BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale
Fig 8
The number of samples needed for BayFlux convergence scales approximately linearly with number of reactions in the model.
Shown are the number of samples required to reach convergence across four parallel chains, for three different size models and the fit to a linear model. We define convergence as having at least 80% of reactions with a net flux Gelman-Rubin statistic across 4 parallel chains, and exclude reactions with no sampling variance, e.g. reactions that are fully constrained and have only a single possible flux value (allowing for small amounts of numerical error) [42]. The data used here for the two largest models are the wild type 5 hour data from Toya et. al. [39].