E-Dissertationen

Endocrine-disrupting nonylphenols

Wed, 29 Jul 2026 00:00:00 GMT

Endocrine-disrupting nonylphenols Alrashed, Nasser Saad Nonylphenols (NPs) are degradation products of 4-nonylphenol ethoxylates (NPEs). NPEs are non-ionic surfactants that have been used for decades in cleaning agents and industrial processes due to their surfactant activity. As a result, NPs are ubiquitously detectable in many environmentally relevant matrices and biological samples. Theoretically, 550 different isomers can be derived. A possible grouping based on chain length and a numbering system based on this was proposed by Guenther et al. (2006).
Nonylphenols are persistent, toxic, and endocrine disruptors. The structural features of various alkylphenols are known to influence estrogenic activity. Therefore, the use of NPEs and NPs was regulated throughout Europe in 2005 by Directive 2003/53/EC. Since then, the sale and use of products/formulations containing more than 0.1% NPE or NP have been prohibited.
Nonylphenols can be extracted from food using steam distillation with integrated liquid-liquid extraction. Adding an isotopically labeled internal standard (¹³C₆-NP₁₁₂) before extraction allows the quantification of nonylphenols present in food as a sum parameter. After analyzing selected foods from different countries by high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS), NPs were detected in 30 of the 35 food samples. The concentration of NPs, based on fresh weight, ranged from 0.1 to 18.9 µg kg⁻¹.
The highest NP contents were found in coffee samples, and therefore an isomer-specific analysis was carried out by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOF). Fourteen pure isomers of NP were used as standards for cross-referencing the NP isomers. We identified the two main NP isomers in the coffee samples as 4-[3-ethyl-1,1-dimethylpentyl]-phenol and 4-[1-ethyl-1,3-dimethylpentyl]-phenol or NP₁₂₈ and NP₁₁₁ (Juelich nomenclature), respectively. The GCxGC-TOF system with a liquid nitrogen cryogenic modulator used in this study is suitable for the isomer-specific analysis of NP and can be applied and further optimized for future routine analysis in food control.

Identification of a Hsp40 Involved in the Cytosolic Quality Control of Mitochondrial Precursor Proteins

Tue, 28 Jul 2026 00:00:00 GMT

Identification of a Hsp40 Involved in the Cytosolic Quality Control of Mitochondrial Precursor Proteins Borgert, Lion (noch nicht zugänglich / not yet accessible)

Development of a Parameter Estimation Pipeline for Multi-Cellular Biological Processes

Tue, 28 Jul 2026 00:00:00 GMT

Development of a Parameter Estimation Pipeline for Multi-Cellular Biological Processes Alamoodi, Emad Mohammed Tissue dynamics are complex and involve interactions between different cell types and extracellular components on different spatial and temporal scales. Specific tissue properties are relevant for a broad range of processes, including tissue homeostasis, viral infection, and tumor development and treatment. The multi-scale and multi-cellular model has been proven to be a valuable tool to study these dynamics. However, these models remain challenging to use in practice. The lack of computational tools and widely adopted data standards that facilitate its simulation and calibration hinders its wider adaptation.
The goal of this cumulative thesis is to advance the computational foundation needed for multi-scale and multi-cellular models to become more accessible, scalable, and reproducible, with a special focus on the parameter estimation aspect.
First, we develop the FitMultiCell pipeline for simulating and parameterizing multi-scale and multi-cellular biological systems. The pipeline has been tested in different real biological applications to validate its robustness and scalability. It provides a user interface for easier adaptation with an extensive set of documentation.
Second, we improve the wall-time of the approximate Bayesian computation—the sequential Monte Carlo (ABC-SMC) inference method in the pipeline by introducing a new scheduling strategy where we are able to achieve better wall-time by introducing a higher resource utilization. We evaluate the new strategy across multiple realistic application scenarios and show consistent performance improvements. By shortening runtimes and improving scalability, the approach enables the calibration of more complex models within practical time limits.
Third, we introduce a standardized format, PEtab-MS, to encapsulate the parameter estimation problem of a multi-scale and multi-cellular nature. PEtab-MS provides a structured, reusable, and machine-readable way to define the model, experimental data, conditions, parameters, and objective function in a reusable and machine-readable form. It enables easier sharing and comparison of results across different studies. This will improve collaboration and advance the research of these models.
Fourth, we apply these advances to investigate the cell migration process in a pillar-forest microenvironment by using a cellular Potts model (CPM) calibrated on experimental data. The results not only show the impact of spatial constraints on impacting the migration dynamics but also highlight the potential of data-driven modeling pipelines to derive mechanistic insights from complex systems.
In conclusion, the advances presented in this thesis enable wider adaptation through the introduction of a general-purpose pipeline, a standardized calibration format, and a faster inference strategy. The practical value of these contributions is demonstrated across multiple biological case studies, both individually and as an integrated end-to-end workflow.

Non-reversible Lifts of Reversible Diffusions and their Convergence to Equilibrium

Tue, 28 Jul 2026 00:00:00 GMT

Non-reversible Lifts of Reversible Diffusions and their Convergence to Equilibrium Lörler, Francis Cameron This thesis is concerned with convergence to stationarity of non-reversible Markov processes with degenerate noise, known as hypocoercivity. We develop a framework that allows to derive rates of convergence towards the invariant measure in L² for a large class of such dynamics, which we term second-order lifts of reversible diffusions. These are motivated by the concept of lifts of Markov chains that was introduced to understand acceleration due to non-reversibility in Markov chain Monte Carlo methods. The six works forming the basis for this thesis are included in the appendix as Chapters A–F.
The development of this framework is the main content of Chapters A, B and C. The concept of second-order lifts of reversible diffusions is motivated and introduced in Chapter A, providing a structural relationship between many non-reversible dynamics with degenerate noise and simple, reversible diffusions that can be exploited to derive a direct lower bound on the L²-relaxation time. We give a first demonstration of how it can also be used to obtain upper bounds on the relaxation time. In Chapter B, we extend this framework to processes on Riemannian manifolds with boundary, and prove the quantitative divergence lemma. The latter is a statement purely on the generator of the underlying reversible diffusion, and is a crucial ingredient in the proof of upper bounds on the relaxation time using the approach of second-order lifts. We conclude in Chapter C by turning these ideas into a general framework. It allows to derive quantitative bounds on rates of convergence to stationarity for second-order lifts by proving a flow Poincaré inequality, a time-averaged Poincaré inequality along trajectories of the associated transition semigroup, under assumptions that are simple to verify in practice. Chapter D compares our approach to hypocoercivity with the highly influential one developed by Dolbeault, Mouhot and Schmeiser, uncovering several structural similarities.
The works underlying Chapters E and F focus on two classes of self-interacting processes, namely self-repellent random walks and self-repelling diffusions. We show that these are second-order lifts of a suitable Ornstein-Uhlenbeck process whose invariant probability measure corresponds to that of the environment process. These processes are closely related to Event Chain Monte Carlo methods and models for polymer growth. For both classes, we show that convergence rates to stationarity can be obtained using the framework of second-order lifts and the results of Chapter C.
We begin with some background on convergence to equilibrium of Markov processes and examples motivating acceleration through non-reversibility in Chapter 1. The main result, the hypocoercivity framework based on second-order lifts and the flow Poincaré inequality, is presented in Chapter 2, focussing on the convergence of Langevin dynamics to highlight the main ideas. Finally, Chapter 3 presents the contributions of the individual projects and some open questions.