Genome-scale analysis of Mannheimia succiniciproducens metabolism

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement.     

Mechanism of amino Acid uptake by sugarcane suspension cells

The amino acid carriers in sugarcane suspension cells were characterized for amino acid specificity and the stoichiometry of proton and potassium flux during amino acid transport.

Amino acid transport by sugarcane cells is dependent upon three distinct transport systems. One system is specific for neutral amino acids and transports all neutral amino acids including glutamine, asparagine, and histidine. The uptake of neutral amino acids is coupled to the uptake of one proton per amino acid; one potassium ion leaves the cells for charge compensation. Histidine is only taken up in the neutral form so that deprotonation of the charged imidazole nitrogen has to occur prior to uptake. The basic amino acids are transported by another system as uniport with charge-compensating efflux of protons and potassium. The acidic amino acids are transported by a third system. Acidic amino acids bind to the transport site only if the distal carboxyl group is in the dissociated form (i.e. if the acidic amino acid is anionic). Two protons are withdrawn from the medium and one potassium leaves the cell for charge compensation during the uptake of acid amino acids. Common to all three uptake systems is a monovalent positively charged amino acidproton carrier complex at the transport site.

     

Energy recycling by lactate efflux in growing and nongrowing cells of Streptococcus cremoris.

Streptococcus cremoris was grown in pH-regulated batch and continuous cultures with lactose as the energy source. During growth the magnitude and composition of the electrochemical proton gradient and the lactate concentration gradient were determined. The upper limit of the number of protons translocated with a lactate molecule during lactate excretion (the proton-lactate stoichiometry) was calculated from the magnitudes of the membrane potential, the transmembrane pH difference, and the lactate concentration gradient. In cells growing in continuous culture, a low lactate concentration gradient (an internal lactate concentration of 35 to 45 mM at an external lactate concentration of 25 mM) existed. The cell yield (Ymax lactose) increased with increasing growth pH. In batch culture at pH 6.34, a considerable lactate gradient (more than 60 mV) was present during the early stages of growth. As growth continued, the electrochemical proton gradient did not change significantly (from -100 to -110 mV), but the lactate gradient decreased gradually. The H+-lactate stoichiometry of the excretion process decreased from 1.5 to about 0.9. In nongrowing cells, the magnitude and composition of the electrochemical proton gradient was dependent on the external pH but not on the external lactate concentration (up to 50 mM). The magnitude of the lactate gradient was independent of the external pH but decreased greatly with increasing external lactate concentrations. At very low lactate concentrations, a lactate gradient of 100 mV existed, which decreased to about 40 mV at 50 mM external lactate. As a consequence, the proton-lactate stoichiometry decreased with increasing external concentrations of protons and lactate at pH 7.0 from 1 mM lactate to 1.1 at 50 mM lactate and at pH 5.5 from 1.4 at l mM lactate to 0.7 at 50 mM lactate. The data presented in this paper suggest that a decrease in external pH and an increase in external lactate concentration both result in lower proton-lactate stoichiometry values and therefore in a decrease of the generation of metabolic energy by the end product efflux process.     

In silico genome-scale reconstruction and validation of the Corynebacterium glutamicum metabolic network

A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolites, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model was extensively validated against published flux data, and flux distribution values were found to correlate well between simulations and experiments. The split pathway of the lysine synthesis pathway of C. glutamicum was investigated, and it was found that the direct dehydrogenase variant gave a higher lysine yield than the alternative succinyl pathway at high lysine production rates. The NADPH demand of the network was not found to be critical for lysine production until lysine yields exceeded 55% (mmol lysine (mmol glucose)(-1)). The model was validated during growth on the organic acids acetate and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed.     

Genome-scale model for Clostridium acetobutylicum: Part I. Metabolic network resolution and analysis

A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico.     

Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium

Constraint-based metabolic modeling comprises various excellent tools to assess experimentally observed phenotypic behavior of micro-organisms in terms of intracellular metabolic fluxes. In combination with genome-scale metabolic networks, micro-organisms can be investigated in much more detail and under more complex environmental conditions. Although complex media are ubiquitously applied in industrial fermentations and are often a prerequisite for high protein secretion yields, such multi-component conditions are seldom investigated using genome-scale flux analysis. In this paper, a systematic and integrative approach is presented to determine metabolic fluxes in Streptomyces lividans TK24 grown on a nutritious and complex medium. Genome-scale flux balance analysis and randomized sampling of the solution space are combined to extract maximum information from exometabolome profiles. It is shown that biomass maximization cannot predict the observed metabolite production pattern as such. Although this cellular objective commonly applies to batch fermentation data, both input and output constraints are required to reproduce the measured biomass production rate. Rich media hence not necessarily lead to maximum biomass growth. To eventually identify a unique intracellular flux vector, a hierarchical optimization of cellular objectives is adopted. Out of various tested secondary objectives, maximization of the ATP yield per flux unit returns the closest agreement with the maximum frequency in flux histograms. This unique flux estimation is hence considered as a reasonable approximation for the biological fluxes. Flux maps for different growth phases show no active oxidative part of the pentose phosphate pathway, but NADPH generation in the TCA cycle and NADPH transdehydrogenase activity are most important in fulfilling the NADPH balance. Amino acids contribute to biomass growth by augmenting the pool of available amino acids and by boosting the TCA cycle, particularly when using glutamate and aspartate. Depletion of glutamate and aspartate causes a distinct shift in fluxes of the central carbon and nitrogen metabolism. In the current work, hurdles encountered in flux analysis at a genome-scale level are addressed using hierarchical flux balance analysis and uniform sampling of the constrained solution space. This general framework can now be adopted in further studies of S. lividans, e.g., as a host for heterologous protein production.     

Characterizing Escherichia coli DH5alpha growth and metabolism in a complex medium using genome-scale flux analysis

Genome-scale flux analysis of Escherichia coli DH5alpha growth in a complex medium was performed to investigate the relationship between the uptake of various nutrients and their metabolic outcomes. During the exponential growth phase, we observed a sequential consumption order of serine, aspartate and glutamate in the complex medium as well as the complete consumption of key carbohydrate nutrients, glucose and trehalose. Based on the consumption and production rates of the measured metabolites, constraints-based flux analysis of a genome-scale E. coli model was then conducted to elucidate their utilization in the metabolism. The in silico analysis revealed that the cell exploited biosynthetic precursors taken up directly from the complex medium, through growth-related anabolic pathways. This suggests that the cell could be functioning in an energetically more efficient manner by reducing the energy needed to produce amino acids. The in silico simulation also allowed us to explain the observed rapid consumption of serine: excessively consumed external serine from the complex medium was mainly converted into pyruvate and glycine, which in turn, led to the acetate accumulation. The present work demonstrates the application of an in silico modeling approach to characterizing microbial metabolism under complex medium condition. This work further illustrates the use of in silico genome-scale analysis for developing better strategies related to improving microbial growth and enhancing the productivity of desirable metabolites.     

Metabolic analysis of the synthesis of high levels of intracellular human SOD in Saccharomyces cerevisiae rhSOD 2060 411 SGA122

The synthesis of human superoxide dismutase (SOD) in batch cultures of a Saccharomyces cerevisiae strain using a glucose-limited minimal medium was studied through metabolic flux analysis. A stoichiometric model was built, which included 78 reactions, according to metabolic pathways operative in these strains during respirofermentative and oxidative metabolism. It allowed calculation of the distribution of metabolic fluxes during diauxic growth on glucose and ethanol. Fermentation profiles and metabolic fluxes were analyzed at different phases of diauxic growth for the recombinant strain (P+) and for its wild type (P-). The synthesis of SOD by the strain P+ resulted in a decrease in specific growth rate of 34 and 54% (growth on glucose and ethanol respectively) in comparison to the wild type. Both strains exhibited similar flux of glucose consumption and ethanol synthesis but important differences in carbon distribution with biomass/substrate yields and ATP production 50% higher in P-. A higher contribution of fermentative metabolism, with 64% of the energy produced at the phosphorylation level, was observed during SOD production. The flux of precursors to amino acids and nucleotides was higher in the recombinant strain, in agreement with the higher total RNA and protein levels. Lower specific growth rates in strain P+ appear to be related to the decrease in the rate of synthesis of nonrecombinant protein, as well as a decrease in the activities of the pentose phosphate (PP) pathway and TCA cycle. A very different way of entry into the stationary phase was observed for each strain: in the wild-type strain most metabolic fluxes decreased and fluxes related to energy reserve synthesis increased, while in the P+ strain the flux of 22 reactions (including PP pathway and amino acids biosynthesis) related to SOD production increased their fluxes. Changes in SOD production rates at different physiological states appear to be related to the differences in building blocks availability between respirofermentative and oxidative metabolism. Using the present expression system, ideal conditions for SOD synthesis are represented by either active growth during respirofermentative metabolism or transition from a growing to a nongrowing state. An increase in SOD flux could be achieved using an expression system nonassociated to growth and potentially eliminating part of the metabolic burden.     

Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110.

Flux balance models of metabolism use stoichiometry of metabolic pathways, metabolic demands of growth, and optimality principles to predict metabolic flux distribution and cellular growth under specified environmental conditions. These models have provided a mechanistic interpretation of systemic metabolic physiology, and they are also useful as a quantitative tool for metabolic pathway design. Quantitative predictions of cell growth and metabolic by-product secretion that are experimentally testable can be obtained from these models. In the present report, we used independent measurements to determine the model parameters for the wild-type Escherichia coli strain W3110. We experimentally determined the maximum oxygen utilization rate (15 mmol of O2 per g [dry weight] per h), the maximum aerobic glucose utilization rate (10.5 mmol of Glc per g [dry weight] per h), the maximum anaerobic glucose utilization rate (18.5 mmol of Glc per g [dry weight] per h), the non-growth-associated maintenance requirements (7.6 mmol of ATP per g [dry weight] per h), and the growth-associated maintenance requirements (13 mmol of ATP per g of biomass). The flux balance model specified by these parameters was found to quantitatively predict glucose and oxygen uptake rates as well as acetate secretion rates observed in chemostat experiments. We have formulated a predictive algorithm in order to apply the flux balance model to describe unsteady-state growth and by-product secretion in aerobic batch, fed-batch, and anaerobic batch cultures. In aerobic experiments we observed acetate secretion, accumulation in the culture medium, and reutilization from the culture medium. In fed-batch cultures acetate is cometabolized with glucose during the later part of the culture period.(ABSTRACT TRUNCATED AT 250 WORDS)     

Elucidation of metabolism in hybridoma cells grown in fed‐batch culture by genome‐scale modeling

Genome‐scale modeling of mouse hybridoma cells producing monoclonal antibodies (mAb) was performed to elucidate their physiological and metabolic states during fed‐batch cell culture. Initially, feed media nutrients were monitored to identify key components among carbon sources and amino acids with significant impact on the desired outcome, for example, cell growth and antibody production. The monitored profiles indicated rapid assimilation of glucose and glutamine during the exponential growth phase. Significant increase in mAb concentration was also observed when glutamine concentration was controlled at 0.5 mM as a feeding strategy. Based on the reconstructed genome‐scale metabolic network of mouse hybridoma cells and fed‐batch profiles, flux analysis was then implemented to investigate the cellular behavior and changes in internal fluxes during the cell culture. The simulated profile of the cell growth was consistent with experimentally measured specific growth rate. The in silico simulation results indicated (i) predominant utilization of glycolytic pathway for ATP production, (ii) importance of pyruvate node in metabolic shifting, and (iii) characteristic pattern in lactate to glucose ratio during the exponential phase. In future, experimental and in silico analyses can serve as a promising approach to identifying optimal feeding strategies and potential cell engineering targets as well as facilitate media optimization for the enhanced production of mAb or recombinant proteins in mammalian cells. Biotechnol. Bioeng. 2009;102: 1494–1504. © 2008 Wiley Periodicals, Inc.     

Genome-scale modeling and in silico analysis of ethanologenic bacteria Zymomonas mobilis

Bioethanol has been recognized as a potential alternative energy source. Among various ethanol-producing microbes, Zymomonas mobilis has acquired special attention due to its higher ethanol yield and tolerance. However, cellular metabolism in Z. mobilis remains unclear, hindering its practical application for bioethanol production. To elucidate such physiological characteristics, we reconstructed and validated a genome-scale metabolic network (iZM363) of Z. mobilis ATCC31821 (ZM4) based on its annotated genome and biochemical information. The phenotypic behaviors and metabolic states predicted by our genome-scale model were highly consistent with the experimental observations of Z. mobilis ZM4 strain growing on glucose as well as NMR-measured intracellular fluxes of an engineered strain utilizing glucose, fructose, and xylose. Subsequent comparative analysis with Escherichia coli and Saccharomyces cerevisiae as well as gene essentiality and flux coupling analyses have also confirmed the functional role of pdc and adh genes in the ethanologenic activity of Z. mobilis, thus leading to better understanding of this natural ethanol producer. In future, the current model could be employed to identify potential cell engineering targets, thereby enhancing the productivity of ethanol in Z. mobilis.     

Systems-level analysis of genome-scalein silico metabolic models using MetaFluxNet

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution residesin silico genome-scale metabolic model. In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.     

Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.     

Metabolic flux analysis for optimizing the specific growth rate of recombinant <Emphasis Type="Italic">Aspergillus niger</Emphasis>

A comprehensive metabolic network comprising three intracellular compartments (cytoplasm, mitochondrion and peroxisome) was developed for Aspergillus niger. The metabolic flux network includes carbohydrate and amino acid metabolism in both anabolic and catabolic reactions. Linear programming was used for the optimization of the specific growth rates in combination with 37 measured input and output fluxes of the key metabolites to evaluate corresponding intracellular flux distributions throughout the batch fermentations. Logarithmic sensitivity analysis revealed that the addition of proline, alanine and glutamate benefited growth in defined media. The experimental observations and flux analysis showed that tyrosine was a potential candidate for biomass production improvement. Model predictions was verified by conducting batch and fed-batch fermentations and it was found that the addition of the four amino acids according to the predetermined schedule resulted in a 44 and 41% improvements in biomass and recombinant protein productions, respectively.     

Dynamic flux balance analysis of the metabolism of Saccharomyces cerevisiae during the shift from fully respirative or respirofermentative metabolic states to anaerobiosis

Dynamic flux balance analysis was utilized to simulate the metabolic behaviour of initially fully respirative and respirofermentative steady-state cultures of Saccharomyces?cerevisiae during sudden oxygen depletion. The hybrid model for the dynamic flux balance analysis included a stoichiometric genome-scale metabolic model as a static part and dynamic equations for the uptake of glucose and the cessation of respirative metabolism. The yeast consensus genome-scale metabolic model [Herrg?rd MJ et?al. (2008) Nat Biotechnol26, 1155-1160; Dobson PD et?al. (2010) BMC Syst Biol4, 145] was refined with respect to oxygen-dependent energy metabolism and further modified to reflect S.?cerevisiae anabolism in the absence of oxygen. Dynamic flux balance analysis captured well the essential features of the dynamic metabolic behaviour of S.?cerevisiae during adaptation to anaerobiosis. Modelling and simulation enabled the identification of short time-scale flux distribution dynamics under the transition to anaerobic metabolism, during which the specific growth rate was reduced, as well as longer time-scale process dynamics when the specific growth rate recovered. Expression of the metabolic genes was set into the context of the identified dynamics. Metabolic gene expression responses associated with the specific growth rate and with the cessation of respirative metabolism were distinguished.     

Electrochemical proton gradient and lactate concentration gradient in Streptococcus cremoris cells grown in batch culture.

The lactate concentration gradient and the components of the electrochemical proton gradient (delta micro H+) were determined in cells of Streptococcus cremoris growing in batch culture. The membrane potential (delta psi) and the pH gradient (delta pH) were determined from the accumulation of the lipophilic cation tetraphenylphosphonium and the weak acid benzoate, respectively. During growth the external pH decreased from 6.8 to 5.3 due to the production of lactate. Delta pH increased from 0 to -35 mV, inside alkaline (at an external pH of 5.7), and fell to zero directly after growth stopped. Delta psi was nearly constant at -90 mV during growth and also dissipated within 40 min after termination of growth. The internal lactate concentration decreased from 200 mM at the beginning of growth (at pH 6.8) to 30 mM at the end of growth (at pH 5.3); the external lactate concentration increased from 8 to 30 mM due to the fermentation of lactose. Thus, the lactate gradient decreased from 80 mV to zero as growth proceeded and the external pH decreased. From the data obtained on delta psi, delta pH, and the lactate concentration gradient, the H+/lactate stoichiometry (n) was calculated. The value of n varied with the external pH from 1.9 (at pH 6.8) to 0.9 (at pH values below 6). This implies that especially at high pH values the carrier-mediated efflux of lactate supplies a significant quantity of metabolic energy to S. cremoris cells. At pH 6.8 this energy gain was almost two ATP equivalents per molecule of lactose consumed if the H+/ATP stoichiometry equals 2. These results supply strong experimental evidence for the energy recycling model postulated by Michels et al.     

Charge balance and acidity regulation during growth of sugar-cane cell suspensions

Abstract. An analysis of nutrient uptake by batch cultures of sugar-cane cells was performed to gain information about the ionic balance during uptake of charged metabolites. Whereas younger cultures (up to 1 week old) have to compensate excess cation influx with proton efflux, older cultures show balanced cation–anion uptake.
Younger cells produce a small amount of carboxylic acids to furnish protons for charge compensation at the cytoplasmic membrane. Older cells synthesize organic acids more abundantly to generate protons necessary for the proton demand of nitrate and sulphate assimilation. Despite these assimilation reactions only a small percentage of carbon, which is taken up mainly as hexose, needs to be oxidized to carboxylic acids for that purpose. In contrast, younger cultures preferentially use the amino acids of the medium instead of assimilating nitrate. The use of amino acids as a nitrogen source does not require a significant part of metabolism for biochemical pH-stability, whereas an efficient proton circulation on the cytoplasmic membrane seems to be of major importance.
A balance study of the main metabolized elements, carbon, nitrogen and sulphur was performed to get a quantitative impression of the fate of these nutrients during growth of cell cultures.     

Construction of an E. Coli genome-scale atom mapping model for MFA calculations

Metabolic flux analysis (MFA) has so far been restricted to lumped networks lacking many important pathways, partly due to the difficulty in automatically generating isotope mapping matrices for genome-scale metabolic networks. Here we introduce a procedure that uses a compound matching algorithm based on the graph theoretical concept of pattern recognition along with relevant reaction information to automatically generate genome-scale atom mappings which trace the path of atoms from reactants to products for every reaction. The procedure is applied to the iAF1260 metabolic reconstruction of Escherichia coli yielding the genome-scale isotope mapping model imPR90068. This model maps 90,068 non-hydrogen atoms that span all 2,077 reactions present in iAF1260 (previous largest mapping model included 238 reactions). The expanded scope of the isotope mapping model allows the complete tracking of labeled atoms through pathways such as cofactor and prosthetic group biosynthesis and histidine metabolism. An EMU representation of imPR90068 is also constructed and made available.     

Metabolic flux analysis for calcium dependent antibiotic (CDA) production in Streptomyces coelicolor

The calcium dependent antibiotic (CDA) is a nonribosomal lipopeptide produced by Streptomyces coelicolor. We constructed a metabolic network of more than 400 reactions for the primary and secondary metabolism of S. coelicolor and used computational metabolic flux balancing to investigate some of the factors affecting growth and production of CDA. Computational results indicated that the CDA production was concomitant with growth. Computational specific growth rates were twice as high as the experimental specific growth rates. Metabolic flux distributions and sensitivity analyses computed for various phases of the batch culture indicated that the specific CDA production rate was affected by nitrogen assimilation, pentose phosphate pathway, shikimate biosynthesis, and oxoglutarate fluxes. Consequently, these metabolic targets were tested using genetic deletions in the model which increased the in silico specific CDA production rate.