able to utilize industrial byproducts or low-value sugars and efficiently produce a variety of chemicals will play a major role in this process, where yeast is one of the preferred host organisms. There exists a number of host organisms which are able to consume a large variety of substrates, however, their efficiency to grow or produce desired products might be non-optimal. Therefore, in the current project we aim to study a variety of non-conventional yeast strains and characterize their ability to consume less preferred carbon sources like xylose and phenolic compounds, abundantly available in lignocellulosic biomass.
Many industrial biotechnological processes are carried out by consortia of bacteria, rather than single strains. To improve performances of such processes, the biotech industry currently relies mostly on a screening-based selection of isolated strains with desired properties. However, these properties are very often influenced by other consortium members in unknown ways. Screening the consortia is challenging because only a tiny subset of all the many possible combinations can ever be tested. There is, therefore, a need to develop methods that can predict the performance of strains in consortia, on the basis of the genome and selected phenotypic traits. This project aims to develop an integrative bioinformatics and modelling approach to predict microbial community functioning from the properties of the constituent isolates. We will do this through a real industrial use case: the design of microbial cultures for the production of yogurt.