On the topological features of optimal metabolic pathway regimes

Abstract

In this work, the stoichiometric metabolic network of Escherichia coli has been formulated as a comprehensive mathematical programming model, with a view to identifying the optimal redirection of metabolic fluxes so that the yield of particular metabolites is maximized. Computation and analysis has shown that the over-production of a given metabolite at various cell growth rates is only possible for a finite ordered set of metabolic structures which, in addition, are metabolite-specific. Each regime has distinct topological features, although the actual flux values differ. Application of the model to the production of 20 amino acids on four carbon sources (glucose, glycerol, lactate, and citrate) has also indicated that, for fixed cell composition, the maximum amino acid yield decreases linearly with increasing cell growth rate. However, when the cell composition varies with cell growth rate, the amino-acid yield varies in a nonlinear manner. Medium optimization studies have also demonstrated that, of the above substrates, glucose and glycerol are the most efficient from the energetic viewpoint. Finally, model predictions are analyzed in the light of experimental data.

Link between primary and secondary metabolism in the biotransformation of trimethylammonium compounds by escherichia coli

“…Attempts have been made to optimize metabolic dynamics, but progress appears to have been hampered by the lack of kinetic and regulatory information regarding the functioning of all enzymes in a particular cell. However, it is now possible to identify the optimal redirection of metabolic fluxes by means of mathematical tools (See et al, 1996;Stephanopoulos, 1998;Stephanopoulos et al, 1998;Torres et al, 1997) whereas analytical tools have allowed the development of methodologies to reveal active metabolic pathways (Chassagnole et al, 2002;Sauer et al, 1999;Yoon et al, 2003).…”

Stoichiometric growth model for riboflavin‐producing Bacillus subtilis

“…Adding information on biosynthetic precursor requirements enables one to identify vital, critical, and redundant reactions (Edwards and Palsson, 1999), calculate maximum theoretical yields (Sauer et al, 1998;Varma et al, 1993), or to examine objectives of metabolic flux distributions (See et al, 1996). Adding experimental data on uptake and production rates, metabolic flux analysis allows one to determine the in vivo flux distribution in the cell.…”

Effect ofEscherichia coli biomass composition on central metabolic fluxes predicted by a stoichiometric model

“…Flux-based metabolic models estimate the steadystate mass and energy distributions in metabolic networks, and experimentally determined consumption and secretion rates and intermediary fluxes can be used as constraints to improve the accuracy of the calculations. Applications of this approach to a subset of hybridoma, yeast, and Escherichia coli metabolisms demonstrated the utility of this technique (Jorgensen et al, 1995;Majewski and Domach, 1990;Pramanik and Keasling, 1997;Savinell and Palsson, 1992a,b;See et al, 1986;van Gulik and Heijnen, 1995;1994 a,b;1995).…”