Bioinformatische Werkzeuge für die Visualisierung und strukturelle Analyse Metabolischer Netzwerke

Living organisms are hierarchical structures that integrate their smallest constituent parts – individual molecules including DNA, proteins and metabolites – across multiple levels of organization, from organelles to cells, tissues, organs and the organism. Thus, a major challenge in biology and medicine today is to understand how the large number of different molecular parts interact and self-organize into a whole system which exhibits organic properties that cannot be explained solely in terms of their component properties. Systems biology is an academic field that seeks to integrate different levels of information to quantitatively understand how biological systems function. Bioinformatics can help this task by developing tools to analyze and visualize these systems. In the present work, several bioinformatics tools were developed to help the visualization and analysis of biologic networks from a systems biology point of view. Methods for the decomposition of metabolic networks were also proposed, including decomposition of a network into a core-periphery structure and its characterization. The tools developed include: software to decompose networks into smaller, functionally related, parts (modules); programs to help visualizing large amounts of data generated by network reconstruction based on genomic and functional genomic data. Software to help the integration of existing tools such as translators for the different file formats used was also developed. Basic analyses of metabolic networks were also provided as tests for the efficiency of the tools developed and to show how the use of these tools can help network analysis.Lebende Organismen sind hierarchische Strukturen, die ihre kleinsten Teile - individuelle Moleküle wie DNA, Proteine und Metaboliten - durch multiple Organisationsebenen, von Organellen bis Zellen, Geweben, Organen und dem Organismus, verbinden. Zu den großen Herausforderungen der Biologie und Medizin gehört deshalb heute, zu verstehen, wie die große Zahl unterschiedlicher molekularer Teile zusammenwirken und sich zu einem komplexen System organisieren, das organische Eigenschaften zeigt, die sich nicht nur aus seinen einzelnen Komponenten erklären lassen. Systembiologie ist ein akademischer Bereich, der versucht, verschiedene Informationsebenen zu integrieren, um quantitativ zu verstehen wie biologische Systeme funktionieren. Die Bioinformatik kann darin unterstützend wirken, denn sie entwickelt Werkzeuge zur Analyse und Visualisierung dieser Systeme. In der vorliegenden Arbeit wurden mehrere bioinformatische Werkzeuge entwickelt, mit denen biologische Netzwerke aus Sicht der Systembiologie visualisiert und analysiert werden können. Es werden darüber hinaus Mehtoden für die Zerlegung metabolischer Netzwerke vorgestellt, einschließlich einer Methode für die Zerlegung und Charakterisierung einer Kernperipheriestruktur. Zu den entwickelten Werkzeugen gehören: Werkzeuge, mit denen Netzwerke in kleinere, funktionsbezogene Teile (Module) zerlegt werden können; Werkzeuge, mit denen große Datenmengen aus Netzwerkrekonstruktionen sichtbar gemacht werden können, die auf genomischen und funktionell genomischen Daten basieren. Darüber hinaus wurde eine Software entwickelt, die dabei hilft, bereits existierende Werkzeuge wie Übersetzer für die verschiedenen Dateiformate zu integrieren. Als Test für die Anwendbarkeit der entwickelten Werkzeuge und als Demonstration wie ihre Nutzung die Netzwerkanalyse unterstützen kann, wurden Basisanalysen metabolischer Netzwerke erstell

Structure and dynamics of core-periphery networks

Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways ensuring cooperation and providing multiple options of network flow re-channelling greatly contribute to the high robustness of complex systems. Core processes enable a coordinated response to various stimuli, decrease noise, and evolve slowly. The integrative function of network cores is an important step in the development of a large variety of complex organisms and organizations. In addition to these important features and several decades of research interest, studies on core/periphery networks still have a number of unexplored areas.Comment: a comprehensive review of 41 pages, 2 figures, 1 table and 182 reference

Efficient enumeration of small graphlets and orbits

As the world is flooded with data, the demand for mining data for useful purposes is increasing. An effective techniques is to model the data as networks (graphs) and then apply graph mining techniques for analysis. As on date, the algorithms available to count graphlets and orbits for various types of graphs and their generalizations are limited. The thesis aims to fill the gap by presenting a simple and efficient algorithm for 3-node graphlet and orbit counting that is generic enough to work for both undirected and directed graphs. Our algorithm is compared with the state-of-art algorithms and we show that in most cases our algorithm performs better. We demonstrate our algorithm in three case studies related to (i) enzyme and metabolite correlation network in corn, (ii) watershed governance networks, and (iii) patterns exhibited by co-expression networks of healthy and cancerous stomach cells