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Currently, for metabolic flux analysis, MFAPipe models the independent fluxes of the reaction network as the independent variables of the regression. Hence, the values of the independent fluxes are fitted to the model-specified isotopic labeling states of the moieties in the reaction network.
A useful enhancement would be to model the labeling percentages of the individual atoms of each isotopically-labeled substrate as independent variables. In principal, this would enable MFAPipe to fit the values of the labeling percentages to the user-specified isotopic labeling state of the moieties in the reaction network.
Potential applications for this proposed enhancement include:
Dijkstra, P., Dalder, J. J., Selmants, P. C., Hart, S. C., Koch, G. W., Schwartz, E., & Hungate, B. A. (2011). Modeling soil metabolic processes using isotopologue pairs of position-specific 13C-labeled glucose and pyruvate. Soil Biology and Biochemistry, 43(9), 1848–1857. https://doi.org/10.1016/j.soilbio.2011.05.001
The text was updated successfully, but these errors were encountered:
Specifically, Paul is interested in determining if adding Entner-Doudoroff pathways would be helpful in explaining the observed CO2 labeling ratios. In some sense, this is a kind of 13C-MFA version of gap-filling.
In which case, it may also be useful to analyze the null spaces and elementary flux modes of the original stoichiometry matrix for the reaction network, which I'm assuming includes reactions for glycolysis, and the modified stoichiometry matrix for the reaction network with the Entner-Doudoroff pathway. This would require only elementary linear algebra.
Since a steady state flux map is a linear combination of the bases of the null space of the stoichiometry matrix, adding the Entner-Doudoroff pathway should, in principal, induce a detectable change in the null space. The flux ratios of the extracellular metabolites at steady-state, calculated, for example, from the null space, may provide a qualitative explanation for the observed CO2 labeling ratios.
Currently, for metabolic flux analysis, MFAPipe models the independent fluxes of the reaction network as the independent variables of the regression. Hence, the values of the independent fluxes are fitted to the model-specified isotopic labeling states of the moieties in the reaction network.
A useful enhancement would be to model the labeling percentages of the individual atoms of each isotopically-labeled substrate as independent variables. In principal, this would enable MFAPipe to fit the values of the labeling percentages to the user-specified isotopic labeling state of the moieties in the reaction network.
Potential applications for this proposed enhancement include:
The text was updated successfully, but these errors were encountered: