Display options
Cite
MacGillivray M, Ko A, Gruber E, et al. Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Sci Rep. 2017;7(1):268doi: 10.1038/s41598-017-00170-3.
MacGillivray, M., Ko, A., Gruber, E., Sawyer, M., Almaas, E., & Holder, A. (2017). Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Scientific reports, 7(1), 268. https://doi.org/10.1038/s41598-017-00170-3
MacGillivray, Michael, et al. "Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways." Scientific reports vol. 7,1 (2017): 268. doi: https://doi.org/10.1038/s41598-017-00170-3
MacGillivray M, Ko A, Gruber E, Sawyer M, Almaas E, Holder A. Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Sci Rep. 2017 Mar 21;7(1):268. doi: 10.1038/s41598-017-00170-3. PMID: 28325918; PMCID: PMC5427939.
Copy Download .nbib Format: NLM
Share it on

Sci Rep. 2017 Mar 21;7(1):268. doi: 10.1038/s41598-017-00170-3.

Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways.

Scientific reports

Michael MacGillivray, Amy Ko, Emily Gruber, Miranda Sawyer, Eivind Almaas, Allen Holder

Affiliations

  1. Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA.
  2. Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. [email protected].
  3. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway. [email protected].
  4. Department of Mathematics, Rose-Hulman Institute of Technology, Terre Haute, IN, USA. [email protected].

PMID: 28325918 PMCID: PMC5427939 DOI: 10.1038/s41598-017-00170-3

Abstract

Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming (i) equilibrium of a linear system of ordinary differential equations, and (ii) deterministic data. However, the steady-state assumption is biologically imperfect, and several key stoichiometric coefficients are experimentally inferred from situations of inherent variation. We propose an approach that explicitly acknowledges heterogeneity and conducts a robust analysis of metabolic pathways (RAMP). The basic assumption of steady state is relaxed, and we model the innate heterogeneity of cells probabilistically. Our mathematical study of the stochastic problem shows that FBA is a limiting case of our RAMP method. Moreover, RAMP has the properties that: A) metabolic states are (Lipschitz) continuous with regards to the probabilistic modeling parameters, B) convergent metabolic states are solutions to the deterministic FBA paradigm as the stochastic elements dissipate, and C) RAMP can identify biologically tolerable diversity of a metabolic network in an optimized culture. We benchmark RAMP against traditional FBA on genome-scale metabolic reconstructed models of E. coli, calculating essential genes and comparing with experimental flux data.

References

  1. J Theor Biol. 2001 Nov 7;213(1):73-88 - PubMed
  2. Nature. 2002 Nov 14;420(6912):190-3 - PubMed
  3. Curr Opin Biotechnol. 2015 Dec;36:176-82 - PubMed
  4. Genome Biol. 2003;4(9):R54 - PubMed
  5. Mol Syst Biol. 2011 Oct 11;7:535 - PubMed
  6. Appl Environ Microbiol. 2006 Feb;72(2):1558-68 - PubMed
  7. Mol Syst Biol. 2007;3:149 - PubMed
  8. Front Bioeng Biotechnol. 2015 Jan 05;2:76 - PubMed
  9. PLoS Comput Biol. 2012;8(7):e1002575 - PubMed
  10. Proc Natl Acad Sci U S A. 2007 May 8;104(19):7797-802 - PubMed
  11. Trends Microbiol. 2005 Oct;13(10):459-62 - PubMed
  12. Nat Rev Microbiol. 2006 Apr;4(4):259-71 - PubMed
  13. Nat Genet. 2004 Oct;36(10):1056-8 - PubMed
  14. Science. 2012 May 4;336(6081):601-4 - PubMed
  15. Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15112-7 - PubMed
  16. Nat Rev Genet. 2014 Feb;15(2):107-20 - PubMed
  17. Mol Syst Biol. 2007;3:121 - PubMed
  18. PLoS Comput Biol. 2008 May 16;4(5):e1000082 - PubMed
  19. Biotechnol Bioeng. 1993 Jun 5;42(1):59-73 - PubMed
  20. Biotechnol Bioeng. 2003 Dec 20;84(6):647-57 - PubMed
  21. Nature. 2002 Nov 14;420(6912):186-9 - PubMed
  22. Mol Syst Biol. 2014 Aug 01;10:744 - PubMed
  23. Nat Rev Microbiol. 2012 Feb 27;10(4):291-305 - PubMed
  24. Nat Protoc. 2011 Aug 04;6(9):1290-307 - PubMed
  25. Biotechnol Prog. 2001 Sep-Oct;17(5):791-7 - PubMed
  26. Phys Med Biol. 2005 Dec 7;50(23):5463-77 - PubMed
  27. BMC Syst Biol. 2012 Dec 06;6:150 - PubMed
  28. Mol Syst Biol. 2007;3:119 - PubMed
  29. Nat Chem Biol. 2011 May 22;7(7):445-52 - PubMed
  30. Mol Syst Biol. 2015 Oct 14;11(10):830 - PubMed
  31. J Bacteriol. 2005 May;187(9):3171-9 - PubMed
  32. PLoS Comput Biol. 2005 Dec;1(7):e68 - PubMed
  33. Nature. 2005 Jul 28;436(7050):588-92 - PubMed
  34. Proc Natl Acad Sci U S A. 2007 Jul 31;104(31):12663-8 - PubMed
  35. Nat Commun. 2014 Oct 07;5:4893 - PubMed
  36. Nature. 2004 Feb 26;427(6977):839-43 - PubMed
  37. Nat Protoc. 2010 Jan;5(1):93-121 - PubMed
  38. Appl Environ Microbiol. 2006 Feb;72(2):1164-72 - PubMed

MeSH terms

Publication Types