Dokument: Enzyme-constrained genome-scale metabolic modelling of Staphylococcus aureus, analysis methods, and in silico drug toxicity prediction

Titel:	Enzyme-constrained genome-scale metabolic modelling of Staphylococcus aureus, analysis methods, and in silico drug toxicity prediction
URL für Lesezeichen:	https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=73038
URN (NBN):	urn:nbn:de:hbz:061-20260428-111501-3
Kollektion:	Dissertationen
Sprache:	Englisch
Dokumententyp:	Wissenschaftliche Abschlussarbeiten » Dissertation
Medientyp:	Text
Autor:	Chapman, Hester Elizabeth [Autor]
Dateien:	[Dateien anzeigen] Adobe PDF [Details] 7,39 MB in einer Datei [ZIP-Datei erzeugen] Dateien vom 23.04.2026 / geändert 23.04.2026
Beitragende:	Prof. Dr. Ebenhöh, Oliver [Gutachter] Prof. Dr. rer. nat. Lercher, Martin [Gutachter]
Dewey Dezimal-Klassifikation:	500 Naturwissenschaften und Mathematik » 570 Biowissenschaften; Biologie
Beschreibung:	Staphylococcus aureus is the leading bacterial cause of death worldwide, and has the ability to quickly develop resistance to antimicrobials. Livestock-associated strains of this pathogen have become increasingly common over the last two decades, further contributing to the global burden of disease. Finding novel drug targets is key in the fight against this major human pathogen. Using an enzyme-constrained genome-scale model of S. aureus, it was found that the enzymes most highly controlling of growth are involved in glycolysis, respiration, and the pentose phosphate pathway. Using novel analysis techniques revealed that optimal growth is achieved through a combination of respiration and fermentation. This flexibility is a potential contributing factor to the ability to colonise and infect different hosts. Several essential genes were found in agreement with experimental datasets, providing a starting point for the development of novel antimicrobials. The model highlights strain-specific metabolic vulnerabilities and serves as a resource for future drug target discovery. In developing drugs to combat S. aureus, the ability to predict toxic effects in humans is crucial for reducing lead-time and minimising harm during clinical trials. A computational pipeline is introduced for predicting phase I and phase II drug metabolism in the liver. A pilot study is presented on the hepatotoxicity of paracetamol, to show the future uses of this tool. This framework demonstrates the potential of in silico approaches to anticipate toxic metabolites early in drug development, improving preclinical assessment and reducing reliance on animal models. This thesis demonstrates the versatility of systems biology, and its applicability across distinct but interconnected biological fields. The analytical methods developed here refine the interpretation of enzyme-constrained models, while applications to bacterial pathogenicity and drug-induced toxicity illustrate their possible impacts in antimicrobial research and predictive toxicology.
Lizenz:	Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz
Fachbereich / Einrichtung:	Mathematisch- Naturwissenschaftliche Fakultät » WE Biologie
Dokument erstellt am:	28.04.2026
Dateien geändert am:	28.04.2026
Promotionsantrag am:	16.09.2025
Datum der Promotion:	20.02.2026