Flux-sum analysis identifies metabolite targets for strain improvement.

Background: Rational design of microbial strains for enhanced cellular physiology through in silico analysis has been reported in many metabolic engineering studies. Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states.

Software applications for flux balance analysis.

Flux balance analysis (FBA) is a widely used computational method for characterizing and engineering intrinsic cellular metabolism. The increasing number of its successful applications and growing popularity are possibly attributable to the availability of specific software tools for FBA. Each tool has its unique features and limitations with respect to operational environment, user-interface and supported analysis algorithms.

NADPH-dependent pgi-gene knockout Escherichia coli metabolism producing shikimate on different carbon sources.

We explored the physiological and metabolic effects of different carbon sources (glucose, fructose, and glucose/fructose mixture) in phosphoglucose isomerase (pgi) knockout Escherichia coli mutant producing shikimic acid (SA). It was observed that the pgi(-) mutant grown on glucose exhibited significantly lower cell growth compared with the pgi(+) strain and its mixed glucose/fructose fermentation grew well. Interestingly, when fructose was used as a carbon source, the pgi(-) mutant showed the enhanced SA production compared with the pgi(+) strain.

Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network.

Background: Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.