A generic algorithm for layout of biological networks

BackgroundBiological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration.ResultsWe present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks.ConclusionThe presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.publishe

Uncovering metabolic pathways relevant to phenotypic traits of microbial genomes

A new machine learning-based method is presented here for the identification of metabolic pathways related to specific phenotypes in multiple microbial genomes

Rhea—a manually curated resource of biochemical reactions

Rhea (http://www.ebi.ac.uk/rhea) is a comprehensive resource of expert-curated biochemical reactions. Rhea provides a non-redundant set of chemical transformations for use in a broad spectrum of applications, including metabolic network reconstruction and pathway inference. Rhea includes enzyme-catalyzed reactions (covering the IUBMB Enzyme Nomenclature list), transport reactions and spontaneously occurring reactions. Rhea reactions are described using chemical species from the Chemical Entities of Biological Interest ontology (ChEBI) and are stoichiometrically balanced for mass and charge. They are extensively manually curated with links to source literature and other public resources on metabolism including enzyme and pathway databases. This cross-referencing facilitates the mapping and reconciliation of common reactions and compounds between distinct resources, which is a common first step in the reconstruction of genome scale metabolic networks and models

Visualization of Metabolic Networks

The metabolism constitutes the universe of biochemical reactions taking place in a cell of an organism. These processes include the synthesis, transformation, and degradation of molecules for an organism to grow, to reproduce and to interact with its environment. A good way to capture the complexity of these processes is the representation as metabolic network, in which sets of molecules are transformed into products by a chemical reaction, and the products are being processed further. The underlying graph model allows a structural analysis of this network using established graphtheoretical algorithms on the one hand, and a visual representation by applying layout algorithms combined with information visualization techniques on the other. In this thesis we will take a look at three different aspects of graph visualization within the context of biochemical systems: the representation and interactive exploration of static networks, the visual analysis of dynamic networks, and the comparison of two network graphs. We will demonstrate, how established infovis techniques can be combined with new algorithms and applied to specific problems in the area of metabolic network visualization. We reconstruct the metabolic network covering the complete set of chemical reactions present in a generalized eucaryotic cell from real world data available from a popular metabolic pathway data base and present a suitable data structure. As the constructed network is very large, it is not feasible for the display as a whole. Instead, we introduce a technique to analyse this static network in a top-down approach starting with an overview and displaying detailed reaction networks on demand. This exploration method is also applied to compare metabolic networks in different species and from different resources. As for the analysis of dynamic networks, we present a framework to capture changes in the connectivity as well as changes in the attributes associated with the network’s elements

Navigating the Human Metabolome for Biomarker Identification and Design of Pharmaceutical Molecules

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics. Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants

Visualisierung biochemischer Reaktionsnetze

In dieser Arbeit werden Anforderungen an die Darstellung biochemischer Reaktionsnetze untersucht und die Netze unter dem Gesichtspunkt der Visualisierung modelliert. Anschliessend wird ein Algorithmus zum Zeichnen biochemischer Reaktionsnetze entwickelt und analysiert.In this dissertation we investigate the requirements for the visualisation of biochemical reaction networks. We compose a model for these networks that lends itself to visualisation and develop and analyse an algorithm to create drawings of the networks

Network design and analysis for multi-enzyme biocatalysis

In vitro synthesis is a biotechnological alternative to classic chemical catalysts. However, the manual design of multi-step biosynthesis routes is very challenging, especially when enzymes from different organisms are involved. There is therefore a demand for in silico tools to guide the design of such synthesis routes using computational methods for the path-finding, as well as the reconstruction of suitable genome-scale metabolic networks that are able to harness the growing amount of biological data available. This work presents an algorithm for finding pathways from arbitrary metabolites to a target product of interest. The algorithm is based on a mixed-integer linear program (MILP) and combines graph topology and reaction stoichiometry. The pathway candidates are ranked using different ranking criteria to help finding the best suited synthesis pathway candidates. Additionally, a comprehensive workflow for the reconstruction of metabolic networks based on data of the Kyoto Encyclopedia of Genes and Genomes (KEGG) combined with thermodynamic data for the determination of reaction directions is presented. The workflow comprises a filtering scheme to remove unsuitable data. With this workflow, a panorganism network reconstruction as well as single organism network models are established. These models are analyzed with graph-theoretical methods. It is also discussed how the results can be used for the planning of biosynthetic production pathways.In vitro Synthese ist eine biotechnologische Alternative zu klassischen chemischen Katalysen. Der manuelle Entwurf von mehrstufigen Biosynthesewegen ist jedoch sehr anspruchsvoll, vor allem wenn Enzyme verschiedener Organismen beteiligt sind. Daher besteht ein Bedarf an Methoden, die helfen solche Synthesewege in silico zu entwerfen und die in der Lage sind große Mengen biologischer Daten zu bewältigen - insbesondere in Hinblick auf die Rekonstruktion genomskaliger metabolischer Netzwerkmodelle und die Pfadsuche in solchen Netzwerken. In dieser Arbeit wird ein Algorithmus zur Pfadsuche zu einem Zielprodukt ausgehend von beliebigen Substraten präsentiert. Der Algorithmus basiert auf einem gemischt-ganzzahligen linearen Programm, das Graphtopologie mit Reaktionsstöchiometrien kombiniert. Die Pfadkandidaten werden anhand verschiedener Kriterien geordnet, um die am besten geeigneten Kandidaten für die Synthese zu finden. Außerdem wird ein umfassender Workflow für die Rekonstruktion metabolischer Netzwerke basierend auf der Datenbank KEGG sowie thermodynamischen Daten vorgestellt. Dieser umfasst einen Filter, der anhand verschiedener Kriterien geeignete Reaktionen auswählt. Der Workflow wird zum Erstellen einer organismusübergreifenden Netzwerkrekonstruktion, sowie Netzwerken einzelner Organismen genutzt. Diese Modelle werden mit graphentheoretischen Methoden analysiert. Es wird diskutiert, wie die Ergebnisse für die Planung von biosynthetischen Produktionswegen genutzt werden können.BMBF; Initiative “Biotechnologie 2020+: Basistechnologien für eine nächste Generation biotechnologischer Verfahren”; Projekt MECA

Kreuzungen in Cluster-Level-Graphen

Clustered graphs are an enhanced graph model with a recursive clustering of the vertices according to a given nesting relation. This prime technique for expressing coherence of certain parts of the graph is used in many applications, such as biochemical pathways and UML class diagrams. For directed clustered graphs usually level drawings are used, leading to clustered level graphs. In this thesis we analyze the interrelation of clusters and levels and their influence on edge crossings and cluster/edge crossings.Cluster-Graphen sind ein erweitertes Graph-Modell mit einem rekursiven Clustering der Knoten entsprechend einer gegebenen Inklusionsrelation. Diese bedeutende Technik um Zusammengehörigkeit bestimmter Teile des Graphen auszudrücken wird in vielen Anwendungen benutzt, etwa biochemischen Reaktionsnetzen oder UML Klassendiagrammen. Für gerichtete Cluster-Graphen werden üblicherweise Level-Zeichnungen verwendet, was zu Cluster-Level-Graphen führt. Diese Arbeit analysiert den Zusammenhang zwischen Clustern und Level und deren Auswirkungen auf Kantenkreuzungen und Cluster/Kanten-Kreuzungen