IEICE TRANSACTIONS on Information
An Efficient Method of Computing Impact Degrees for Multiple Reactions in Metabolic Networks with Cycles
Summary :
The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.12 pp.2393-2399
- Publication Date
- 2011/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.2393
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
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Takeyuki TAMURA, Yang CONG, Tatsuya AKUTSU, Wai-Ki CHING, "An Efficient Method of Computing Impact Degrees for Multiple Reactions in Metabolic Networks with Cycles" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 12, pp. 2393-2399, December 2011, doi: 10.1587/transinf.E94.D.2393.
Abstract: The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.2393/_p
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@ARTICLE{e94-d_12_2393,
author={Takeyuki TAMURA, Yang CONG, Tatsuya AKUTSU, Wai-Ki CHING, },
journal={IEICE TRANSACTIONS on Information},
title={An Efficient Method of Computing Impact Degrees for Multiple Reactions in Metabolic Networks with Cycles},
year={2011},
volume={E94-D},
number={12},
pages={2393-2399},
abstract={The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions.},
keywords={},
doi={10.1587/transinf.E94.D.2393},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - An Efficient Method of Computing Impact Degrees for Multiple Reactions in Metabolic Networks with Cycles
T2 - IEICE TRANSACTIONS on Information
SP - 2393
EP - 2399
AU - Takeyuki TAMURA
AU - Yang CONG
AU - Tatsuya AKUTSU
AU - Wai-Ki CHING
PY - 2011
DO - 10.1587/transinf.E94.D.2393
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2011
AB - The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions.
ER -
English
