L1 Norm Minimal Mode-Based Methods for Listing Reaction Network Designs for Metabolite Production
Summary :
Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved. Although simple elementary flux mode (EFM)-based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic flows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it (minL1-FMDL), which works in polynomial time. Computational experiments were conducted for (1) a relatively small network to compare the performance of minL1-FMDL with that of the simple EFM-based method and (2) a genome-scale network to verify the scalability of minL1-FMDL. In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks. The developed software, minL1-FMDL, implemented in MATLAB, is available on https://sunflower.kuicr.kyoto-u.ac.jp/~tamura/software, and can be used for genome-scale metabolic network design for metabolite production.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.5 pp.679-687
- Publication Date
- 2021/05/01
- Publicized
- 2021/02/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7247
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Takeyuki TAMURA
Kyoto University
Keyword
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Takeyuki TAMURA, "L1 Norm Minimal Mode-Based Methods for Listing Reaction Network Designs for Metabolite Production" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 5, pp. 679-687, May 2021, doi: 10.1587/transinf.2020EDP7247.
Abstract: Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved. Although simple elementary flux mode (EFM)-based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic flows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it (minL1-FMDL), which works in polynomial time. Computational experiments were conducted for (1) a relatively small network to compare the performance of minL1-FMDL with that of the simple EFM-based method and (2) a genome-scale network to verify the scalability of minL1-FMDL. In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks. The developed software, minL1-FMDL, implemented in MATLAB, is available on https://sunflower.kuicr.kyoto-u.ac.jp/~tamura/software, and can be used for genome-scale metabolic network design for metabolite production.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7247/_p
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@ARTICLE{e104-d_5_679,
author={Takeyuki TAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={L1 Norm Minimal Mode-Based Methods for Listing Reaction Network Designs for Metabolite Production},
year={2021},
volume={E104-D},
number={5},
pages={679-687},
abstract={Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved. Although simple elementary flux mode (EFM)-based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic flows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it (minL1-FMDL), which works in polynomial time. Computational experiments were conducted for (1) a relatively small network to compare the performance of minL1-FMDL with that of the simple EFM-based method and (2) a genome-scale network to verify the scalability of minL1-FMDL. In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks. The developed software, minL1-FMDL, implemented in MATLAB, is available on https://sunflower.kuicr.kyoto-u.ac.jp/~tamura/software, and can be used for genome-scale metabolic network design for metabolite production.},
keywords={},
doi={10.1587/transinf.2020EDP7247},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - L1 Norm Minimal Mode-Based Methods for Listing Reaction Network Designs for Metabolite Production
T2 - IEICE TRANSACTIONS on Information
SP - 679
EP - 687
AU - Takeyuki TAMURA
PY - 2021
DO - 10.1587/transinf.2020EDP7247
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2021
AB - Metabolic networks represent the relationship between chemical reactions and compounds in cells. In useful metabolite production using microorganisms, it is often required to calculate reaction deletion strategies from the original network to result in growth coupling, which means the target metabolite production and cell growth are simultaneously achieved. Although simple elementary flux mode (EFM)-based methods are useful for listing such reaction deletions strategies, the number of cases to be considered is often proportional to the exponential function of the size of the network. Therefore, it is desirable to develop methods of narrowing down the number of reaction deletion strategy candidates. In this study, the author introduces the idea of L1 norm minimal modes to consider metabolic flows whose L1 norms are minimal to satisfy certain criteria on growth and production, and developed a fast metabolic design listing algorithm based on it (minL1-FMDL), which works in polynomial time. Computational experiments were conducted for (1) a relatively small network to compare the performance of minL1-FMDL with that of the simple EFM-based method and (2) a genome-scale network to verify the scalability of minL1-FMDL. In the computational experiments, it was seen that the average value of the target metabolite production rates of minL1-FMDL was higher than that of the simple EFM-based method, and the computation time of minL1-FMDL was fast enough even for genome-scale networks. The developed software, minL1-FMDL, implemented in MATLAB, is available on https://sunflower.kuicr.kyoto-u.ac.jp/~tamura/software, and can be used for genome-scale metabolic network design for metabolite production.
ER -
English
