Functional similarities of reaction sets in metabolic pathways

2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010

Volumn , Issue , 2010, Pages 102-111

  Ay, Ferhat ^a   Kahveci, Tamer ^a  

^a University of Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLOSED FORM; FUNCTIONAL SIMILARITY; HIGH DIMENSIONAL SPACES; METABOLIC FLUX; METABOLIC PATHWAYS; MINIMUM ENCLOSING BALL; NOVEL METHODS; PATHWAY COMPONENTS; POLYHEDRAL CONE; POLYTOPES; STEADY STATE;

METABOLISM; TOPOLOGY;

BIOINFORMATICS;

EID: 77958050626     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1854776.1854795     Document Type: Conference Paper

References (30)

1. F. Ay and T. Kahveci. SubMAP: Aligning metabolic pathways with subnetwork mappings. In International Conference on Research in Computational Molecular Biology (RECOMB), To appear 2010.
2. F. Ay, T. Kahveci, and V. Crecy-Lagard. A fast and accurate algorithm for comparative analysis of metabolic pathways. Journal of Bioinformatics and Computational Biology (JBCB), 7(3):389-428, 2009.
3. A. Cakmak and G. Ozsoyoglu. Mining biological networks for unknown pathways. Bioinformatics, 23(20):2775-83, 2007.
4. M. Campillos, M. Kuhn, A. C. Gavin, L. J. Jensen, and P. Bork. Drug Target Identification Using Side-Effect Similarity. Science, 321(5886):263-6, 2008.
5. C. Conradi, D. Flockerzi, J. Raisch, and J. Stelling. Subnetwork analysis reveals dynamic features of complex (bio)chemical networks. Proceedings of the National Academy of Sciences (PNAS), 104(49):19175-80, 2007.
6. K. Fischer, B. Gartner, and M. Kutz. Fast Smallest-Enclosing-Ball Computation in High Dimensions. In 11th Annual European Symposium on Algorithms (ESA), pages 630-41, 2003.
7. C. Francke, R. Siezen, and B. Teusink. Reconstructing the metabolic network of a bacterium from its genome. Trends in Microbiology, 13(11):550-8, 2005.
8. M. Heymans and A. Singh. Deriving phylogenetic trees from the similarity analysis of metabolic pathways. Bioinformatics, 19:138-46, 2003.
9. R. Jothi, T. M. Przytycka, and L. Aravind. Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: A comprehensive assessment. BMC Bioinformatics, 8:173, 2007.
10. J. Kato and M. Hashimoto. Construction of consecutive deletions of the Escherichia coli chromosome. Molecular Systems Biology, 3:132, 2007.
11. S. Klamt and J. Stelling. Two approaches for metabolic pathway analysis? Trends in Biotechnology, 21(2):64-9, 2003.
12. A. Larhlimi and A. Bockmayr. A new constraint-based description of the steady-state flux cone of metabolic networks. Discrete Applied Mathematics, 157:2257-66, 2009.
13. L. Lynd, C. Wyman, and T. Gerngross. Biocommodity Engineering. Biotechnology Progress, 15(5):777-93, 1999.
14. H. W. Ma and A. P. Zeng. The connectivity structure, giant strong component and centrality of metabolic networks. Bioinformatics, 19(11):1423-30, 2003.
15. A. A. Margolin, K. Wang, W. K. Lim, M. Kustagi, I. Nemenman, and A. Califano. Reverse engineering cellular networks. Nature Protocols, 1(2):662-71, 2006.
16. J. A. Papin, N. D. Price, and B. O. Palsson. Extreme pathway lengths and reaction participation in genome-scale metabolic networks. Genome Research, 12(12):1889-900, 2002.
17. A. Samal, S. Singh, V. Giri, S. Krishna, N. Raghuram, and S. Jain. Low degree metabolites explain essential reactions and enhance modularity in biological networks. BMC Bioinformatics, 7:118, 2006.
18. S. Schuster and C. Hilgetag. On elementary flux modes in biochemical reaction systems at steady state. Journal of Biological Systems, 2:165-82, 1994.
19. S. Schuster, C. Hilgetag, J. Woods, and D. Fell. Reaction routes in biochemical reaction systems: algebraic properties, validated calculation procedure and example from nucleotide metabolism. Journal of Mathematical Biology, 45(2):153-81, 2002.
20. S. Schuster, T.Dandekar, and D. A. Fell. Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends in Biotechnology, 17(2):53-60, 1999.
21. D. Segre, D. Vitkup, and G. M. Church. Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences (PNAS), 99(23):15112-7, 2002.
22. P. Sridhar, T. Kahveci, and S. Ranka. An iterative algorithm for metabolic network-based drug target identification. In Pacific Symposium on Biocomputing (PSB), volume 12,pages 88-99, 2007.
23. J. Stelling, S. Klamt, K. Bettenbrock, S. Schuster, and E. D. Gilles. Metabolic network structure determines key aspects of functionality and regulation. Nature, 420:190-193, 2002.
24. G. Stephanopoulos. Metabolic engineering. Current Opinions in Biotechnology, 5(2):196-200, 1994.
25. B. L. Steven and B. O. Palsson. Expa: a program for calculating extreme pathways in biochemical reaction networks. Bioinformatics, 21(8):1739-40, 2005.
26. A. Tatsuya, M. Satoru, and K. Satoru. Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In Pacific Symposium on Biocomputing (PSB), volume 4, pages 17-28, 1999.
27. M. Terzer and J. Stelling. Large scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24(19):2229-35, 2008.
28. C. Trinh, A. Wlaschin, and F. Srienc. Elementary mode analysis: a useful metabolic pathway analysis tool for characterizing cellular metabolism. Applied Microbiology and Biotechnology, 81(5):813-26, 2009.
29. S. Wong, L. Zhang, A. Tong, Z. Li, D. Goldberg, O. King, G. Lesage, M. Vidal, B. Andrews, H. Bussey, C. Boone, and F. Roth. Combining biological networks to predict genetic interactions. Proceedings of the National Academy of Sciences (PNAS), 101(44):15682-7, 2004.
30. X. Wu, L. Zhu, J. Guo, D.-Y. Zhang, and K. Lin. Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations. Nucleic Acids Research, 34(7):2137-50, 2006.