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Open Access Highly Accessed Research article

Redox balance is key to explaining full vs. partial switching to low-yield metabolism

Milan JA van Hoek123 and Roeland MH Merks1234*

Author Affiliations

1 Centrum Wiskunde & Informatica, Life Sciences, Science Park 123, 1098 XG Amsterdam, The Netherlands

2 Netherlands Institute for Systems Biology, Science Park 123, 1098 XG Amsterdam, The Netherlands

3 Netherlands Consortium for Systems Biology, Amsterdam, The Netherlands

4 Mathematical Institute, Leiden University, P.O. Box 9512, 2300 RA Leiden, The Netherlands

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BMC Systems Biology 2012, 6:22  doi:10.1186/1752-0509-6-22

Published: 24 March 2012

Additional files

Additional file 1:

Figure S1. Histogram of decrease in oxygen uptake for E. coli, when acetate excretion is allowed (black) and knocked out (red). When acetate excretion is knocked out, there are more simulations that become fully high-yield, but also more that stop consuming oxygen.

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Additional file 2:

Table S1. Table describing the summary of fitting Vprot to experimental growth rate and glucose uptake rate. For every organism, we varied Vprot (volume fraction of macromolecules devoted to metabolic enzymes) between 0 and 1 and performed, for each value of Vprot, 1000 simulations with random sets of crowding coefficients. For the simulations described in this paper, we used the value of Vprot that minimized ((μmax, fit - μmax, obs)/μmax, obs)2+((Gupmax, fit - Gupmax, obs)/Gupmax, obs)2. Here, μmax, fit, μmax, obs are the fitted and observed maximal growth rate and Gupmax, fit, Gupmax, obs are the fitter and observed maximal glucose uptake rate. In this table, Pineff indicates the fraction of the 1000 simulations that exhibits low-yield metabolism, which was defined as having a growth yield < 0.3 gr/gr glucose. Experimental data is from Hoek et al. [29]; Thomas et al. [4]; Varma and Palsson [2].

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Additional file 3:

Figure S2. Excel file with turnover numbers and enzyme masses used to calculate the crowding coefficients.

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Additional file 4:

Figure S3. Histograms of turnover numbers (1/s) (A) and crowding coefficients (gram DW hr/mmol) (B) of E. coli. A. All turnover numbers of E. coli in BRENDA (Chang et al. [28]); B. Crowding coefficients resulting from all turnover numbers of E. coli in BRENDA (Chang et al. [28]); C. Turnover numbers of E. coli used for the simulations; D. Crowding coefficients of E. coli used in the simulations; E. Turnover numbers as used in Vazquez et al. [17]; F. Crowding coefficients as used in Vazquez et al. [17].

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Additional file 5:

Figure S4. Histograms of turnover numbers (1/s) (A,C) and crowding coefficients (gram DW hr/mmol) (B,D) of S. cerevisiae. A. All turnover numbers of S. cerevisiae in BRENDA (Chang et al. [28]); B. Crowding coefficients resulting from all turnover numbers of S. cerevisiae in BRENDA (Chang et al. [28]); C. Turnover numbers of S. cerevisiae used for the simulations; D. Crowding coefficients of S. cerevisiae used in the simulations.

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Additional file 6:

Table S2. Excel file describing, for every reaction, the lower and upper bounds used in the simulations.

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Additional file 7:

Mini-website with Matlab code and instructions for reproducing the simulations.

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