From environmental contaminants to cellular response

Environmental contaminants are substances that, when distributed into the environment, may potentially be harmful to people, wildlife and plants. Polycyclic aromatic hydrocarbons (PAHs) such as dioxins (e. g. TCDD) and benzo[a]pyrene (BaP) represent an important class of environmental contaminants exerting a wide range of toxic effects including carcinogenesis, immunosuppression and a pro-inflammatory response. BaP is often used as a model contaminant to study the impact of PAHs on the cellular level. This work studies the distribution and interaction of this model contaminant in living cells in order to get a better understanding of the cellular response to environmental contaminants. In order to fully understand the consequences of contamination on the cellular level, appropriate models describing the main processes are necessary. After identification of the relevant processes and their parametrization they have to be incorporated into the corresponding models. Parameter estimation in cellular systems can experimentally be addressed using non-invasive methods based on fluorescence of the contaminants or other involved molecules in question such as Fluorescence Recovery After Photobleaching (FRAP) which was used for this study. Several attempts have been made to analyze such experiments by different reaction diffusion models. In this work, it has been started from the most general reaction diffusion model that includes an unconfined number of reacting and diffusing compounds. The analytical solution is presented and applied to real datasets. The relevant parameters estimated by methods like FRAP have to be inverted from the preprocessed measurements using fitting algorithms. In that study the global optimization method of Simulated Annealing was applied. Subsequently, the parameterized processes should run within a realistic environment, i. e. the cellular geometries. Within this work geometries of the cytoplasm and the nucleus of different cell lines have been extracted from confocal microscopy images using the Neuron Reconstruction algorithm NeuRA2. Finally, the geometries and the parameterized processes have to be combined to generate an appropriate simulation environment. Such a simulation tool not only enables the testing of influences on hypothetical FRAP experiments but also yields a deeper insight in cellular processes, i. e. identification of major factors controlling cellular response to contamination.