Method
RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations
1 Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
2 DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
Genome Biology 2012, 13:R78 doi:10.1186/gb-2012-13-9-r78
Published: 26 September 2012Additional files
Additional File 1:
Supplementary Materials and methods.
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Additional File 2:
Supplementary Figure S1. Sensitivity analysis of parameters using E. coli knockout strains (Δpgi, Δppc, and Δtpi) before and after adaptive evolution.
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Additional File 3:
Supplementary Table S1. Comparison of predicted and experimentally measured values of growth, substrate uptake, and product secretion rates for four E. coli mutants before adaptive evolution.
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Additional File 4:
Supplementary Table S2. Comparison of predicted and experimentally measured values of growth, substrate uptake, and product secretion rates for four E. coli mutants after adaptive evolution.
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Additional File 5:
Supplementary Figure S2. Sensitivity analysis of data used to generate reference flux distributions.
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Additional File 6:
Supplementary Figure S3. Sensitivity analysis of metabolic network models.
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Additional File 7:
Supplementary Table S3. Growth rate predictions for 22 single knockout E. coli mutants.
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Additional File 8:
Supplementary Table S4. Growth phenotype predictions for 1,260 single knockout E. coli mutants.
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Additional File 9:
Supplementary Table S5. RELATCH prediction errors (SSE) for E. coli, S. cerevisiae, and B. subtilis knockout mutants shown in Figure 4.
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Additional File 10:
Supplementary Figure S4. Comparison of metabolic flux predictions using RELATCH and MOMA for knockout mutants of S. cerevisiae and B. subtilis.
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Additional File 11:
Supplementary Figure S5. Comparison of MFA estimated fluxes and predictions by RELATCH for E. coli strains grown on galactose.
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Additional File 12:
Implementation of RELATCH. RELATCH is implemented using the COBRA Toolbox for MATLAB.
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