Modeling the spatio-temporal organization and segregation of bacterial chromosomes
This work examined the spatio-temporal organization and segregation of bacterial DNA in order to investigate the fundamental processes regulating the inheritance of genetic material and the proliferation of life. For the investigation of the spatio-temporal organization of genetic material in the ce...
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|This work examined the spatio-temporal organization and segregation of bacterial DNA in order to investigate the fundamental processes regulating the inheritance of genetic material and the proliferation of life. For the investigation of the spatio-temporal organization of genetic material in the cell fundamental physical principles were used in this work. The aim was to use concepts of polymer physics to formulate physical models of the complex biological reality. These models were evaluated in computer simulations and compared with experimental data.
In the first project of this thesis, the spatial organization of DNA in multipartite bacteria (= bacteria with multiple replicons) was investigated. The results of this work reveal high order of spatial organization even for multipartite bacteria. The organization could be reproduced using a physical model of compacted DNA and geometric constraints on individual genes. Furthermore, it was possible to make accurate predictions for different mutants and to predict interactions between replicons with the developed model.
The second project focused on the study of simultaneous replication and segregation of bacterial DNA. Segregation patterns of the ori were analyzed in the model organism Bacillus subtilis. Using Molecular Dynamics simulations, it was shown that entropic segregation of chromosomes is a plausible mechanism for the segregation of genetic material that would also explain the observed variability in the experimental data.
The model of entropic segregation of bacterial chromosomes was extended in the third project by the implementation of additional segregation mechanisms, so that a large data set of different trajectories of the ori through the cell could be generated. Thus, machine learning models could be used to classify the different segregation movements. The evaluation of the predictions showed very good results and encourages future classification of experimental data based on the developed models.
This work is intended to provide new perspectives on the organization of DNA in the bacterial cell as well as a better understanding of the physical basis of cellular processes.