An overview of data models for the analysis of biochemical pathways
Professor in the Computing Science and Engineering Department at the Université Catholique de Louvain. His current research interests include modelling and analysis of biochemical networks, and constraint programming.
Professor of Bioinformatics at the University of Glasgow (UK) where he directs the Bioinformatics Research Centre. His research interests include modelling and analysis of biochemical networks and protein structures.
Involved in various aspects of the aMAZE project (model design, data integration, application of graph theory for the inference of metabolic pathways). He also developed the software suite Regulatory Sequence Analysis Tools.
Professor at the Faculty of Sciences of the Université Libre de Bruxelles. Between 1995 and 2002 she also held the position of group leader at the EBI in Cambridge (UK). Her research interests cover structural computational biology and bioinformatics.
Yves Deville, Computing Science and Engineering Department, Université Catholique de Louvain, Place Saint-Barbe 2, B-1348 Louvain-la-Neuve, Belgium E-mail: deville{at}info.ucl.ac.be
Biochemical pathways such as metabolic, regulatory or signal transduction pathways can be viewed as interconnected processes forming an intricate network of functional and physical interactions between molecular species in the cell. The amount of information available on such pathways for different organisms is increasing very rapidly. This is offering the possibility of performing various analyses on the structure of the full network of pathways for one organism as well as across different organisms, and has therefore generated interest in developing databases for storing and managing this information. Analysing these networks remains far from straightforward owing to the nature of the databases, which are often heterogeneous, incomplete or inconsistent. Pathway analysis is hence a challenging problem in systems biology and in bioinformatics.
Various forms of data models have been devised for the analysis of biochemical pathways. This paper presents an overview of the types of models used for this purpose, concentrating on those concerned with the structural aspects of biochemical networks. In particular, the different types of data models found in the literature are classified using a unified framework. In addition, how these models have been used in the analysis of biochemical networks is described. This enables us to underline the strengths and weaknesses of the different approaches, as well as to highlight relevant future research directions.
Keywords: biochemical networks, data analysis, graphs, modelling
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