Quantitative global studies of reactomes and metabolomes using a vectorial representation of reactions and chemical compounds
| Autor: | Juan C Triviño, Florencio Pazos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Proteomics
Biochemical Phenomena Systems biology Structure (category theory) Biology Bioinformatics Set (abstract data type) Structural Biology Modelling and Simulation Research article Escherichia coli Databases Protein lcsh:QH301-705.5 Molecular Biology Sequence Molecular Structure Hierarchy (mathematics) Basis (linear algebra) Escherichia coli Proteins Applied Mathematics Representation (systemics) Computer Science Applications Hierarchical clustering Metabolism lcsh:Biology (General) Modeling and Simulation Biological system |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname BMC Systems Biology, Vol 4, Iss 1, p 46 (2010) BMC Systems Biology |
| Popis: | 11 páginas, 3 figuras, 2 tablas. [Background]: Global studies of the protein repertories of organisms are providing important information on the characteristics of the protein space. Many of these studies entail classification of the protein repertory on the basis of structure and/or sequence similarities. The situation is different for metabolism. Because there is no good way of measuring similarities between chemical reactions, there is a barrier to the development of global classifications of "metabolic space" and subsequent studies comparable to those done for protein sequences and structures. [Results]: In this work, we propose a vectorial representation of chemical reactions, which allows them to be compared and classified. In this representation, chemical compounds, reactions and pathways may be represented in the same vectorial space. We show that the representation of chemical compounds reflects their physicochemical properties and can be used for predictive purposes. We use the vectorial representations of reactions to perform a global classification of the reactome of the model organism E. coli. [Conclusions]: We show that this unsupervised clustering results in groups of enzymes more coherent in biological terms than equivalent groupings obtained from the EC hierarchy. This hierarchical clustering produces an optimal set of 21 groups which we analyzed for their biological meaning. This work was partially funded by projects BIO2006-15318 and PIE 200620I240 from the Spanish Ministry for Education and Science. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|