Weighting schemes in metabolic graphs for identifying biochemical routes
Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and othe...
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| Published in | Systems and synthetic biology Vol. 8; no. 1; pp. 47 - 57 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.03.2014
Springer Nature B.V |
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| Abstract | Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes
K
M
S
K
M
P
and
K
M
K
c
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t
fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of
Escherichia coli
stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks. |
|---|---|
| AbstractList | Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes
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\begin{document}$$\left[ {\left( {\frac{{K_{M}^{S} }}{{ K_{M}^{P } }}} \right){\text{ and }}\left( { \frac{{K_{M} }}{{K_{cat} }} } \right)} \right]$$\end{document}
fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of
Escherichia coli
stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks. Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes [Formula: see text] fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks. Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes K M S K M P and K M K c a t fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks. (ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) Issue Title: Special Issue: Perspectives in Systems Biology Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes ... fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks. [PUBLICATION ABSTRACT] |
| Author | Vishveshwara, S. Ghosh, S. Chandra, N. Baloni, P. |
| Author_xml | – sequence: 1 givenname: S. surname: Ghosh fullname: Ghosh, S. organization: I.I.Sc. Mathematics Initiative, Indian Institute of Science – sequence: 2 givenname: P. surname: Baloni fullname: Baloni, P. organization: Department of Biochemistry, Indian Institute of Science – sequence: 3 givenname: S. surname: Vishveshwara fullname: Vishveshwara, S. organization: Molecular Biophysics Unit, Indian Institute of Science – sequence: 4 givenname: N. surname: Chandra fullname: Chandra, N. email: nchandra@biochem.iisc.ernet.in organization: Department of Biochemistry, Indian Institute of Science |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24592291$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1186/1752-0509-3-103 10.1371/journal.pone.0000881 10.1099/mic.0.27481-0 10.1038/msb.2010.18 10.1186/1742-4682-3-42 10.1093/bib/bbl022 10.1016/S1631-0691(03)00117-3 10.1371/journal.pcbi.1003126 10.1007/978-1-61779-361-5_6 10.1074/jbc.R800056200 10.1126/science.292.5518.929 10.1002/bit.260361013 10.1093/nar/gki437 10.1016/j.jmb.2005.09.079 10.1038/nrd3146 10.1093/bioinformatics/18.suppl_1.S233 10.1145/1925041.1925054 10.1093/nar/gkp889 10.1074/mcp.M400110-MCP200 10.1093/nar/gkq1089 10.1101/gr.1239303 10.1016/j.dam.2011.09.019 10.1038/nrc2817 10.1093/nar/27.1.29 10.1145/367766.368168 10.1038/81125 10.1093/bfgp/eln011 |
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| Keywords | Biochemical networks Metabolomics Alternate paths Weighted networks Transcriptomics |
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| Title | Weighting schemes in metabolic graphs for identifying biochemical routes |
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