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Integration of ligand characteristics for the simulation of cellular reactions


Bauer,  Raphael André
Max Planck Society;

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Bauer, R. A. (2009). Integration of ligand characteristics for the simulation of cellular reactions. PhD Thesis, Freie Universität Berlin, Berlin.

A major characteristic of life sciences is the generation of vast amounts of raw data produced by modern wet lab technologies. The data may come from large-scale experiments where chemical compounds are tested on their ability to act as possible agents against certain diseases. It can also originate in the determination of the 3D structure of macromolecules, or from the genetic code of a species. Evaluation and integration of that raw data is becoming increasingly important. Currently, often only a fraction of the generated data is integrated and evaluated. The data integration problem is addressed in the first part of the work. A data-warehouse is developed that integrates 3D information on proteins with information about potential drugs, potential binding sites and advanced 3D binding site screening techniques. Furthermore, as similarity screening of molecules and proteins can often only be carried out with limited accuracy on a limited amount of data sets. A framework is presented that facilitates the integration of data sources and methods with an emphasis on exact 3D screening techniques. The amount of searchable macromolecular structures, such as proteins and RNAs is growing rapidly. However, there are only a few methods available allowing for a rapid 3D screening of thousands of proteins, and only a handful of methods can be used for aligning RNA structures. A novel method is presented that uses n-grams and index structures in concert with a nomenclature that reduces a biomolecule to a string. It can be shown that the method delivers comparable or better results in comparison to leading methods in the field of protein and RNA alignment. The method can be used in high throughput experiments because of its precision and adjustable speed. The last part of the work deals with interaction and signal transduction. Expression levels correlate to the amount of signals that are transduced in a biological network. Various concepts are evaluated that map expression levels of genes on the apoptosis signal transduction network using Petri nets. Finally, a software package is presented that is able to simulate Petri nets based on the developed paradigm. The software can hide the complexity of the Petri net, which allows non-computer experts to use the software efficiently.