Volume 9, Issue 2 (2018)
JMBS 2018, 9(2): 193-199 |
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Mohammadi R, Zahiri J, Niroomand M. Reconstruction and Modeling of Integrated Metabolic Network of a Cyanobacterium to Increase the Production of Biofuels. JMBS 2018; 9 (2) :193-199
URL: http://biot.modares.ac.ir/article-22-24444-en.html
URL: http://biot.modares.ac.ir/article-22-24444-en.html
1- Bioscience & Biotechnology Department, Malek Ashtar University of Technology, Tehran, Iran, Malek Ashtar University of Technology, Shabanloo Street, Shahi Babaei Highway, Tehran, Iran , rezamohammadi@mut.ac.ir
2- Biophysics Department, Biological Science Faculty, Tarbiat Modares University, Tehran, Iran
3- Computer Engineering Department, Electrical & Computer Engineering Faculty, University of Tehran, Tehran, Iran
2- Biophysics Department, Biological Science Faculty, Tarbiat Modares University, Tehran, Iran
3- Computer Engineering Department, Electrical & Computer Engineering Faculty, University of Tehran, Tehran, Iran
Abstract: (10987 Views)
Aims: The production of biofuels has been one of the promising efforts in biotechnology in the past decades. Unicellular cyanobacteria are widespread phototrophic microorganisms that can be suitable chassis for production of valuable organic materials like biofuels. The aim of this study was the reconstruction and modeling of integrated metabolic network of a cyanobacterium to increase the production of biofuels.
Materials and Methods: In the present computational study, a software for integrating reconstructed metabolic networks was developed to optimize and increase their efficiency and was named as iMet. First, iMet was used to integrate the 3 pre-reconstructed metabolic networks of Synechocystis PCC6803. In the next step, the reconstructed network was modeled to produce 4 types of biofuels, including ethanol, propanol, butanol, and isobutanol.
Findings: The new merged model had 808 reactions and 560 metabolites. The amount of flux or flow in the integrated model was calculated to be 0.0295 hours per hour. This showed a remarkable increase compared to the previous three models. The cells were divided once every 24 hours. The amount of flux of 4 types of alcohol and their maximum theoretical efficiency increased in the integrated model compared to the previous 3 models. The flux of ethanol production was greater in all models than flux of 3 other alcohols, and the ethanol production reactions were closer to the flow or the central flux of carbon.
Conclusion: The analyses of flow equilibrium in the metabolic network coverage show an increase in the production of biofuels and a decrease in the number of blocked reactions in the new model, thereby the efficiency of the developed iMet software is proved.
Materials and Methods: In the present computational study, a software for integrating reconstructed metabolic networks was developed to optimize and increase their efficiency and was named as iMet. First, iMet was used to integrate the 3 pre-reconstructed metabolic networks of Synechocystis PCC6803. In the next step, the reconstructed network was modeled to produce 4 types of biofuels, including ethanol, propanol, butanol, and isobutanol.
Findings: The new merged model had 808 reactions and 560 metabolites. The amount of flux or flow in the integrated model was calculated to be 0.0295 hours per hour. This showed a remarkable increase compared to the previous three models. The cells were divided once every 24 hours. The amount of flux of 4 types of alcohol and their maximum theoretical efficiency increased in the integrated model compared to the previous 3 models. The flux of ethanol production was greater in all models than flux of 3 other alcohols, and the ethanol production reactions were closer to the flow or the central flux of carbon.
Conclusion: The analyses of flow equilibrium in the metabolic network coverage show an increase in the production of biofuels and a decrease in the number of blocked reactions in the new model, thereby the efficiency of the developed iMet software is proved.
Subject:
Agricultural Biotechnology
Received: 2016/08/11 | Accepted: 2017/09/8 | Published: 2018/06/21
Received: 2016/08/11 | Accepted: 2017/09/8 | Published: 2018/06/21
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