Biodiesel production is usually accompanied by the production of 10% (w/v) glycerol as main low-value by-product, making it not yet economically competitive to petroleum-based processes. Recently, Ustilaginaceae fungi have attracted more attention due to their abilities of using crude glycerol to produce chemicals of industrial interest. Unlike established filamentous fungi, many Ustilaginaceae strains can grow in haploid and unicellular form, which are remarkably advantageous for industrial applications. Of note, U. trichophora was reported to have the highest titre for microbial malate production, even if the yield is still low. If the carbon lost during cultivation is suppressed, U. trichophora will be a novel candidate for industrial malate production and contribute directly to crude glycerol valorisation. However, the metabolic network and its function are not described for any Ustilaginaceae species. Isotope-assisted metabolomics approaches are powerful in exploring the metabolic network operation. By capturing the snapshot or the kinetics of metabolite pools, these approaches can guide metabolic engineering strategies to alter metabolic flux distribution and maximize target compound production. Therefore, this study aims to decipher the structure and dynamics of the metabolic networks of U. trichophora by using isotope-assisted metabolomics approaches. Results obtained in this research will guide ongoing efforts in metabolic engineering to maximize malate production from crude glycerol of U. trichophora. Further contributions will be made beyond the envisaged industrial applications, as the Ustilaginaceae are also investigated in the context of host-pathogen interactions and fundamental cell biology.
Aromatic molecules are integral to every aspect of chemistry. In general, the preparation of these compounds is approached via the use of aromatic precursors that are progressively functionalized using reactivity like electrophilic/nucleophilic aromatic substitution. Derivatives equipped with electron withdrawing groups (ester, ketone...) are particularly used in synthesis but are often challenge to prepare. This is because aromatic chemistry has to follow some stringent selectivity rules that activate or deactivate specific positions. This means that installing a functionality on a deactivated position (e.g. meta in an electron rich aromatic) is very difficult and requires many steps. This project seeks to address this challenge by developing an innovative approach to aromatic synthesis using simple Diels-Alder cycloadditions to construct a six-carbon cyclic framework followed by an unprecedented desaturation process. In particular, we will demonstrate the integration of three catalytic modes, photoredox + cobalt + HAT catalysis, as blueprint to progressively desaturate Diels-Alder cycloadduct to poly-functionliased aromatics. This reactivity will streamline the preparation of many high-value but difficult to make aromatic products, will be used in late-stage functionalizations and will substantially expand the fields of dual photoredox–cobalt catalysis and boryl radical chemistry. This research capitalizes on recent developments of the Leonori group that has experience in the development of methodologies based on both desaturation and boryl radical reactivity. The completion of such an innovative and ambitious project at RWTH Aachen University will be facilitated by generating, transferring, sharing and disseminating knowledge, and will enhance my future career following the training plan envisioned.
The goal of this project is to develop an algorithmic theory of similarity between graphs. Graphs are versatile models for representing complex data ranging from chemical molecules to social interactions. Dealing with graphical data and enabling modern data analysis techniques, a fundamental task is to compare graphs and to measure their similarity, preferably in a semantically meaningful and algorithmically efficient way. However, it is not clear at all how to achieve this. In many application areas, for example, computer vision, database systems, and formal verification, researchers have proposed (often ad-hoc) solutions to this problem tailored for the specific application, but a general theory is missing. We will develop such a theory in this project. Similarity of graphs has many different facets. We will identify the common core of different approaches to similarity, but also exhibit their differences. We will design methods for comparing different similarity measures and for obtaining a semantic understanding of similarity. We will develop criteria for the suitability of various similarity measures for different types of applications. A particular focus of our research will be on efficient algorithms for computing similarity. There is little use in having a perfect similarity measure if we have no efficient way of determining how similar two graphs are. A classic algorithmic problem in this context is the graph isomorphism problem of deciding whether two graphs are structurally identical. Determining the precise computational complexity of this problem, or of the equivalent problem of computing all symmetries of a graph, is regarded to be one of the most important open questions in theoretical computer science. Building on recent progress, we will design new algorithms breaking barriers towards a polynomial-time algorithm for the isomorphism problem.