Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T06:22:14.278Z Has data issue: false hasContentIssue false

Graph methods for the investigation of metabolic networks in parasitology

Published online by Cambridge University Press:  06 May 2010

LUDOVIC COTTRET
Affiliation:
INRA, UMR 1089 Xénobiotiques, 180 chemin de Tournefeuille BP 93173, F31027 Toulouse Cedex3, France
FABIEN JOURDAN*
Affiliation:
INRA, UMR 1089 Xénobiotiques, 180 chemin de Tournefeuille BP 93173, F31027 Toulouse Cedex3, France
*
*Corresponding author: INRA, UMR 1089 Xénobiotiques, 180 chemin de Tournefeuille BP 93173, F31027 Toulouse Cedex3, France. Tel: +33 561 28 57 15. Fax: +33 561 28 52 44. E-mail: [email protected]

Summary

Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Antonov, A. V., Dietmann, S., Wong, P. and Mewes, H. W. (2009). TICL–a web tool for network-based interpretation of compound lists inferred by high-throughput metabolomics. FEBS Journal 276(7), 20842094.CrossRefGoogle ScholarPubMed
Arita, M. (2004). The metabolic world of Escherichia coli is not small, Proceedings of the National Academy of Sciences, USA 101(6), 15431547.CrossRefGoogle Scholar
Aziz, R. K., Bartels, D., Best, A. A., DeJongh, M., Disz, T., Edwards, R. A., Formsma, K., Gerdes, S., Glass, E. M., Kubal, M., Meyer, F., Olsen, G. J., Olson, R., Osterman, A. L., Overbeek, R. A., McNeil, L. K., Paarmann, D., Paczian, T., Parrello, B., Pusch, G. D., Reich, C., Stevens, R., Vassieva, O., Vonstein, V., Wilke, A. and Zagnitko, O. (2008). The RAST Server: rapid annotations using subsystems technology. BMC Genomics 9, 75.CrossRefGoogle ScholarPubMed
Bairoch, A. (2000). The ENZYME database in 2000. Nucleic Acids Research 28(1), 304305.CrossRefGoogle ScholarPubMed
Barabási, A. and Oltvai, Z. N. (2004). Network biology: understanding the cells functional organization. Nature Reviews Genetics 5(2), 101113.CrossRefGoogle ScholarPubMed
Blum, T. and Kohlbacher, O. (2008). MetaRoute: fast search for relevant metabolic routes for interactive network navigation and visualization. Bioinformatics 24, 21082109.CrossRefGoogle ScholarPubMed
Borenstein, E., Kupiec, M., Feldman, M. W. and Ruppin, E. (2008). Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proceedings of the National Academy of Sciences, USA 105(38), 1448214487.CrossRefGoogle ScholarPubMed
Bourqui, R., Cottret, L., Lacroix, V., Auber, D., Mary, P., Sagot, M. and Jourdan, F. (2007). Metabolic network visualization eliminating node redundance and preserving metabolic pathways. BMC Systems Biology 1, 29.CrossRefGoogle ScholarPubMed
Breitling, R., Pitt, A. R. and Barrett, M. P. (2006 a). Precision mapping of the metabolome. Trends Biotechnology 24(12), 543548.CrossRefGoogle ScholarPubMed
Breitling, R., Ritchie, S., Goodenowe, D., Stewart, M. L. and Barrett, M. P. (2006 b). Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data, Metabolomics 2(3), 155164.CrossRefGoogle ScholarPubMed
Brohée, S., Faust, K., Lima-Mendez, G., Sand, O., Janky, R., Vanderstocken, G., Deville, Y. and van Helden, J. (2008). NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways, Nucleic Acids Research 36, W444W451.Google Scholar
Casadio, R., Martelli, P. L. and Pierleoni, A. (2008). The prediction of protein subcellular localization from sequence: a shortcut to functional genome annotation. Briefings in Functional Genomics and Proteomics 7(1), 6373.Google Scholar
Caspi, R., Altman, T., Dale, J. M., Dreher, K., Fulcher, C. A., Gilham, F., Kaipa, P., Karthikeyan, A. S., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., Paley, S., Popescu, L., Pujar, A., Shearer, A. G., Zhang, P. and Karp, P. D. (2008). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Research 38, D473D479.CrossRefGoogle Scholar
Chavali, A. K., Whittemore, J. D., Eddy, J. A., Williams, K. T. and Papin, J. A. (2008). Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major. Molecular Systems Biology 4, 177.CrossRefGoogle ScholarPubMed
Chukualim, B., Peters, N., Fowler, C. and Berriman, M. (2008). TrypanoCyc – a metabolic pathway database for Trypanosoma brucei. BMC Bioinformatics 9 (Suppl 10). P5.Google Scholar
Claudel-Renard, C., Chevalet, C., Faraut, T. and Kahn, D. (2003). Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Research 31, 66336639.CrossRefGoogle ScholarPubMed
Cottret, L., Vieira Milreu, P., Acu na, V., Marchetti-Spaccamela, A., Viduani Martinez, F., Sagot, M. and Stougie, L. (2008). Enumerating Precursor Sets of Target Metabolites in a Metabolic Network. WABI ‘08: Proceedings of the 8th international workshop on Algorithms in Bioinformatics, Springer-Verlag, 233244.CrossRefGoogle Scholar
Coustou, V., Biran, M., Breton, M., Guegan, F., Rivière, L., Plazolles, N., Nolan, D., Barrett, M. P., Franconi, J. and Bringaud, F. (2008). Glucose-induced remodeling of intermediary and energy metabolism in procyclic Trypanosoma brucei. Journal of Biological Chemistry 283(24), 1634216354.CrossRefGoogle ScholarPubMed
DeJongh, M., Formsma, K., Boillot, P., Gould, J., Rycenga, M. and Best, A. (2007). Toward the automated generation of genome-scale metabolic networks in the SEED. BMC Bioinformatics 8, 139.CrossRefGoogle ScholarPubMed
Doyle, M. A., MacRae, J. I., Souza, D. P. D., Saunders, E. C., McConville, M. J. and Likić, V. A. (2009). LeishCyc: a biochemical pathways database for Leishmania major. BMC Systems Biology 3, 57.CrossRefGoogle ScholarPubMed
Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., Broadbelt, L. J., Hatzimanikatis, V. and Palsson, B. (2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 mg1655 that accounts for 1260 ORFs and thermodynamic information. Molecular Systems Biology 3, 121.CrossRefGoogle ScholarPubMed
Finney, A. and Hucka, M. (2003). Systems biology markup language: Level 2 and beyond, Biochemical Society Transactions 31, 14721473.Google Scholar
Francke, C., Siezen, R. J. and Teusink, B. (2005). Reconstructing the metabolic network of a bacterium from its genome. Trends in Microbiology 13(11), 550558.CrossRefGoogle ScholarPubMed
de la Fuente, A., Bing, N., Hoeschele, I. and Mendes, P. (2004). Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18), 35653574.CrossRefGoogle ScholarPubMed
Garfinkel, D. (1968). The role of computer simulation in biochemistry. Computers and Biomedical Research 2(1), iii.CrossRefGoogle ScholarPubMed
Ginsburg, H. (2006). Progress in in silico functional genomics: the malaria Metabolic Pathways database. Trends in Parasitology 22(6), 238240.CrossRefGoogle ScholarPubMed
Ginsburg, H. (2009). Caveat emptor: limitations of the automated reconstruction of metabolic pathways in Plasmodium. Trends in Parasitology 25(1), 3743.CrossRefGoogle ScholarPubMed
Green, M. L. and Karp, P. D. (2004). A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinformatics 5, 76.CrossRefGoogle ScholarPubMed
Handorf, T., Ebenhöh, O. and Heinrich, R. (2005). Expanding metabolic networks: scopes of compounds, robustness, and evolution. Journal of Molecular Evolution 61(4), 498512.CrossRefGoogle ScholarPubMed
Handorf, T. and Ebenhöh, O. (2007). MetaPath Online: a web server implementation of the network expansion algorithm. Nucleic Acids Research 35, W613W618.Google Scholar
Handorf, T., Christian, N., Ebenhöh, O. and Kahn, D. (2008). An environmental perspective on metabolism. Journal of Theoretical Biology 252, 530537.Google Scholar
Jourdan, F., Breitling, R., Barrett, M. P. and Gilbert, D. (2008). MetaNetter: inference and visualization of high-resolution metabolomic networks. Bioinformatics 24(1), 143145.CrossRefGoogle ScholarPubMed
Jourdan, F., Cottret, L., Wildridge, D., Scheltema, R., Hillenweck, A., Barrett, M. P., Zalko, D., Watson, D. G. and Debrauwer, L. (2010). Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining. Metabolomics. In Press.CrossRefGoogle ScholarPubMed
Jünger, M. and Mutzel, P., ed. (2004). Graph Drawing Software, Springer.CrossRefGoogle Scholar
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M. and Hirakawa, M. (2010). KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Research 38, D355D360.CrossRefGoogle ScholarPubMed
Karp, P. D., Paley, S. and Romero, P. (2002). The Pathway Tools software, Bioinformatics 18 Suppl 1, 225238.Google Scholar
Kind, T. and Fiehn, O. (2006). Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics 7, 234.CrossRefGoogle Scholar
Kind, T. and Fiehn, O. (2007). Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8, 105.Google Scholar
Kümmel, A., Panke, S. and Heinemann, M. (2006). Systematic assignment of thermodynamic constraints in metabolic network models. BMC Bioinformatics 7, 512.CrossRefGoogle ScholarPubMed
Lacroix, V., Cottret, L., Thébault, P. and Sagot, M. (2008). An introduction to metabolic networks and their structural analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 5, 594617.CrossRefGoogle ScholarPubMed
Matthews, L., Gopinath, G., Gillespie, M., Caudy, M., Croft, D., de Bono, B., Garapati, P., Hemish, J., Hermjakob, H., Jassal, B., Kanapin, A., Lewis, S., Mahajan, S., May, B., Schmidt, E., Vastrik, I., Wu, G., Birney, E., Stein, L. and D'Eustachio, P. (2009). Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Research 37, D619D622.CrossRefGoogle ScholarPubMed
Mintz-Oron, S., Aharoni, A., Ruppin, E. and Shlomi, T. (2009). Network-based prediction of metabolic enzymes subcellular localization. Bioinformatics 25, i247i252.Google Scholar
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C. and Kanehisa, M. (2007). KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Research 35, W182W185.CrossRefGoogle ScholarPubMed
Nikolsky, Y., Nikolskaya, T. and Bugrim, A. (2005). Biological networks and analysis of experimental data in drug discovery, Drug Discovery Today 1(10), 653662.CrossRefGoogle Scholar
Notebaart, R. A., van Enckevort, F. H. J., Francke, C., Siezen, R. J. and Teusink, B. (2006). Accelerating the reconstruction of genome-scale metabolic networks. BMC Bioinformatics 7, 296.CrossRefGoogle ScholarPubMed
Oh, M., Yamada, T., Hattori, M., Goto, S. and Kanehisa, M. (2007). Systematic analysis of enzyme-catalyzed reaction patterns and prediction of microbial biodegradation pathways. Journal of Chemical Information and Modeling 47, 17021712.CrossRefGoogle ScholarPubMed
Palsson, B. O. (2000). The challenges of in silico biology. Nature Biotechnology 18, 11471150.Google Scholar
Pinney, J. W., Papp, B., Hyland, C., Wambua, L., Westhead, D. R. and McConkey, G. A. (2007). Metabolic reconstruction and analysis for parasite genomes. Trends in Parasitology 23, 548554.CrossRefGoogle ScholarPubMed
Pinney, J. W., Shirley, M. W., McConkey, G. A. and Westhead, D. R. (2005). metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Research 33, 13991409.Google Scholar
Rahman, S. A., Advani, P., Schunk, R., Schrader, R. and Schomburg, D. (2005). Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC). Bioinformatics 21, 11891193.Google Scholar
Rahman, S. A. and Schomburg, D. (2006). Observing local and global properties of metabolic pathways: load points and choke points in the metabolic networks. Bioinformatics 22, 17671774.CrossRefGoogle ScholarPubMed
Reed, J. L., Famili, I., Thiele, I. and Palsson, B. O. (2006). Towards multidimensional genome annotation. Nature Reviews Genetics 7, 130141.CrossRefGoogle ScholarPubMed
Roberts, S. B., Robichaux, J. L., Chavali, A. K., Manque, P. A., Lee, V., Lara, A. M., Papin, J. A. and Buck, G. A. (2009). Proteomic and network analysis characterize stage-specific metabolism in Trypanosoma cruzi. BMC Systems Biology 3, 52.CrossRefGoogle ScholarPubMed
Romero, P., Wagg, J., Green, M. L., Kaiser, D., Krummenacker, M. and Karp, P. D. (2005). Computational prediction of human metabolic pathways from the complete human genome. Genome Biology 6, R2.Google Scholar
Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G. and Schomburg, D. (2004). BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Research 32(Database issue), D431D433.Google Scholar
Schuster, S. and Hilgetag, C. (1994). On elementary flux modes in biochemical reaction systems at steady state. Journal of Biological Systems 2, 165182.CrossRefGoogle Scholar
Schwartz, J. and Nacher, J. C. (2009). Local and global modes of drug action in biochemical networks. BMC Chemical Biology 9, 4.Google Scholar
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B. and Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 13, 24982504.Google Scholar
Singh, S., Malik, B. K. and Sharma, D. K. (2007). Choke point analysis of metabolic pathways in E. histolytica: A computational approach for drug target identification. Bioinformation 2, 6872.CrossRefGoogle Scholar
Strömbäck, L. and Lambrix, P. (2005). Representations of molecular pathways: an evaluation of SBML, PSI MI and BioPAX. Bioinformatics 21, 44014407.CrossRefGoogle ScholarPubMed
Suhre, K. and Schmitt-Kopplin, P. (2008). MassTRIX: mass translator into pathways. Nucleic Acids Research 36(Web Server issue), W481W484.Google Scholar
Vial, H. J., Eldin, P., Tielens, A. G. M. and van Hellemond, J. J. (2003). Phospholipids in parasitic protozoa. Molecular and Biochemical Parasitology 126, 143154.CrossRefGoogle ScholarPubMed
Voit, E. O. (2002). Metabolic modeling: a tool of drug discovery in the post-genomic era. Drug Discovery Today 7, 621628.Google Scholar
Whitaker, J. W., Letunic, I., McConkey, G. A. and Westhead, D. R. (2009). metaTIGER: a metabolic evolution resource. Nucleic Acids Research 37(Database issue), D531D538.CrossRefGoogle ScholarPubMed
Wilson, I. D., Plumb, R., Granger, J., Major, H., Williams, R. and Lenz, E. M. (2005). HPLC-MS-based methods for the study of metabonomics. Journal of Chromatography B Analytical Technologies in Biomedical and Life Sciences 817, 6776.Google Scholar
Yeh, I., Hanekamp, T., Tsoka, S., Karp, P. D. and Altman, R. B. (2004). Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Research 14, 917924.CrossRefGoogle ScholarPubMed