Literature DB >> 15531617

ISYMOD: a knowledge warehouse for the identification, assembly and analysis of bacterial integrated systems.

Julie Chabalier1, Cécile Capponi, Yves Quentin, Gwennaele Fichant.   

Abstract

MOTIVATION: Complex biological functions emerge from interactions between proteins in stable supra-molecular assemblies and/or through transitory contacts. Most of the time protein partners of the assemblies are composed of one or several domains which exhibit different biochemical functions. Thus the study of cellular process requires the identification of different functional units and their integration in an interaction network; such complexes are referred to as integrated systems. In order to exploit with optimum efficiency the increased release of data, automated bioinformatics strategies are needed to identify, reconstruct and model such systems. For that purpose, we have developed a knowledge warehouse dedicated to the representation and acquisition of bacterial integrated systems involved in the exchange of the bacterial cell with its environment.
RESULTS: ISYMOD is a knowledge warehouse that consistently integrates in the same environment the data and the methods used for their acquisition. This is achieved through the construction of (1) a domain knowledge base (DKB) devoted to the storage of the knowledge about the systems, their functional specificities, their partners and how they are related and (2) a methodological knowledge base (MKB) which depicts the task layout used to identify and reconstruct functional integrated systems. Instantiation of the DKB is obtained by solving the tasks of the MKB, whereas some tasks need instances of the DKB to be solved. AROM, an object-based knowledge representation system, has been used to design the DKB, and its task manager, AROMTasks, for developing the MKB. In this study two integrated systems, ABC transporters and two component systems, both involved in adaptation processes of a bacterial cell to its biotope, have been used to evaluate the feasibility of the approach.

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Year:  2004        PMID: 15531617     DOI: 10.1093/bioinformatics/bti137

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  1 in total

1.  A transversal approach to predict gene product networks from ontology-based similarity.

Authors:  Julie Chabalier; Jean Mosser; Anita Burgun
Journal:  BMC Bioinformatics       Date:  2007-07-02       Impact factor: 3.169

  1 in total

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