Functional Classification of Genome-Scale Metabolic Networks. (19th January 2009)

• Record Type: Journal Article
• Authors: Ebenhöh, Oliver; Handorf, Thomas
• Other Names: Steinfath Matthias Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2009(2009)
• Publication Date: 2009-01-19
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2009/570456
• Languages: English
• ISSNs: 1687-4145

Abstract

Abstract : We propose two strategies to characterize organisms with respect to their metabolic capabilities. The first, investigative, strategy describes metabolic networks in terms of their capability to utilize different carbon sources, resulting in the concept of carbon utilization spectra . In the second, predictive, approach minimal nutrient combinations are predicted from the structure of the metabolic networks, resulting in a characteristic nutrient profile . Both strategies allow for a quantification of functional properties of metabolic networks, allowing to identify groups of organisms with similar functions. We investigate whether the functional description reflects the typical environments of the corresponding organisms by dividing all species into disjoint groups based on whether they are aerotolerant and/or photosynthetic. Despite differences in the underlying concepts, both measures display some common features. Closely related organisms often display a similar functional behavior and in both cases the functional measures appear to correlate with the considered classes of environments. Carbon utilization spectra and nutrient profiles are complementary approaches toward a functional classification of organism-wide metabolic networks. Both approaches contain different information and thus yield different clusterings, which are both different from the classical taxonomy of organisms. Our results indicate that a sophisticated combination of our approaches will allow for a quantitative description reflecting the lifestyles of organisms.