Fig. 1.

The approach followed for metabolic network inference. Three datasets with different variability properties are collected in silico. Each dataset is processed to calculate similarity scores with linear and nonlinear methods (relevance networks). The alternative scores which remove indirect interactions are also applied (conditioned networks). All these networks are fed into pruning algorithm which checks data processing inequality (DPI). See Sect. 2 for details