Browsing by Subject "Biological networks"

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    Algorithms for effective querying of compound graph-based pathway databases
    (BioMed Central Ltd., 2009-11-16) Doğrusöz, Uğur; Çetintaş, Ahmet; Demir, Emek; Babur, Özgün
    Background: Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools. Results: Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases. Conclusion: The PATIKA Project Web site is http://www.patika.org. PATIKAweb version 2.1 is available at http://web.patika.org. © 2009 Dogrusoz et al; licensee BioMed Central Ltd.
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    ItemOpen Access
    Automated layout of process description maps drawn in systems biology graphical notation = Systems biology graphical notation kullanılarak çizilen proses diyagramlarının otomatik yerleştirilmesi
    (2014) Genç, Begüm
    Evolving technology has increased the focus on genomics. The combination of today’s advanced studies with decades of molecular biology research yield in huge amount of pathway data. These models can be used to improve high-throughput data analysis by linking correlation to the causation, shedding light on many complex diseases. In order to prevent ambiguity and ensure regularity of the research, a need for using a standard notation has emerged. Systems Biology Graphical Notation (SBGN) is a visual language developed by a community of biochemists, modellers and computer scientists with the intention of enabling scientists to represent networks, including models of cellular processes, in a standard, unambiguous way. SBGN is formed of three languages: process, entity relationship and activity flow. This research is focused on its process diagram branch. Automated layout is commonly used to clearly visualize the information represented by graphs. Considering the fact that, biological pathways includes nested structures (e.g., nucleoplasms), we have made use of a force-directed automatic layout algorithm called Compound Spring Embedder (CoSE), which supports the compound graph structures. On top of this layout structure, we have developed a specialized layout algorithm called SBGN-PD layout. SBGN-PD layout enhancements mainly include properly tiling of complex members and disconnected molecules, placement of product and substrate edges on the opposite sides of a process node without disturbing the force-directed structure of the algorithm.