KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

<p>Abstract</p> <p>Background</p> <p>The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.</p> <p>Results</p> <p>A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented.</p> <p>Conclusion</p> <p>KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at: <url>http://www.grissom.gr/keggconverter</url></p

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

Journal URL: http://www.biomedcentral.com/bmcbioinformatics

Building in-silico pathway SBML models from heterogeneous sources

The recent revolutionary developments concerning the high throughput (-omics) measuring techniques in life sciences is expediting the way for the development of in silico models envisioning the Systems biology perspective in the description of biological problems. As a resuit, very large open biological databases provide in silico descriptions in various formats, of biochemical pathways related to various cellular physiological aspects across the evolutionary climax. However, the lack of standardization regarding conceptual biological data representation incurs sheer limitations with respect to the functionality as well as the scientific completeness of the respective models. In this work, a software solution is presented which successfully bridges the gap towards building in-silico metabolic pathway models in Systems Biology Markup Language (SBML) format (standard SBML, CellDesigner SBML) by exploiting various XML based formats (SBML, KGML- KEGG Markup Language-, CellML - Cell Markup Language-, for pathway representation). Our solution provides methods for the biochemically correct transformation, curation and automatic simulation of the pathways, thus accomplishing the setup of fully functional in-silico models

GRISSOM Platform: Enabling Distributed Processing and Management of Biological Data Through Fusion of Grid and Web Technologies

Transcriptomic technologies have a critical impact in the revolutionary changes that reshape biological research. Through the recruitment of novel high-throughput instrumentation and advanced computational methodologies, an unprecedented wealth of quantitative data is produced. Microarray experiments are considered high-throughput, both in terms of data volumes (data intensive) and processing complexity (computationally intensive). In this paper, we present grids for in silico systems biology and medicine (GRISSOM), a web-based application that exploits GRID infrastructures for distributed data processing and management, of DNA microarrays (cDNA, Affymetrix, Illumina) through a generic, consistent, computational analysis framework. GRISSOM performs versatile annotation and integrative analysis tasks, through the use of third-party application programming interfaces, delivered as web services. In parallel, by conforming to service-oriented architectures, it can be encapsulated in other biomedical processing workflows, with the help of workflow enacting software, like Taverna Workbench, thus rendering access to its algorithms, transparent and generic. GRISSOM aims to set a generic paradigm of efficient metamining that promotes translational research in biomedicine, through the fusion of grid and semantic web computing technologies