Generally, FBA predicts metabolic fluxes based on maximization of objective functions (typically the specific growth rate or the target metabolite production rate) (Feist and Palsson 2010; Sanchez et al. 2012), and the basic framework of FBA is composed of variables, objectives, and constraints.
Possibilistic MFA also enables estimating the metabolic fluxes based on the model and the available measurements.
Classical C metabolic flux analysis (MFA) evaluated steady state metabolic fluxes based on isotopomer fractions measured under the conditions of isotopic steady state [ 7].
Methods of metabolic flux analysis seeking to calculate internal metabolic fluxes based on measurements of extracellular fluxes or/and on data from isotopic tracer experiments are able to resolve metabolic fluxes for smaller (core) networks only.
The OptORF approach could be improved by applying these linear approximations in the regulatory part of the model, in order to describe varying gene expression levels, and using approaches to constrain metabolic fluxes based on predicted gene expression levels [ 42- 44].
The goal of this study was to analyze the regulation of plant cell wall precursor metabolism using metabolic flux analysis based on dynamic labeling experiments.
We emphasize that metabolic flux analysis based on material balance is a critical method for quantitative investigations into cell metabolism.
Analytical expressions were derived for calculating the sensitivities of isotopomer distribution vectors, the weighted output matrix with respect to the fluxes, and the covariance matrix for the metabolic flux analysis based on isotopomers mapping matrices (IMM).
Meanwhile, under a pseudo-steady-state metabolic model, FBA determines metabolic flux distribution based on the assumption that organisms could endeavor to optimize growth or production.
In this contribution, a novel systematic methodology for dynamic metabolic flux analysis, based on B-spline parameterizations, has been presented.
