Birgitta Ebert

Bayesian Optimisation of Media Compositions for Saccharomyces cerevisiae to Increase the Yield of High Value Natural Products

Optimising media for the microbial production of high-value chemicals is an important but complicated endeavour. The minimal media used for industrial applications often have ten or more compounds, all of which can affect the final yield of the target compound. These compounds range from relatively inexpensive salts to trace minerals and expensive organic compounds like vitamins. To reduce production costs, it is thus essential to find the cheapest combination of components that produces the highest amount of product. However, changes in the concentration of one component can have unforeseen consequences on the required concentrations of other components. This makes brute force optimisation of media compositions unattractive, since the amount of lab work required to screen all possible combinations is too demanding.

An alternative to testing all conditions is to strategically explore the combinatorial space through Bayesian optimisation, a statistical framework that can find the underlying relationships in a set of complex variables to optimise them. Bayesian models can also guide researchers through an active learning workflow by suggesting the optimal testing space for the next round of experiments for optimal information gain, thus drastically reducing the necessary lab work.

The goal of this work is to find the optimal media composition for the overproduction of squalene and squalene derivatives in the chassis organism Saccharomyces cerevisiae. S. cerevisiae is well established as a host organism for the overproduction of triterpenoids due to its well-studied metabolism and genome and the ease of genetic manipulation. S. cerevisiae also natively produces squalene and squalene overproduction strains have been described in the literature.

The Honours or Master student working on this project will conduct a literature review to identify important media components for squalene overproduction. Based on this, they will then perform high-throughput screening of media compositions using lab automation such as liquid handling robots and Growth Profilers for small-scale cultivation of up to 960 parallel growth experiments. The generated data will be analysed using Bayesian statistical models developed by the student in collaboration with computational experts. The results are then used to optimise the media composition further until an optimum is reached.

Redox engineering for carbon-efficient biotechnological processes

Redox cofactors play a central role in the metabolism of living organisms. The most widely used cofactor is NAD(P)H. In the central carbon metabolism, the oxidised form NAD(P)+ is the primary acceptor for electrons from carbon oxidation. These electrons are then fed into the electron transfer chain, powering the respiratory system for ATP and thus energy generation. Although efficient, this system leads to CO2 formation via the oxidation of carbon metabolites and, hence, to CO2 emissions during biotechnological processes. In this project, we investigate alternative systems for NADH regeneration with electrons from sustainable energy sources, ultimately decoupling energy and carbon metabolism. Our focus lies hereby on hydrogenases. These enzymes use electrons from hydrogen to generate NADH instead of carbon metabolites, while hydrogen can be produced solely from water and electrons from renewable energy sources. Implementing efficient hydrogenase-based NADH regeneration systems in vivo should lead to more carbon-efficient and sustainable biotechnological processes for a greener bio-based future.

We’re looking for a motivated student interested in carbon-efficient biological processes. The project offers options for working in molecular biology, bioprocess development, and robotics. Please get in touch with me for further information.

Microbial fatty acid derivative production from CO2-derived intermediates

This project uses synthetic biology to develop microbial fermentation for producing fatty acid derivatives from CO2-derived acetate/ethanol. Target molecules include methyl ketones for aviation fuel, plasticizers, and monomers, addressing critical gaps in renewable chemical production for a sustainable industry.

Brewing natural products with yeast

The yeast Saccharomyces cerevisiae is widely used in fermentation to produce wine, beer, and bioethanol. However, this well-researched microbe can also be efficiently engineered for the production of complex natural products. Well-known examples are the anti-malaria drug artemisinin are the ant-cancer drug paclitaxel.

In this project, we are interested in the production of triterpenoids, the largest group in the natural product class. Many of these molecules have biological activities that make them promising candidates for pharma, nutraceutical, or cosme(ceu)tical applications.

We have engineered a superior S. cerevisiae platform strain capable of the synthesis of diverse triterpenoids at the gram-scale level. In this project, we aim to expand the product spectrum to alpha-amyrin type triterpenoids with anti-ageing and anti-obesity properties that are used are investigated for use in cosmetics and pharmaceuticals.

You will recombinantly express plant enzymes in the yeast chassis to enable the production of a few target products. You will further address a major bottleneck in the production of triterpenoids, the intracellular accumulation of the products, which results in cell toxification and low production efficiency. We are following alternative and complementary approaches including the expression of recently identified transporter, in situ extraction and optimization of the intracellular product trafficking route.

You will gain in-depth knowledge on the metabolism of S. cerevisiae and practical skills in metabolic engineering and synthetic biology including, molecular biology, omics analyses, microscopy, fermentation, and analytics.

Honours and (under)graduate students are welcomed to work on specific subprojects.

Please contact me for further information.