Publications

20 Publications visible to you, out of a total of 20

Abstract (Expand)

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments.

Authors: Mélanie Courtot, Nick Juty, Christian Knüpfer, Dagmar Waltemath, Anna Zhukova, Andreas Dräger, Michel Dumontier, Andrew Finney, Martin Golebiewski, Janna Hastings, Stefan Hoops, Sarah Keating, Douglas B Kell, Samuel Kerrien, James Lawson, Allyson Lister, James Lu, Rainer Machne, Pedro Mendes, Matthew Pocock, Nicolas Rodriguez, Alice Villeger, Darren J Wilkinson, Sarala Wimalaratne, Camille Laibe, Michael Hucka, Nicolas Le Novère

Date Published: 27th Oct 2011

Publication Type: Not specified

Abstract

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Authors: Vladimir B Teif, Karsten Rippe

Date Published: 1st Aug 2011

Publication Type: Not specified

Abstract (Expand)

SUMMARY: Systems Biology Markup Language (SBML) is the leading exchange format for mathematical models in Systems Biology. Semantic annotations link model elements with external knowledge via unique database identifiers and ontology terms, enabling software to check and process models by their biochemical meaning. Such information is essential for model merging, one of the key steps towards the construction of large kinetic models. SemanticSBML is a tool that helps users to check and edit MIRIAM annotations and SBO terms in SBML models. Using a large collection of biochemical names and database identifiers, it supports modellers in finding the right annotations and in merging existing models. Initially, an element matching is derived from the MIRIAM annotations and conflicting element attributes are categorized and highlighted. Conflicts can then be resolved automatically or manually, allowing the user to control the merging process in detail. AVAILABILITY: SemanticSBML comes as a free software written in Python and released under the GPL 3. A Debian package, a source package for other Linux distributions, a Windows installer and an online version of semanticSBML with limited functionality are available at http://www.semanticsbml.org. A preinstalled version can be found on the Linux live DVD SB.OS, available at http://www.sbos.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Authors: Firstname Lastname, Jannis Uhlendorf, Timo Lubitz, Marvin Schulz, Edda Klipp, Wolfram Liebermeister

Date Published: 17th Nov 2009

Publication Type: Not specified

Abstract (Expand)

BACKGROUND: During the lifetime of a fermenter culture, the soil bacterium S. coelicolor undergoes a major metabolic switch from exponential growth to antibiotic production. We have studied gene expression patterns during this switch, using a specifically designed Affymetrix genechip and a high-resolution time-series of fermenter-grown samples. RESULTS: Surprisingly, we find that the metabolic switch actually consists of multiple finely orchestrated switching events. Strongly coherent clusters of genes show drastic changes in gene expression already many hours before the classically defined transition phase where the switch from primary to secondary metabolism was expected. The main switch in gene expression takes only 2 hours, and changes in antibiotic biosynthesis genes are delayed relative to the metabolic rearrangements. Furthermore, global variation in morphogenesis genes indicates an involvement of cell differentiation pathways in the decision phase leading up to the commitment to antibiotic biosynthesis. CONCLUSIONS: Our study provides the first detailed insights into the complex sequence of early regulatory events during and preceding the major metabolic switch in S. coelicolor, which will form the starting point for future attempts at engineering antibiotic production in a biotechnological setting.

Authors: Kay Nieselt, Florian Battke, Alexander Herbig, Per Bruheim, Alexander Wentzel, Øyvind M Jakobsen, Håvard Sletta, Mohammad T Alam, Maria E Merlo, Firstname Lastname, Firstname Lastname, Edward R Morrissey, Miguel A Juarez-Hermosillo, Antonio Rodríguez-García, Merle Nentwich, Louise Thomas, Mudassar Iqbal, Roxane Legaie, William H Gaze, Gregory L Challis, Ritsert C Jansen, Lubbert Dijkhuizen, David A Rand, David L Wild, Michael Bonin, Jens Reuther, Wolfgang Wohlleben, Margaret C M Smith, Nigel J Burroughs, Juan F Martín, David A Hodgson, Eriko Takano, Rainer Breitling, Trond E Ellingsen, Elizabeth M H Wellington

Date Published: 28th May 2009

Publication Type: Not specified

Abstract (Expand)

The torrent of data emerging from the application of new technologies to functional genomics and systems biology can no longer be contained within the traditional modes of data sharing and publication with the consequence that data is being deposited in, distributed across and disseminated through an increasing number of databases. The resulting fragmentation poses serious problems for the model organism community which increasingly rely on data mining and computational approaches that require gathering of data from a range of sources. In the light of these problems, the European Commission has funded a coordination action, CASIMIR (coordination and sustainability of international mouse informatics resources), with a remit to assess the technical and social aspects of database interoperability that currently prevent the full realization of the potential of data integration in mouse functional genomics. In this article, we assess the current problems with interoperability, with particular reference to mouse functional genomics, and critically review the technologies that can be deployed to overcome them. We describe a typical use-case where an investigator wishes to gather data on variation, genomic context and metabolic pathway involvement for genes discovered in a genome-wide screen. We go on to develop an automated approach involving an in silico experimental workflow tool, Taverna, using web services, BioMart and MOLGENIS technologies for data retrieval. Finally, we focus on the current impediments to adopting such an approach in a wider context, and strategies to overcome them.

Authors: Damian Smedley, Morris A Swertz, Firstname Lastname, Glenn Proctor, Michael Zouberakis, Jonathan Bard, John M Hancock, Paul Schofield

Date Published: 30th Dec 2008

Publication Type: Not specified

Abstract (Expand)

This paper briefly describes the SABIO-RK database model for the storage of reaction kinetics information and the guidelines followed within the SABIO-RK project to annotate the kinetic data. Such annotations support the definition of cross links to other related databases and augment the semantics of the data stored in the database.

Authors: Firstname Lastname, Martin Golebiewski, Renate Kania, Firstname Lastname, Saqib Mir, Andreas Weidemann, Ulrike Wittig

Date Published: 14th Sep 2007

Publication Type: Not specified

Abstract (Expand)

It is increasingly common to combine Microarray and Quantitative Trait Loci data to aid the search for candidate genes responsible for phenotypic variation. Workflows provide a means of systematically processing these large datasets and also represent a framework for the re-use and the explicit declaration of experimental methods. In this article, we highlight the issues facing the manual analysis of microarray and QTL data for the discovery of candidate genes underlying complex phenotypes. We show how automated approaches provide a systematic means to investigate genotype-phenotype correlations. This methodology was applied to a use case of resistance to African trypanosomiasis in the mouse. Pathways represented in the results identified Daxx as one of the candidate genes within the Tir1 QTL region. Subsequent re-sequencing in Daxx identified a deletion of an amino acid, identified in susceptible mouse strains, in the Daxx-p53 protein-binding region. This supports recent experimental evidence that apoptosis could be playing a role in the trypanosomiasis resistance phenotype. Workflows developed in this investigation, including a guide to loading and executing them with example data, are available at http://workflows.mygrid.org.uk/repository/myGrid/PaulFisher/.

Authors: Paul Fisher, Cornelia Hedeler, Firstname Lastname, Helen Hulme, Harry Noyes, Stephen Kemp, Robert Stevens, Andrew Brass

Date Published: 20th Aug 2007

Publication Type: Not specified

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