Figure 1: Overview of the uFBA workflow.

First, extracellular (exo) and intracellular (endo) metabolite time profiles are split into discrete time intervals of linearized metabolic states using principal component analysis. For each metabolic state, the rate of change of each metabolite is calculated using linear regression, along with the 95% confidence interval (b₁ and b₂). If the metabolite’s rate of change is significant, the model is updated by changing the steady-state constraint b = 0 to a range denoted by b₁ and b₂. uFBA differs from FBA in that elements of the b vector are known and can be used as constraints, but FBA in the absence of such information assumes that these elements are zero (i.e., at steady-state).