Quantifying the Propagation of Parametric Uncertainty on Flux Balance Analysis

Themes: Conversion

Keywords: Metabolomics, Modeling


Dinh, H.V., Sarkar, D., Maranas, C.D. Oct. 27, 2021. Data from: “Quantifying the Propagation of Parametric Uncertainty on Flux Balance Analysis.” GitHub Repository.


Overview of the method to inject and quantify propagated uncertainty from biomass coefficients and ATP maintenance rates to FBA predictions. First, normally distributed uncertainty is injected to biomass precursor coefficients and ATP maintenance parameters. The biomass reaction is reassembled while accounting for ATP hydrolysis balance and biomass MW. FBA is next carried out using the new biomass reaction and parameter SDR Output/Input quantifies the extent of uncertainty propagation.

In the repository are example scripts that perform uncertainty injection and propagation to flux balance analysis with outputs for a small sample size (for demonstration purpose only). For proper analysis, user should download the scripts and run for a large sample size (e.g., 10,000 samples).

If you use the scripts, please cite the following Metabolic Engineering article: “Quantifying the propagation of parametric uncertainty on flux balance analysis” (https://doi.org/10.1016/j.ymben.2021.10.012)

There are two subdirectories:

  1. /uncFBA/uncBiom: injection of normally distributed noise to biomass precursor coeffcients and ATP maintenance (growth-associated ATP maintenance (GAM) and non-growth associated ATP maintenance (NGAM))
  2. /uncFBA/uncRHS: departure from steady-state by adding noise drawn from normal distribution to the RHS terms of mass balance constraints


GitHub Repository – Includes model scripts.

Download (495.7KB) includes:

  • Co-factor fluxes
  • SDR values
  • Alternative metabolic pathways

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