Figure 5. MinSpan pathways help predict transcription factor activity.

1. Constraint-based models can determine reaction activity, or flux states (v), using Monte Carlo sampling. Decomposing sampled flux states into linear weightings of MinSpan pathways (α) allows prediction of TF activity. For example, metabolic reaction fluxes are sampled under glucose minimal media and glucose minimal media + l-arginine supplementation. Typical analysis would yield a list of reactions (including v_i) that are significantly changed. With MinSpan pathways, the flux distributions can be converted into significant changes in pathway activity (including α_j). TFs are associated with pathways based on enrichment of regulated genes. Predicting TF activity is based on which TFs are associated with the significantly changed pathways; in this case, α_j is associated with ArgR.
2. The TF activity of 51 nutrient shifts was predicted and can be hierarchically clustered by nutrient shift type. TF activity for the heatmap is defined as the percentage of differential MinSpan pathways that are associated with that TF. 36% of the TF–environment associations predicted are not known, providing numerous predictions for experimentation. Experimentally tested TF–environment associations are highlighted.