TY - GEN A1 - Köhl, Karin I. A1 - Basler, Georg A1 - Lüdemann, Alexander A1 - Selbig, Joachim A1 - Walther, Dirk T1 - A plant resource and experiment management system based on the Golm Plant Database as a basic tool for omics research T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Background: For omics experiments, detailed characterisation of experimental material with respect to its genetic features, its cultivation history and its treatment history is a requirement for analyses by bioinformatics tools and for publication needs. Furthermore, meta-analysis of several experiments in systems biology based approaches make it necessary to store this information in a standardised manner, preferentially in relational databases. In the Golm Plant Database System, we devised a data management system based on a classical Laboratory Information Management System combined with web-based user interfaces for data entry and retrieval to collect this information in an academic environment. Results: The database system contains modules representing the genetic features of the germplasm, the experimental conditions and the sampling details. In the germplasm module, genetically identical lines of biological material are generated by defined workflows, starting with the import workflow, followed by further workflows like genetic modification (transformation), vegetative or sexual reproduction. The latter workflows link lines and thus create pedigrees. For experiments, plant objects are generated from plant lines and united in so-called cultures, to which the cultivation conditions are linked. Materials and methods for each cultivation step are stored in a separate ACCESS database of the plant cultivation unit. For all cultures and thus every plant object, each cultivation site and the culture's arrival time at a site are logged by a barcode-scanner based system. Thus, for each plant object, all site-related parameters, e. g. automatically logged climate data, are available. These life history data and genetic information for the plant objects are linked to analytical results by the sampling module, which links sample components to plant object identifiers. This workflow uses controlled vocabulary for organs and treatments. Unique names generated by the system and barcode labels facilitate identification and management of the material. Web pages are provided as user interfaces to facilitate maintaining the system in an environment with many desktop computers and a rapidly changing user community. Web based search tools are the basis for joint use of the material by all researchers of the institute. Conclusion: The Golm Plant Database system, which is based on a relational database, collects the genetic and environmental information on plant material during its production or experimental use at the Max-Planck-Institute of Molecular Plant Physiology. It thus provides information according to the MIAME standard for the component 'Sample' in a highly standardised format. The Plant Database system thus facilitates collaborative work and allows efficient queries in data analysis for systems biology research. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 830 KW - microarray data KW - arabidopsis KW - information Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427595 IS - 830 ER - TY - THES A1 - Basler, Georg T1 - Mass-balanced randomization : a significance measure for metabolic networks T1 - Massebalancierte Randomisierung : ein Maß für Signifikanz in metabolischen Netzwerken N2 - Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, including small average path length, large clustering coefficient, heavy-tail degree distribution, and hierarchical organization, viewed as requirements for efficient and robust system architectures. Existing studies estimate the significance of network properties using a generic randomization scheme - a Markov-chain switching algorithm - which generates unrealistic reactions in metabolic networks, as it does not account for the physical principles underlying metabolism. Therefore, it is unclear whether the properties identified with this generic approach are related to the functions of metabolic networks. Within this doctoral thesis, I have developed an algorithm for mass-balanced randomization of metabolic networks, which runs in polynomial time and samples networks almost uniformly at random. The properties of biological systems result from two fundamental origins: ubiquitous physical principles and a complex history of evolutionary pressure. The latter determines the cellular functions and abilities required for an organism’s survival. Consequently, the functionally important properties of biological systems result from evolutionary pressure. By employing randomization under physical constraints, the salient structural properties, i.e., the smallworld property, degree distributions, and biosynthetic capabilities of six metabolic networks from all kingdoms of life are shown to be independent of physical constraints, and thus likely to be related to evolution and functional organization of metabolism. This stands in stark contrast to the results obtained from the commonly applied switching algorithm. In addition, a novel network property is devised to quantify the importance of reactions by simulating the impact of their knockout. The relevance of the identified reactions is verified by the findings of existing experimental studies demonstrating the severity of the respective knockouts. The results suggest that the novel property may be used to determine the reactions important for viability of organisms. Next, the algorithm is employed to analyze the dependence between mass balance and thermodynamic properties of Escherichia coli metabolism. The thermodynamic landscape in the vicinity of the metabolic network reveals two regimes of randomized networks: those with thermodynamically favorable reactions, similar to the original network, and those with less favorable reactions. The results suggest that there is an intrinsic dependency between thermodynamic favorability and evolutionary optimization. The method is further extended to optimizing metabolic pathways by introducing novel chemically feasibly reactions. The results suggest that, in three organisms of biotechnological importance, introduction of the identified reactions may allow for optimizing their growth. The approach is general and allows identifying chemical reactions which modulate the performance with respect to any given objective function, such as the production of valuable compounds or the targeted suppression of pathway activity. These theoretical developments can find applications in metabolic engineering or disease treatment. The developed randomization method proposes a novel approach to measuring the significance of biological network properties, and establishes a connection between large-scale approaches and biological function. The results may provide important insights into the functional principles of metabolic networks, and open up new possibilities for their engineering. N2 - In der Systembiologie und Bioinformatik wurden in den letzten Jahren immer komplexere Netzwerke zur Beschreibung verschiedener biologischer Prozesse, wie Genregulation, Protein-Interaktionen und Stoffwechsel (Metabolismus) rekonstruiert. Ein Hauptziel der Forschung besteht darin, die strukturellen Eigenschaften von Netzwerken für Vorhersagen über deren Funktion nutzbar zu machen, also eine Verbindung zwischen Netzwerkeigenschaften und Funktion herzustellen. Die netzwerkbasierte Forschung zielte bisher vor allem darauf ab, gemeinsame Eigenschaften von Netzwerken unterschiedlichen Ursprungs zu entdecken. Dazu zählen die durchschnittliche Länge von Verbindungen im Netzwerk, die Häufigkeit redundanter Verbindungen, oder die hierarchische Organisation der Netzwerke, welche als Voraussetzungen für effiziente Kommunikationswege und Robustheit angesehen werden. Dabei muss zunächst bestimmt werden, welche Eigenschaften für die Funktion eines Netzwerks von besonderer Bedeutung (Signifikanz) sind. Die bisherigen Studien verwenden dafür eine Methode zur Erzeugung von Zufallsnetzwerken, welche bei der Anwendung auf Stoffwechselnetzwerke unrealistische chemische Reaktionen erzeugt, da sie physikalische Prinzipien missachtet. Es ist daher fraglich, ob die Eigenschaften von Stoffwechselnetzwerken, welche mit dieser generischen Methode identifiziert werden, von Bedeutung für dessen biologische Funktion sind, und somit für aussagekräftige Vorhersagen in der Biologie verwendet werden können. In meiner Dissertation habe ich eine Methode zur Erzeugung von Zufallsnetzwerken entwickelt, welche physikalische Grundprinzipien berücksichtigt, und somit eine realistische Bewertung der Signifikanz von Netzwerkeigenschaften ermöglicht. Die Ergebnisse zeigen anhand der Stoffwechselnetzwerke von sechs Organismen, dass viele der meistuntersuchten Netzwerkeigenschaften, wie das Kleine-Welt-Phänomen und die Vorhersage der Biosynthese von Stoffwechselprodukten, von herausragender Bedeutung für deren biologische Funktion sind, und somit für Vorhersagen und Modellierung verwendet werden können. Die Methode ermöglicht die Identifikation von chemischen Reaktionen, welche wahrscheinlich von lebenswichtiger Bedeutung für den Organismus sind. Weiterhin erlaubt die Methode die Vorhersage von bisher unbekannten, aber physikalisch möglichen Reaktionen, welche spezifische Zellfunktionen, wie erhöhtes Wachstum in Mikroorganismen, ermöglichen könnten. Die Methode bietet einen neuartigen Ansatz zur Bestimmung der funktional relevanten Eigenschaften biologischer Netzwerke, und eröffnet neue Möglichkeiten für deren Manipulation. KW - Bioinformatik KW - Metabolische Netzwerke KW - Signifikanz KW - Randomisierung KW - Nullmodell KW - computational biology KW - metabolic networks KW - significance KW - randomization KW - null model Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-62037 ER -