Biosynthetic potentials from species-specific metabolic networks.

Studies of genome-scale metabolic networks allow for qualitative and quantitative descriptions of an organism's capability to convert nutrients into products. The set of synthesizable products strongly depends on the provided nutrients as well as on the structure of the metabolic network. Here, we apply the method of network expansion and the concept of scopes, describing the synthesizing capacities of an organism when certain nutrients are provided. We analyze the biosynthetic properties of four species: Arabidopsis thaliana, Saccharomyces cerevisiae, Buchnera aphidicola, and Escherichia coli. Matthäus et al. have recently developed a method to identify clusters of scopes, reflecting specific biological functions and exhibiting a hierarchical arrangement, using the network comprising all reactions in KEGG. We extend this method by considering random sets of nutrients on well-curated networks of the investigated species from BioCyc. We identify structural properties of the networks that allow to differentiate their biosynthetic capabilities. Furthermore, we evaluate the quality of the clustering of scopes applied to the species-specific networks. Our study provides a novel assessment of the biosynthetic properties of different species.

[1]  R Heinrich,et al.  Hierarchy of metabolic compounds based on their synthesising capacity. , 2006, Systems biology.

[2]  Oliver Ebenhöh,et al.  A cross species comparison of metabolic network functions. , 2005, Genome informatics. International Conference on Genome Informatics.

[3]  Ulrik Brandes,et al.  On Modularity Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.

[4]  R Heinrich,et al.  The regulatory principles of glycolysis in erythrocytes in vivo and in vitro. A minimal comprehensive model describing steady states, quasi-steady states and time-dependent processes. , 1976, The Biochemical journal.

[5]  C. Ouzounis,et al.  Expansion of the BioCyc collection of pathway/genome databases to 160 genomes , 2005, Nucleic acids research.

[6]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[7]  B. Palsson,et al.  The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[8]  D. Fell,et al.  The small world inside large metabolic networks , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[9]  S. Schuster,et al.  ON ELEMENTARY FLUX MODES IN BIOCHEMICAL REACTION SYSTEMS AT STEADY STATE , 1994 .

[10]  Reinhart Heinrich,et al.  Structural analysis of expanding metabolic networks. , 2004, Genome informatics. International Conference on Genome Informatics.

[11]  Oliver Ebenhöh,et al.  Expanding Metabolic Networks: Scopes of Compounds, Robustness, and Evolution , 2005, Journal of Molecular Evolution.

[12]  Adam M. Feist,et al.  A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information , 2007, Molecular systems biology.

[13]  Oliver Ebenhöh,et al.  Biosynthetic Potentials of Metabolites and Their Hierarchical Organization , 2008, PLoS Comput. Biol..

[14]  Kiyoko F. Aoki-Kinoshita,et al.  From genomics to chemical genomics: new developments in KEGG , 2005, Nucleic Acids Res..

[15]  Bernhard O Palsson,et al.  Network-based analysis of metabolic regulation in the human red blood cell. , 2003, Journal of theoretical biology.

[16]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  S. Strogatz Exploring complex networks , 2001, Nature.

[18]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[19]  Antje Chang,et al.  BRENDA , the enzyme database : updates and major new developments , 2003 .

[20]  B. Palsson,et al.  Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use , 1994, Bio/Technology.