Volume 9, Issue 2 (2018)                   JMBS 2018, 9(2): 193-199 | Back to browse issues page

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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
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
Abstract:   (11138 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.
Full-Text [PDF 600 kb]   (3061 Downloads)    
Subject: Agricultural Biotechnology
Received: 2016/08/11 | Accepted: 2017/09/8 | Published: 2018/06/21

1. Fargione J, Hill J, Tilman D, Polasky S, Hawthorne P. Land clearing and the biofuel carbon debt. Science. 2008;319(5867):1235-8. [Link] [DOI:10.1126/science.1152747]
2. Ducat DC, Way JC, Silver PA. Engineering cyanobacteria to generate high-value products. Trends Biotechnol. 2011;29(2):95-103. [Link] [DOI:10.1016/j.tibtech.2010.12.003]
3. Machado IM, Atsumi S. Cyanobacterial biofuel production. J Biotechnol. 2012;162(1):50-6. [Link] [DOI:10.1016/j.jbiotec.2012.03.005]
4. Zhou J, Li Y. Engineering cyanobacteria for fuels and chemicals production. Protein Cell. 2010;1(3):207-10. [Link] [DOI:10.1007/s13238-010-0043-9]
5. Gronenberg LS, Marcheschi RJ, Liao JC. Next generation biofuel engineering in prokaryotes. Curr Opin Chem Biol. 2013;17(3):462-71. [Link] [DOI:10.1016/j.cbpa.2013.03.037]
6. Rosgaard L, De Porcellinis AJ, Jacobsen JH, Frigaard NU, Sakuragi Y. Bioengineering of carbon fixation, biofuels, and biochemicals in cyanobacteria and plants. J Biotechnol. 2012;162(1):134-47. [Link] [DOI:10.1016/j.jbiotec.2012.05.006]
7. Hess WR. Cyanobacterial genomics for ecology and biotechnology. Curr Opin Microbiol. 2011;14(5):608-14. [Link] [DOI:10.1016/j.mib.2011.07.024]
8. Erdrich P, Knoop H, Steuer R, Klamt S. Cyanobacterial biofuels: New insights and strain design strategies revealed by computational modeling. Microb Cell Fact. 2014;13:128. [Link] [DOI:10.1186/s12934-014-0128-x]
9. Knoop H, Gründel M, Zilliges Y, Lehmann R, Hoffmann S, Lockau W, et al. Flux balance analysis of cyanobacterial metabolism: The metabolic network of Synechocystis sp. PCC 6803. PLoS Comput Biol. 2013;9(6):e1003081. [Link] [DOI:10.1371/journal.pcbi.1003081]
10. Dutta D, De D, Chaudhuri S, Bhattacharya SK. Hydrogen production by Cyanobacteria. Microb Cell Fact. 2005;4:36. [Link] [DOI:10.1186/1475-2859-4-36]
11. Deng MD, Coleman JR. Ethanol synthesis by genetic engineering in cyanobacteria. Appl Environ Microbiol. 1999;65(2):523-8. [Link]
12. Vidal R, López-Maury L, Guerrero MG, Florencio FJ. Characterization of an alcohol dehydrogenase from the Cyanobacterium Synechocystis sp. strain PCC 6803 that responds to environmental stress conditions via the Hik34-Rre1 two-component system. J Bacteriol. 2009;191(13):4383-91. [Link] [DOI:10.1128/JB.00183-09]
13. Knoop H, Steuer R. A computational analysis of stoichiometric constraints and trade-offs in cyanobacterial biofuel production. Front Bioeng Biotechnol. 2015;3:47. [Link] [DOI:10.3389/fbioe.2015.00047]
14. Shastri AA, Morgan JA. Flux balance analysis of photoautotrophic metabolism. Biotechnol Prog. 2005;21(6):1617-26. [Link] [DOI:10.1021/bp050246d]
15. Knoop H, Zilliges Y, Lockau W, Steuer R. The metabolic network of Synechocystis sp. PCC 6803: Systemic properties of autotrophic growth. Plant Physiol. 2010;154(1):410-22. [Link] [DOI:10.1104/pp.110.157198]
16. Nogales J, Gudmundsson S, Knight EM, Palsson BO, Thiele I. Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. Proc Natl Acad Sci U S A. 2012;109(7):2678-83. [Link] [DOI:10.1073/pnas.1117907109]
17. Ravikrishnan A, Raman K. Critical assessment of genome-scale metabolic networks: The need for a unified standard. Brief Bioinform. 2015;16(6):1057-68. [Link] [DOI:10.1093/bib/bbv003]
18. Lewis NE, Nagarajan H, Palsson BO. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol. 2012;10(4):291-305. [Link] [DOI:10.1038/nrmicro2737]
19. Fu P. Genome-scale modeling of Synechocystis sp. PCC 6803 and prediction of pathway insertion. J Chem Technol Biotechnol. 2009;84(4):473-83. [Link] [DOI:10.1002/jctb.2065]
20. Montagud A, Navarro E, Fernández De Córdoba P, Urchueguía JF, Patil KR. Reconstruction and analysis of genome-scale metabolic model of a photosynthetic bacterium. BMC Syst Biol. 2010;4:156. [Link] [DOI:10.1186/1752-0509-4-156]
21. Yoshikawa K, Kojima Y, Nakajima T, Furusawa C, Hirasawa T, Shimizu H. Reconstruction and verification of a genome-scale metabolic model for Synechocystis sp. PCC6803. Appl Microbiol Biotechnol. 2011;92(2):347-58. [Link] [DOI:10.1007/s00253-011-3559-x]
22. Montagud A, Zelezniak A, Navarro E, De Córdoba PF, Urchueguía JF, Patil KR. Flux coupling and transcriptional regulation within the metabolic network of the photosynthetic bacterium Synechocystis sp. PCC6803. Biotechnol J. 2011;6(3):330-42. [Link] [DOI:10.1002/biot.201000109]
23. Saha R, Verseput AT, Berla BM, Mueller TJ, Pakrasi HB, Maranas CD. Reconstruction and comparison of the metabolic potential of cyanobacteria Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. PLoS One. 2012;7(10):e48285. [Link] [DOI:10.1371/journal.pone.0048285]
24. Burgard AP, Pharkya P, Maranas CD. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng. 2003;84(6):647-57. [Link] [DOI:10.1002/bit.10803]
25. Chindelevitch L, Stanley S, Hung D, Regev A, Berger B. MetaMerge: Scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis. Genome Biol. 2012;13(1):r6. [Link] [DOI:10.1186/gb-2012-13-1-r6]
26. Swainston N, Smallbone K, Mendes P, Kell D, Paton N. The SuBliMinaL Toolbox: Automating steps in the reconstruction of metabolic networks. J Integr Bioinform. 2011;8(2):186. [Link] [DOI:10.1515/jib-2011-186]
27. Vitkin E, Shlomi T. MIRAGE: A functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks. Genome Biol. 2012;13(11):R111. [Link] [DOI:10.1186/gb-2012-13-11-r111]
28. Hädicke O, Klamt S. CASOP: A computational approach for strain optimization aiming at high productivity. J Biotechnol. 2010;147(2):88-101. [Link] [DOI:10.1016/j.jbiotec.2010.03.006]
29. Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, et al. The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics. 2003;19(4):524-31. [Link] [DOI:10.1093/bioinformatics/btg015]
30. Chaouiya C, König M, Le Novère N, Zhang F, editors. The systems biology markup language [Internet]. Tokyo: SBML; 2015 [cited 2016 Jul 5]. Available from: http://sbml.org/Main_Page. [Link]
31. Bairoch A. The ENZYME database in 2000. Nucleic Acids Res. 2000;28(1):304-5. [Link] [DOI:10.1093/nar/28.1.304]
32. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30. [Link] [DOI:10.1093/nar/28.1.27]
33. UniProt Consortium. Ongoing and future developments at the Universal Protein Resource. Nucleic Acids Res. 2011;39(Database issue):D214-9. [Link]
34. Chemical Abstracts Service (CAS). Solve scientific information challenges [Internet]. Ohio: CAS; 2015 [cited 2016 May 25]. Available from: https://www.cas.org/ . [Link]
35. Degtyarenko K, De Matos P, Ennis M, Hastings J, Zbinden M, McNaught A, et al. ChEBI: A database and ontology for chemical entities of biological interest. Nucleic Acids Res. 2008;36(Database issue):D344-50. [Link]
36. Jaro MA. Advances in record-linkage methodology as applied to matching the 1985 census of Tampa, Florida. J Am Stat Assoc. 1989;84(406):414-20. [Link] [DOI:10.1080/01621459.1989.10478785]
37. Winkler W. The state of record linkage and current research problems [Internet]. Maryland: Statistical Research Division, U S Bureau of the Census; 1999 [cited 2016 Feb 11]. Available from: https://www.bibsonomy.org/bibtex/18abfbb3ca7affa51548ca3fd5cc9f85a/sam_chapman. [Link]
38. Orth JD, Thiele I, Palsson BØ. What is flux balance analysis?. Nat Biotechnol. 2010;28(3):245-8. [Link] [DOI:10.1038/nbt.1614]
39. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, et al. Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox v2.0. Nat Protoc. 2011;6:1290-307. [Link] [DOI:10.1038/nprot.2011.308]
40. Thiele I, Palsson BØ. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5(1):93-121. [Link] [DOI:10.1038/nprot.2009.203]
41. Cooley JW, Howitt CA, Vermaas WF. Succinate: Quinol oxidoreductases in the cyanobacterium synechocystis sp. strain PCC 6803: Presence and function in metabolism and electron transport. J Bacteriol. 2000;182(3):714-22. [Link] [DOI:10.1128/JB.182.3.714-722.2000]
42. King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, et al. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 2016;44(D1):D515-22. [Link] [DOI:10.1093/nar/gkv1049]

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