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Yet more elaborate flux balance analysis methods sample flux states that are feasible under steady-state constraints.
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The first method was the one in which sample injection fluxes were variable with constant background flux, while the second was the one in which the background fluxes were flexible with stable sample flux.
Possible sources of error in the current method include unwanted bias in the sampled flux space, possible inadequacy of the Z-score as a statistical metric over more sophisticated tests such as the Kolmogorov-Smirnov test, and inadequacy of the distribution median as a threshold for use in hi-lo hypotheses.
Ensemble modeling proved to be a feasible method to separate samples with flux parameters that could be estimated from samples with poor information on the fluxes in the system.
These include methods like flux balance analysis, FBA, [ 7] and elementary flux mode analysis, EFMA [ 8, 9].
In order to solve this problem we use a hybrid method combining discrete sampling of flux values, inversion of the smooth flux-concentration relation, and a final smooth local optimization.
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.
Most of these applications utilize optimization-based methods such as flux balance analysis (FBA) to explore the metabolic flux space.
Before the sampling was performed, the effective constraints for each reaction were calculated using the method of Flux Balance Analysis in COBRA toolbox [ 50].
Both E-Flux and PROM are extensions of a method called flux balance analysis (FBA) [ 59].
To complement the rank of the flux system, we use the proposed method of flux estimation.
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