Context-Specific Metabolic Networks Are Consistent with Experiments
Figure 1
A flow chart schematic representation of the GIMME algorithm.
The GIMME algorithm takes three inputs: gene expression (or any other data type) mapped to reactions, a metabolic reconstruction, and one or more RMFs. A metabolic reconstruction is mapped through a data set, removing reactions that are not available and creating a reduced model. Reactions are reinserted into the reduced model as needed to achieve RMFs (such as growth and/or ATP production), resulting in a functional, context-specific model that features minimal disagreement with the data. The consistency score quantifies the disagreement with data, showing the minimal sum of fluxes weighted with reaction data deviations from data.