There is a huge demand for analysis and visualization of the biological models. PathwayPioneer is a web-based tool to analyze and visually represent complex biological models. PathwayPioneer generates the initial layout of the model and allows users to customize it. It is developed using .net technologies (C#) and hosted on the Internet Information Service (IIS) server. At back-end it interacts with python-based COBRApy library for biological calculations like Flux Balance Analysis (FBA). We have developed a parallel processing architecture to accommodate processing of large models and enable message-based communication between the .net webserver and python engine. We compared the performance of our online system by loading a website with multiple concurrent dummy users and performed different time intensive operations in parallel. Given two metabolites of interest, millions of pathways can be found between them even in a small metabolic network. Depth First Search or Breadth First search algorithm retrieves all the possible pathways, thereby requiring huge computational time and resources. In Pathway Search using Informed Method, we have implemented, compared, and analyzed three different informed search techniques (Selected Subsystem, Selected Compartment, and Dynamic Search) and traditional exhaustive search technique. We found that the Dynamic approach performs exceedingly well with respect to time and total number of pathways searches. During our implementation we developed a SBML parser which outperforms the commercial libSBML parser in C#.

<p>Research of protein-protein interactions produce vast quantities of data and there exists a large number of databases with data from this research. Many of these databases offers the data for download on the web in a number of different formats, many of them XML-based.</p><p>With the arrival of these XML-based formats, and especially the standardized formats such as PSI-MI, SBML and BioPAX, there is a need for searching in data represented in XML. We wanted to investigate the capabilities of XML query tools when it comes to searching in this data. Due to the large datasets we concentrated on native XML database systems that in addition to search in XML data also offers storage and indexing specially suited for XML documents.</p><p>A number of queries were tested on data exported from the databases IntAct and Reactome using the XQuery language. There were both simple and advanced queries performed. The simpler queries consisted of queries such as listing information on a specified protein or counting the number of reactions.</p><p>One central issue with protein-protein interactions is to find pathways, i.e. series of interconnected chemical reactions between proteins. This problem involve graph searches and since we suspected that the complex queries it required would be slow we also developed a C++ program using a graph toolkit.</p><p>The simpler queries were performed relatively fast. Pathway searches in the native XML databases took long time even for short searches while the C++ program achieved much faster pathway searches.</p>

Research of protein-protein interactions produce vast quantities of data and there exists a large number of databases with data from this research. Many of these databases offers the data for download on the web in a number of different formats, many of them XML-based. With the arrival of these XML-based formats, and especially the standardized formats such as PSI-MI, SBML and BioPAX, there is a need for searching in data represented in XML. We wanted to investigate the capabilities of XML query tools when it comes to searching in this data. Due to the large datasets we concentrated on native XML database systems that in addition to search in XML data also offers storage and indexing specially suited for XML documents. A number of queries were tested on data exported from the databases IntAct and Reactome using the XQuery language. There were both simple and advanced queries performed. The simpler queries consisted of queries such as listing information on a specified protein or counting the number of reactions. One central issue with protein-protein interactions is to find pathways, i.e. series of interconnected chemical reactions between proteins. This problem involve graph searches and since we suspected that the complex queries it required would be slow we also developed a C++ program using a graph toolkit. The simpler queries were performed relatively fast. Pathway searches in the native XML databases took long time even for short searches while the C++ program achieved much faster pathway searches.

From its first use in the field of biochemistry, instrumental analysis offered a variety of invaluable tools for the comprehensive description of biological systems. Multi-selective methods that aim to cover as many endogenous compounds as possible in biological samples use different analytical platforms and include methods like gene expression profile and metabolite profile analysis. The enormous amount of data generated in application of profiling methods needs to be evaluated in a manner appropriate to the question under investigation. The new field of system biology rises to the challenge to develop strategies for collecting, processing, interpreting, and archiving this vast amount of data; to make those data available in form of databases, tools, models, and networks to the scientific community.<br><br> On the background of this development a multi-selective method for the determination of phytohormones was developed and optimised, complementing the profile analyses which are already in use (Chapter I). The general feasibility of a simultaneous analysis of plant metabolites and phytohormones in one sample set-up was tested by studies on the analytical robustness of the metabolite profiling protocol. The recovery of plant metabolites proved to be satisfactory robust against variations in the extraction protocol by using common extraction procedures for phytohormones; a joint extraction of metabolites and hormones from plant tissue seems practicable (Chapter II).<br><br> Quantification of compounds within the context of profiling methods requires particular scrutiny (Chapter II). In Chapter III, the potential of stable-isotope in vivo labelling as normalisation strategy for profiling data acquired with mass spectrometry is discussed. First promising results were obtained for a reproducible quantification by stable-isotope in vivo labelling, which was applied in metabolomic studies.<br><br> In-parallel application of metabolite and phytohormone analysis to seedlings of the model plant Arabidopsis thaliana exposed to sulfate limitation was used to investigate the relationship between the endogenous concentration of signal elements and the ‘metabolic phenotype’ of a plant. An automated evaluation strategy was developed to process data of compounds with diverse physiological nature, such as signal elements, genes and metabolites – all which act in vivo in a conditional, time-resolved manner (Chapter IV). Final data analysis focussed on conditionality of signal-metabolome interactions. / Die instrumentelle Analytik stellt mit ihrem unschätzbaren Methodenreichtum Analysenwerkzeuge zur Verfügung, die seit ihrem Einzug in die Biologie die Aufzeichnung immer komplexerer ‚Momentaufnahmen’ von biologischen Systemen ermöglichen. Konkret hervorzuheben sind dabei vor allem die sogenannten ‚Profilmethoden’. Die Anwendung von Profilmethoden zielt darauf ab, aus einer bestimmten Stoffklasse so viele zugehörige Komponenten wie nur möglich gleichzeitig zu erfassen. <br><br> Für die Auswertung derart komplexer Daten müssen nun auch entsprechende Auswertungsmethoden bereit gestellt werden. Das neu entstandene Fachgebiet der Systembiologie erarbeitet deshalb Strategien zum Sammeln, Auswerten und Archivieren komplexer Daten, um dieses gesammelte Wissen in Form von Datenbanken, Modellen und Netzwerken der allgemeinen Nutzung zugänglich zu machen.<br><br> Vor diesem Hintergrund wurde den vorhandenen Profilanalysen eine Methode zur Erfassung von Pflanzenhormonen hinzugefügt. Verschiedene Experimente bestätigten die Möglichkeit zur Kopplung von Pflanzenhormon- und Pflanzeninhaltsstoff(=metabolit)-Profilanalyse. In weiteren Untersuchungen wurde das Potential einer innovativen Standardisierungstechnologie für die mengenmässige Erfassung von Pflanzeninhaltsstoffen in biologischen Proben betrachtet (in vivo labelling mit stabilen Isotopen).<br><br> Hormon- und Metabolitprofilanalyse wurden dann parallel angewandt, um Wechselwirkungen zwischen der Konzentration von Signalkomponenten und der Ausprägung des Stoffwechsels in Keimlingen der Modellpflanze Arabidopsis thaliana zu untersuchen. Es wurde eine Prozessierungsmethode entwickelt, die es auf einfache Art und Weise erlaubt, Daten oder Komponenten verschiedenen Ursprungs wie Signalelemente, Gene und Metabolite, die in biologischen Systemen zeitlich versetzt aktiv oder verändert erscheinen, im Zusammenhang zu betrachten. Die abschließende Analyse aller Daten richtet sich auf die Abschätzung der Bedingtheit von Signal-Metabolismus Interaktionen.