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The plots of marginal possibility can also detect multiple flux vectors with maximum possibility (possibility distribution functions with flat top).
Moreover, when multiple flux vectors might be reasonably possible, the marginal and conditional possibility distributions for each flux can be computed.
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For instance, in metabolic flux analysis, each admissible flux vector can be expressed as a non-negative linear combination of a small number of elementary flux vectors.
The second screen entails determining whether the analytes provide unique flux values or multiple flux solutions.
In the following, flux vectors satisfying these conditions will be referred to as feasible stead-state flux distributions.
However, in these methods one makes an arbitrary choice of the flux vector out of many alternative flux vectors satisfying the constraints.
First, two flux vectors were chosen randomly, one from each of the two samples to be compared and the difference between the flux vectors was computed.
Then we take convex combination of these basis vectors to generate the flux vectors that satisfy certain inequality constraints.
This cone contains all self-maintenance flux vectors as described in the self-maintenance definition.
The method, first of all, generates the possible flux vectors in the pathway.
The flux vectors along with their corresponding weighting factors are used to determine z.
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