Cupe - the CUBIC Pathway Editor

Cupe(CUBIC Pathway Editor) is a graphical editor for the automatic or interactive generation and display of metabolic networks. Cupe combines the user guidance by its graphical user interface (GUI) with the ability of automatic graph drawing and the possibility for manual interaction. Furthermore, it provides a programming interface for analysis, simulation and cross linking of reactions. One of the outstanding features of Cupe is its automatic layout mechanism which is provided by utilising the well-known AGD library. The adaptation and development of layout algorithms for the requirements of metabolic networks is an interdisciplinary cooperation between the Department of Computer Science, Cologne University, the Chair of Algorithm Engineering, Dortmund University, and the Cologne University Bioinformatics Center. Poster presentation in 14th International Symposium on Graph Drawin

A Fast Layout Algorithm for k-Level Graphs

In this paper, we present a fast layout algorithm for k-level graphs with given permutations of the vertices on each level. The algorithm can be used in particular as a third phase of the Sugiyama algorithm (1981). The Sugiyama algorithm computes a layout for an arbitrary graph by (1) converting it into a k-level graph, (2) reducing the number of edge crossings by permuting the vertices on the levels, and (3) assigning y-coordinates to the levels and x-coordinates to the vertices. In the layouts generated by our algorithm, every edge will have at most two bends, and will be drawn vertically between these bends

TULIP 4

Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 15 years of research and development, Tulip is built on a suite of tools and techniques , that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, Tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels

Simple and Efficient Bilayer Cross Counting

We consider the problem of counting the interior edge crossings when a bipartite graph G=(V,E) with node set V and edge set E is drawn such that the nodes of the two shores of the bipartition are on two parallel lines and the edges are straight lines. The efficient solution of this problem is important in layered graph drawing.Our main observation is that it can be reduced to counting the inversions of a certain sequence. This leads to an O(|E|+|C|) algorithm, where C denotes the set of pairwise interior edge crossings, as well as to a simple O(|E|log|V_{m small}|) algorithm, where V_{m small} is the smaller cardinality node set in the bipartition of the node set |V| of the graph. We present the algorithms and the results of computational experiments with these and other algorithms on a large collection of instances

Graph layout using subgraph isomorphisms

Today, graphs are used for many things. In engineering, graphs are used to design circuits in very large scale integration. In computer science, graphs are used in the representation of the structure of software. They show information such as the flow of data through the program (known as the data flow graph [1]) or the information about the calling sequence of programs (known as the call graph [145]). These graphs consist of many classes of graphs and may occupy a large area and involve a large number of vertices and edges. The manual layout of graphs is a tedious and error prone task. Algorithms for graph layout exist but tend to only produce a 'good' layout when they are applied to specific classes of small graphs. In this thesis, research is presented into a new automatic graph layout technique. Within many graphs, common structures exist. These are structures that produce 'good' layouts that are instantly recognisable and, when combined, can be used to improve the layout of the graphs. In this thesis common structures are given that are present in call graphs. A method of using subgraph isomorphism to detect these common structures is also presented. The method is known as the ANHOF method. This method is implemented in the ANHOF system, and is used to improve the layout of call graphs. The resulting layouts are an improvement over layouts from other algorithms because these common structures are evident and the number of edge crossings, clusters and aspect ratio are improved

Using Sifting for k-Layer Straightline Crossing Minimization

We present a new algorithm for k-layer straightline crossing minimization which is based on sifting that is a heuristic for dynamic reordering of decision diagrams used during logic synthesis and formal verification of logic circuits. The experiments prove sifting to be very efficient. In particular it outperforms the traditional layer by layer sweep based heuristics known from literature by far when applied to k-layered graphs with k \ge 3

Entwicklung von Methoden zur automatischen Generierung, grafischen Darstellung und interaktiven Analyse von metabolischen Netzwerken

Die große Anzahl der metabolischen Reaktionen und der an ihnen beteiligten Komponenten kann nur mit Methoden zur automatisierten Darstellung und Analyse von metabolischen Netzwerken erforscht werden. Mit dem CUBIC Pathway Editor "Cupe" existiert jetzt ein Programm, das auf einzigartige Art und Weise die Generierung, Darstellung und Analyse von Stoffwechselnetzwerken mit den aktuellsten Methoden zum automatischen Zeichnen von Graphen verbindet. Dank eines integrierten Datenbestandes von ca. 32.000 Reaktionen und ca 47.000 Komponenten bietet Cupe zudem die größte Reaktionsdatenbank unter allen bisher bekannten Programmen für die Stoffwechselanalyse. Der Anwender wird sowohl bei der manuellen als auch bei der automatischen Erzeugung von metabolischen Netzwerken unterstützt und hat mit Cupe die Möglichkeit, alle Netzwerkelemente völlig frei zu formatieren. Besonders die Fähigkeit, von Cupe Subnetzwerke in Superknoten bzw. Clustern zusammenzufassen und einen Metabolitpool zu verwalten, erlaubt es, den Graphen eines Reaktionsnetzwerks erheblich zu vereinfachen. Dadurch ist es möglich geworden, die Lesbarkeit automatisch gezeichneter Graphen deutlich zu verbessern. Mit der Entwicklung der "Cubic-Sparse-Matrix" verfügt "Cupe" über ein neuartiges und effizientes Datenmodell. Auf der Grundlage dieses Datenmodells konnte die "Mengenlehre für metabolische Netzwerke" für die Vergleichende Analyse von Reaktionsnetzwerken entwickelt werden. Weitere Analysemethoden können über die einfach zu bedienende Erweiterungsschnittstelle zu Cupe hinzugefügt werden. Die verschiedenen Module von Cupe verknüpfen so zahlreiche Forschungsgebiete und bilden dadurch eine interdisziplinäre Forschungs- und Ausbildungsplattform, deren Weiterentwicklung über das im Internet bereitgestellte "Cupe Knowledge Portal" koordiniert wird