Genome-scale metabolic networks can now be reconstructed based on annotated
genomic data augmented with biochemical and physiological information abou
t the organism. Mathematical analysis can be performed to assess the capabi
lities of these reconstructed networks. The constraints-based framework, wi
th flux balance analysis (FBA), has been used successfully to predict time
course of growth and by-product secretion, effects of mutation and knock-ou
ts, and gene expression profiles. However, FBA leads to incorrect predictio
ns in situations where regulatory effects are a dominant influence on the b
ehavior of the organism. Thus, there is a need to include regulatory events
within FBA to broaden its scope and predictive capabilities. Here we repre
sent transcriptional regulatory events as time-dependent constraints on the
capabilities of a reconstructed metabolic network to further constrain the
space of possible network functions. Using a simplified metabolic/regulato
ry network, growth is simulated under various conditions to illustrate syst
emic effects such as catabolite repression, the aerobic/anaerobic diauxic s
hift and amino acid biosynthesis pathway repression. The incorporation of t
ranscriptional regulatory events in FBA enables us to interpret, analyse an
d predict the effects of transcriptional regulation on cellular metabolism
at the systemic level. (C) 2001 Academic Press.