Application of metabolic engineering principles to the rational design of microbial production processes crucially depends on the ability to describe quantitatively the systemic behavior of the central carbon metabolism to redirect carbon fluxes to the product-forming pathways. Despite the importance for several production processes, development of an essential dynamic model for central carbon metabolism of Escherichia coli has been severely hampered by the current lack of kinetic information on the dynamics of the metabolic reactions. Here we present the design and experimental validation of such a dynamic model, which, for the first time, links the sugar transport system (i.e., phosphotransferase system [PTS]) with the reactions of glycolysis and the pentose-phosphate pathway. Experimental observations of intracellular concentrations of metabolites and cometabolites at transient conditions are used to validate the structure of the model and to estimate the kinetic parameters. Further analysis of the detailed characteristics of the system offers the possibility of studying important questions regarding the stability and control of metabolic fluxes.

We have determined the kinetic parameters of the individual steps of the threonine pathway from aspartate in Escherichia coli under a single set of experimental conditions chosen to be physiologically relevant. Our aim was to summarize the kinetic behaviour of each enzyme in a single tractable equation that takes into account the effect of the products as competitive inhibitors of the substrates in the forward reaction and also, when appropriate (e.g. near-equilibrium reactions), as substrates of the reverse reactions. Co-operative feedback inhibition by threonine and lysine was also included as necessary. We derived the simplest rate equations that describe the salient features of the enzymes in the physiological range of metabolite concentrations in order to incorporate them ultimately into a complete model of the threonine pathway, able to predict quantitatively the behaviour of the pathway under natural or engineered conditions.

A "second-generation" production strain was derived from a Corynebacterium glutamicum pantothenate producer by rational design to assess its potential to synthesize and accumulate the vitamin pantothenate by batch cultivation. The new pantothenate production strain carries a deletion of the ilvA gene to abolish isoleucine synthesis, the promoter down-mutation P-ilvEM3 to attenuate ilvE gene expression and thereby increase ketoisovalerate availability, and two compatible plasmids to overexpress the ilvBNCD genes and duplicated copies of the panBC operon. Production assays in shake flasks revealed that the P-ilvEM3 mutation and the duplication of the panBC operon had cumulative effects on pantothenate production. During pH-regulated batch cultivation, accumulation of 8 mM pantothenate was achieved, which is the highest value reported for C. glutamicum. Metabolic flux analysis during the fermentation demonstrated that the P-ilvEM3 mutation successfully reoriented the carbon flux towards pantothenate biosynthesis. Despite this repartition of the carbon flux, ketoisovalerate not converted to pantothenate was excreted by the cell and dissipated as by-products (ketoisocaproate, DL-2,3,-dihydroxy-isovalerate, ketopantoate, pantoate), which are indicative of saturation of the pantothenate biosynthetic pathway. Genome-wide expression analysis of the production strain during batch cultivation was performed by whole-genome DNA microarray hybridization and agglomerative hierarchical clustering, which detected the enhanced expression of genes involved in leucine biosynthesis, in serine and glycine formation, in regeneration of methylenetetrahydrofolate, in de novo synthesis of nicotinic acid mononucleotide, and in a complete pathway of acyl coenzyme A conversion. Our strategy not only successfully improved pantothenate production by genetically modified C. glutamicum strains but also revealed new constraints in attaining high productivity.Pantothenate, also known as vitamin B 5 , is a necessary precursor for the biosynthesis of coenzyme A and phosphopantetheine, the prosthetic group of acyl carrier proteins, both of which are vital for a multitude of metabolic processes in all living cells (13). Pantothenate is synthesized by bacteria, fungi, and plants, but it is a nutritional requirement in mammals, including livestock and humans. The present technology for industrial pantothenate production relies largely on chemical synthesis from bulk chemicals, but manufacturing of pantothenate was also demonstrated by chemical conversion utilizing an enzymatic resolution process for the synthesis of D-pantolactone, the key intermediate in calcium pantothenate production (18), and by biotechnological approaches using recombinant Escherichia coli strains (8). Pantothenate biosynthesis and production have also been investigated in the gram-positive soil bacterium Corynebacterium glutamicum, which is widely used for large-scale fermentative production of amino acids, such as L-glutamate and L-lysine (10). In C. glutamicum ATCC 13032, four ...

A computer simulation of the threonine-synthesis pathway in Escherichia coli Tir-8 has been developed based on our previous measurements of the kinetics of the pathway enzymes under near-physiological conditions. The model successfully simulates the main features of the time courses of threonine synthesis previously observed in a cell-free extract without alteration of the experimentally determined parameters, although improved quantitative fits can be obtained with small parameter adjustments. At the concentrations of enzymes, precursors and products present in cells, the model predicts a threonine-synthesis flux close to that required to support cell growth. Furthermore, the first two enzymes operate close to equilibrium, providing an example of a near-equilibrium feedback-inhibited enzyme. The predicted flux control coefficients of the pathway enzymes under physiological conditions show that the control of flux is shared between the first three enzymes : aspartate kinase, aspartate semialdehyde dehydrogenase and homoserine dehydrogenase,

No abstract

We have determined the kinetic parameters of the individual steps of the threonine pathway from aspartate in Escherichia coli under a single set of experimental conditions chosen to be physiologically relevant. Our aim was to summarize the kinetic behaviour of each enzyme in a single tractable equation that takes into account the effect of the products as competitive inhibitors of the substrates in the forward reaction and also, when appropriate (e.g. near-equilibrium reactions), as substrates of the reverse reactions. Co-operative feedback inhibition by threonine and lysine was also included as necessary. We derived the simplest rate equations that describe the salient features of the enzymes in the physiological range of metabolite concentrations in order to incorporate them ultimately into a complete model of the threonine pathway, able to predict quantitatively the behaviour of the pathway under natural or engineered conditions.

No abstract

A computer simulation of the threonine-synthesis pathway in Escherichia coli Tir-8 has been developed based on our previous measurements of the kinetics of the pathway enzymes under near-physiological conditions. The model successfully simulates the main features of the time courses of threonine synthesis previously observed in a cell-free extract without alteration of the experimentally determined parameters, although improved quantitative fits can be obtained with small parameter adjustments. At the concentrations of enzymes, precursors and products present in cells, the model predicts a threonine-synthesis flux close to that required to support cell growth. Furthermore, the first two enzymes operate close to equilibrium, providing an example of a near-equilibrium feedback-inhibited enzyme. The predicted flux control coefficients of the pathway enzymes under physiological conditions show that the control of flux is shared between the first three enzymes: aspartate kinase, aspartate semialdehyde dehydrogenase and homoserine dehydrogenase, with no single activity dominating the control. The response of the model to the external metabolites shows that the sharing of control between the three enzymes holds across a wide range of conditions, but that the pathway flux is sensitive to the aspartate concentration. When the model was embedded in a larger model to simulate the variable demands for threonine at different growth rates, it showed the accumulation of free threonine that is typical of the Tir-8 strain at low growth rates. At low growth rates, the control of threonine flux remains largely with the pathway enzymes. As an example of the predictive power of the model, we studied the consequences of over-expressing different enzymes in the pathway.