Display options
Cite
García-Jiménez B, García JL, Nogales J. FLYCOP: metabolic modeling-based analysis and engineering microbial communities. Bioinformatics. 2018;34(17):i954-i963doi: 10.1093/bioinformatics/bty561.
García-Jiménez, B., García, J. L., & Nogales, J. (2018). FLYCOP: metabolic modeling-based analysis and engineering microbial communities. Bioinformatics (Oxford, England), 34(17), i954-i963. https://doi.org/10.1093/bioinformatics/bty561
García-Jiménez, Beatriz, et al. "FLYCOP: metabolic modeling-based analysis and engineering microbial communities." Bioinformatics (Oxford, England) vol. 34,17 (2018): i954-i963. doi: https://doi.org/10.1093/bioinformatics/bty561
García-Jiménez B, García JL, Nogales J. FLYCOP: metabolic modeling-based analysis and engineering microbial communities. Bioinformatics. 2018 Sep 01;34(17):i954-i963. doi: 10.1093/bioinformatics/bty561. PMID: 30423096; PMCID: PMC6129290.
Copy Download .nbib Format: NLM
Share it on

Bioinformatics. 2018 Sep 01;34(17):i954-i963. doi: 10.1093/bioinformatics/bty561.

FLYCOP: metabolic modeling-based analysis and engineering microbial communities.

Bioinformatics (Oxford, England)

Beatriz García-Jiménez, José Luis García, Juan Nogales

Affiliations

  1. Department of Systems Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), 28049 Madrid, Spain.
  2. Microbial and Plant Biotechnology Department, Centro de Investigaciones Biológicas (CIB-CSIC), 28040 Madrid, Spain.
  3. Applied System Biology and Synthetic Biology Department, Institute for Integrative Systems Biology (I2Sysbio-CSIC-UV), 46980 Paterna, Spain.

PMID: 30423096 PMCID: PMC6129290 DOI: 10.1093/bioinformatics/bty561

Abstract

MOTIVATION: Synthetic microbial communities begin to be considered as promising multicellular biocatalysts having a large potential to replace engineered single strains in biotechnology applications, in pharmaceutical, chemical and living architecture sectors. In contrast to single strain engineering, the effective and high-throughput analysis and engineering of microbial consortia face the lack of knowledge, tools and well-defined workflows. This manuscript contributes to fill this important gap with a framework, called FLYCOP (FLexible sYnthetic Consortium OPtimization), which contributes to microbial consortia modeling and engineering, while improving the knowledge about how these communities work. FLYCOP selects the best consortium configuration to optimize a given goal, among multiple and diverse configurations, in a flexible way, taking temporal changes in metabolite concentrations into account.

RESULTS: In contrast to previous systems optimizing microbial consortia, FLYCOP has novel characteristics to face up to new problems, to represent additional features and to analyze events influencing the consortia behavior. In this manuscript, FLYCOP optimizes a Synechococcus elongatus-Pseudomonas putida consortium to produce the maximum amount of bio-plastic (PHA, polyhydroxyalkanoate), and highlights the influence of metabolites exchange dynamics in a four auxotrophic Escherichia coli consortium with parallel growth. FLYCOP can also provide an explanation about biological evolution driving evolutionary engineering endeavors by describing why and how heterogeneous populations emerge from monoclonal ones.

AVAILABILITY AND IMPLEMENTATION: Code reproducing the study cases described in this manuscript are available on-line: https://github.com/beatrizgj/FLYCOP.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

References

  1. Am Nat. 2000 Jan;155(1):24-35 - PubMed
  2. Nature. 2009 Oct 29;461(7268):1243-7 - PubMed
  3. Nature. 2017 Nov 2;551(7678):45-50 - PubMed
  4. Mol Syst Biol. 2011 Oct 11;7:535 - PubMed
  5. Environ Microbiol. 2016 Feb;18(2):341-57 - PubMed
  6. Metab Eng. 2016 Sep;37:114-121 - PubMed
  7. J Bacteriol. 2007 Jul;189(14):5142-52 - PubMed
  8. PLoS Comput Biol. 2017 May 22;13(5):e1005544 - PubMed
  9. Trends Biotechnol. 2008 Sep;26(9):483-9 - PubMed
  10. Front Microbiol. 2017 Nov 27;8:2299 - PubMed
  11. Microb Biotechnol. 2016 Sep;9(5):564-7 - PubMed
  12. Appl Environ Microbiol. 2012 Apr;78(8):2660-8 - PubMed
  13. Biotechnol Biofuels. 2016 Jan 22;9:17 - PubMed
  14. BMC Syst Biol. 2013 Aug 08;7:74 - PubMed
  15. Science. 2014 Mar 21;343(6177):1366-9 - PubMed
  16. Front Microbiol. 2016 May 18;7:673 - PubMed
  17. Curr Opin Biotechnol. 2017 Oct;47:67-82 - PubMed
  18. Nat Rev Genet. 2014 Feb;15(2):107-20 - PubMed
  19. Mol Syst Biol. 2007;3:121 - PubMed
  20. J R Soc Interface. 2016 Nov;13(124): - PubMed
  21. Curr Opin Biotechnol. 2017 Jun;45:85-91 - PubMed
  22. ACS Synth Biol. 2014 Apr 18;3(4):247-57 - PubMed
  23. J Theor Biol. 2008 Jun 7;252(3):497-504 - PubMed
  24. Biotechnol J. 2017 Mar;12(3): - PubMed
  25. Nat Protoc. 2011 Aug 04;6(9):1290-307 - PubMed
  26. Cell Rep. 2014 May 22;7(4):1104-15 - PubMed
  27. PLoS Comput Biol. 2017 May 15;13(5):e1005539 - PubMed
  28. BMC Evol Biol. 2016 Aug 20;16(1):163 - PubMed
  29. Sci Rep. 2016 Jul 04;6:29182 - PubMed
  30. Proc Natl Acad Sci U S A. 2012 Jun 12;109(24):9487-92 - PubMed
  31. Nat Rev Genet. 2010 May;11(5):367-79 - PubMed
  32. Microb Biotechnol. 2016 Sep;9(5):610-7 - PubMed
  33. Proc Natl Acad Sci U S A. 2016 Dec 20;113(51):E8344-E8353 - PubMed
  34. ACS Synth Biol. 2018 Apr 20;7(4):1163-1166 - PubMed
  35. Front Plant Sci. 2016 Sep 26;7:1421 - PubMed
  36. Elife. 2015 Oct 16;4:e08208 - PubMed
  37. Front Microbiol. 2017 May 10;8:827 - PubMed
  38. Environ Microbiol. 2017 Aug;19(8):2949-2963 - PubMed
  39. Bioinformatics. 2016 Jul 1;32(13):2008-16 - PubMed
  40. Biotechnol Adv. 2017 Nov 15;35(7):845-866 - PubMed
  41. J Bacteriol. 2012 Nov;194(21):5897-908 - PubMed
  42. Genome Biol. 2016 May 23;17(1):109 - PubMed
  43. Am Nat. 2017 Aug;190(S1):S57-S68 - PubMed

MeSH terms

Publication Types