Universität zu Köln

Seeger, Leon ORCID: 0000-0002-2824-1231 (2026). Physiology, Kinetics, and Fitness: Mechanistic Models of Microbial Adaptation. PhD thesis, Universität zu Köln.

PDF Dissertation_LSeeger_Veroeffentlichung.pdf - Accepted Version Bereitstellung unter der CC-Lizenz: Creative Commons Attribution Share Alike. Download (18MB)

Abstract

Microbes adapt to environmental challenges through physiological plasticity on short timescales and through evolutionary adaptation on longer timescales. Preventing antibiotic resistance, designing effective combination therapies, and preparing for climate change require predicting the speed and limits of microbial adaptation through both mechanisms. However, current models of the interplay between ecology, physiology, and evolution lack mechanistic integration and predictive power. In this thesis, I develop a metabolic fitness model to predict microbial adaptation to environmental challenges. Based on a mechanistic description of microbial metabolism, the model predicts growth-optimal resource allocation and enzyme evolution under nutrient limitation, physiological adaptation to drug challenges, and constraints on the evolution of drug resistance. To test my predictions, I set up a bioreactor and validate the model using integrative multi-omics analyses. Model and proteomic data show that growth-optimal resource reallocation in response to nutrient limitation and antibiotic challenge are similar: cells must minimize metabolite loss downstream of a constrained reaction. I use the model to predict the fitness of enzyme mutants and show that the physiological distribution of enzyme kinetic parameters matches a mutation-selection-drift equilibrium. Lastly, I investigate how resource reallocation shapes the dose-response to antibiotic challenge and predict mutant fitness as a function of drug dose. This framework provides a quantitative understanding of microbial adaptation through regulation and evolution, offering guidance for the development of effective and sustainable antibiotic strategies.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Seeger, Leon leon.seeger@uni-koeln.de https://orcid.org/0000-0002-2824-1231 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-797201
Date:	2026
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Physics > Institut für Biologische Physik
Subjects:	Physics Life sciences
Uncontrolled Keywords:	Keywords Language Systems Biology, Physics, Physiology, Kinetics, Microbiology, Evolution, Metabolism, Proteomics, Metabolomics, Adaptation, Modeling, Prediction, Antibiotic, Resistance UNSPECIFIED
Date of oral exam:	15 January 2026
Referee:	Name Academic Title Lässig, Michael Prof. Krug, Joachim Prof. Regös, Roland Prof.
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/79720