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Quantifying Nitric Oxide Flux Distributions

  • Darshan M. Sivaloganathan
  • Xuanqing Wan
  • Mark P. BrynildsenEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)

Abstract

Nitric oxide (NO) is a radical that is used as an attack molecule by immune cells. NO can interact and damage a range of biomolecules, and the biological outcome for bacteria assaulted with NO will be governed by how the radical distributes within their biochemical reaction networks. Measurement of those NO fluxes is complicated by the low abundance and transience of many of its reaction products. To overcome this challenge, we use computational modeling to translate measurements of several biochemical species (e.g., NO, O2, NO2) into NO flux distributions. In this chapter, we provide a detailed protocol, which includes experimental measurements and computational modeling, to estimate the NO flux distribution in an Escherichia coli culture. Those fluxes will have uncertainty associated with them and we also discuss how further experiments and modeling can be employed for flux refinement.

Key words

Escherichia coli Nitric oxide Metabolic flux Nitric oxide dioxygenase Nitric oxide reductase 

Notes

Acknowledgments

This work was supported by National Science Foundation grant CBET-1453325 (MPB), Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (DMS), and the generosity of Helen Shipley Hunt ∗71 through the Shipley Hunt Fund (MPB). We would like to thank Weng Kang Chou and Annabel S. Lemma for their assistance. The funders had no role in the preparation of the manuscript or decision to publish, and this content is solely the responsibility of the authors and does not necessarily represent the views of the funding agencies.

Supplementary material

465987_1_En_8_MOESM1_ESM.xlsx (15 kb)
Data 1 AlphaModel1 (XLSX 14 kb)
465987_1_En_8_MOESM2_ESM.xlsx (15 kb)
Data 2 AlphaModel2 (XLSX 14 kb)
465987_1_En_8_MOESM3_ESM.xlsx (39 kb)
Data 3 EcoliNOmodel (XLSX 38 kb)
465987_1_En_8_MOESM4_ESM.m (8 kb)
Data 4 FitModelParams (M 8 kb)
465987_1_En_8_MOESM5_ESM.m (11 kb)
Data 5 LoadEcoliNOmodel (M 11 kb)
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Data 6 PathwayFluxEcoli (M 2 kb)
465987_1_En_8_MOESM7_ESM.m (6 kb)
Data 7 RandomInitParamOpt (M 5 kb)
465987_1_En_8_MOESM8_ESM.m (6 kb)
Data 8 RunEcoliNOmodel (M 5 kb)
465987_1_En_8_MOESM9_ESM.docx (549 kb)
Table S1 Optimized model parameter values. Parameter values that yielded the lowest SSR between the experimental and simulated data following the procedures of each section are provided. In Subheading 3.1.2, [O2] curve for cell-free oxygen transport was used for optimization. In Subheading 3.2.1, [NO] curve and terminal [NO2] for MAHMA NONOate dose were used for optimization. In Subheading 3.2.2, [NO] curve for cell-free DPTA NONOate was used for optimization. In Subheading 3.2.3, [O2] curve for wild-type E. coli respiration was used for optimization. In Subheading 3.2.4, [NO] curve for wild-type E. coli under DPTA NONOate treatment was used for optimization (DOCX 410 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Darshan M. Sivaloganathan
    • 1
  • Xuanqing Wan
    • 2
  • Mark P. Brynildsen
    • 2
    Email author
  1. 1.Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonUSA
  2. 2.Department of Chemical and Biological EngineeringPrinceton UniversityPrincetonUSA

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