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  Integration of ligand characteristics for the simulation of cellular reactions

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

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 Creators:
Bauer, Raphael André1, Author
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1Max Planck Society, ou_persistent13              

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Free keywords: Drug similarity; substructure search; structural alignment; protein; RNA; hash-table; n-grams; dihedral angles; Petri net; microarray; apoptosis
 Abstract: 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.

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Language(s): eng - English
 Dates: 2009-12-10
 Publication Status: Accepted / In Press
 Pages: 143
 Publishing info: Berlin : Freie Universität Berlin
 Table of Contents: Abstract 5
Acknowledgements 9
1 Introduction 13
1.1 A greater picture of systems biology . . . 13
1.1.1 A declaration and a virtual human . . . . 13
1.1.2 Double helix { single sequence . . . . . 13
1.1.3 From sequence to regulation . . . . . . . 15
1.1.4 Regulation and interaction in the cellular apparatus 15
1.1.5 Personalized Medicine . . . . . . . . . . 17
1.2 Loose ends . . . . . . .. . . . . . . . . . 18
1.2.1 An integration gap: elements, processes and methods 18
1.2.2 Biomolecules, string encoding and applications . . 19
1.2.3 Cell signaling . . . . . . . . . . .... . . . . . . 21
1.3 Objectives and overview . . . . . . . . . . . . . . . 23
2 Integration of ligand characteristics ..................25
2.1 Data warehouse of metabolite and drug binding sites: SuperSite 25
2.1.1 Introduction . . . . . ........ . . . . . . . . . . 25
2.1.2 Database and tools . . . . . . . . . . . .. . . . . 27
2.1.3 Case studies . . . . . . . . . . .. . . . . . . . . 29
2.1.4 Conclusions . . . . . . . . . . . . . ..... . . . . 31
2.2 A metaserver for structural search: Superimpos e . . 33
2.2.1 Introduction . . . . . . . . . . . . . . . . . . .. 33
2.2.2 Algorithms . . . . . . . . . . . . . . . . . . . .. 35
2.2.3 Databases . . . . . . . . . . . . . . . . . . . . . 39
2.2.4 Web server description . . . . . . . . . . . . . . 41
2.2.5 Case studies . . . . . . . . . . . . . . . . . . . 42
2.2.6 Conclusions . . . . . . . . . . . . . . . . . . . . 43
12 Contents
3 Biomolecular search, similarity and coding properties ..47
3.1 RNA character encoding and su x techniques . . .. . . 47
3.1.1 Introduction . . . . . . . . . . . . . . . . . . . 47
3.1.2 Methods . . . . . . . . . . . . . . . . . . . . . . 50
3.1.3 Results . . . . . . . . . . . . . . . . . . . . . . 54
3.1.4 Discussion . . . . . . . . . . . . . . . . . . . . 62
3.1.5 Conclusions . . . . . . . . . . . . . . . . . . . . 63
3.2 Macromolecular similarity screening . . . . . . . . . 64
3.2.1 Introduction . . . . . . . . . . .. . . . . . . . . 64
3.2.2 Material and methods . . . . . . . . .... . . . . . 68
3.2.3 Results . . . . . . . . . . . . . . . . . . . . . . 73
3.2.4 Discussion . . . . . . . . . . . . . .. . . . . . . 78
3.2.5 Conclusions . . . . . . . . . . . . . . . . . . . . 81
3.3 A novel approach for the detection of structural features of RNA ..........................................83
3.3.1 Introduction . . . . . . . . . . ...... . . . . . . 83
3.3.2 Material and methods . . . . . . . . . . . .. . . . 85
3.3.3 Results . . . . . . . . . . . . . . . . . . . . . . 86
3.3.4 Discussion . . . . . . . . . . . . . . . . . . . . 90
3.3.5 Conclusions . . . . . . . . . . . . . . . . . . . . 92
4 Simulation of cellular reactions .......................93
4.1 Quantitative simulation of apoptosis using Petri nets 93
4.1.1 Introduction . . . . . . . . . . . . . . . . . . . .93
4.1.2 Petri nets in systems biology . . . . . . . . . . . 94
4.1.3 Material and methods . . . . . . . . . . . .. . . . 96
4.1.4 Results . . . . . . . . . . . . . . . . . . . . . . 99
4.1.5 Discussion and future directions . . . . . . . . . 101
4.1.6 Conclusions . . . . . . . . . . . . . . . . . . . 104
4.2 Cell Sim {a Petri net based cell simulation software 105
4.2.1 Introduction . . . . . . . . . . . . . . . . . . . 105
4.2.2 Material and methods . . . . . . . . . . . . . . . 105
4.2.3 Results . . . . . . . . . . . . . . . . . . . . . 106
4.2.4 Discussion and conclusions . . . . . . . . . . . . 106
5 Discussion 111
6 Appendix 115
Ehrenwortliche Erklarung . . . . . . . . . . . . . . . 115
Zusammenfassung . . . . . . . . . . . . . . . . . . . . 116
Curriculum Vitae . . . . . . . . . . . . . . . . . . . . 117
List of gures . . . . . . . . . . . . . . . . . . . . . 118
List of tables . . . . . . . . . . . . . . . . . . . . . 122
Bibliography . . . . . . . . . . . . . . . . . . . . . . 123
 Rev. Type: -
 Identifiers: eDoc: 446314
 Degree: PhD

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