Metabolic Reaction Model Research Articles

An Efficient and Very Accurate Method for Calculating Steady-State Sensitivities in Metabolic Reaction Systems.

Stability and sensitivity analyses of biological systems require the ad hocwriting of computer code, which is highly dependent on the particular model and burdensome for large systems. We propose a very accurate strategy to overcome this challenge. Its core concept is the conversion of the model into the format of biochemical systems theory (BST), which greatly facilitates the computation of sensitivities. First, the steady state of interest is determined by integrating the model equations toward the steady state and then using a Newton-Raphson method to fine-tune the result. The second step of conversion into the BST format requires several instances of numerical differentiation. The accuracy of this task is ensured by the use of a complex-variable Taylor scheme for all differentiation steps. The proposed strategy is implemented in a new software program, COSMOS, which automates the stability and sensitivity analysis of essentially arbitrary ODE models in a quick, yet highly accurate manner. The methods underlying the process are theoretically analyzed and illustrated with four representative examples: a simple metabolic reaction model; a model of aspartate-derived amino acid biosynthesis; a TCA-cycle model; and a modified TCA-cycle model. COSMOS has been deposited to https://github.com/BioprocessdesignLab/COSMOS.

Effects of the changes in enzyme activities on metabolic flux redistribution around the 2-oxoglutarate branch in glutamate production by Corynebacterium glutamicum.

An experimental method for metabolic control analysis (MCA) was applied to the investigation of a metabolic network of glutamate production by Corynebacterium glutamicum. A metabolic reaction (MR) model was constructed and used for flux distribution analysis (MFA). The flux distribution at a key branch point, 2-oxoglutarate, was investigated in detail. Activities of isocitrate dehydrogenase (ICDH), glutamate dehydrogenase (GDH), and 2-oxoglutarate dehydrogenase complex (ODHC) around this the branch point were changed, using two genetically engineered strains (one with enhanced ICDH activity and the other with enhanced GDH activity) and by controlling environmental conditions (i.e. biotin-deficient conditions). The mole flux distribution was determined by an MR model, and the effects of the changes in the enzyme activities on the mole flux distribution were compared. Even though both GDH and ICDH activities were enhanced, the mole flux distribution was not significantly changed. When the ODHC activity was attenuated, the flux through ODHC decreased, and glutamate production was markedly increased. The flux control coefficients of the above three enzymes for glutamate production were determined based on changes in enzyme activities and the mole flux distributions. It was found that the factor with greatest impact on glutamate production in the metabolic network was obtained by attenuation of ODHC activity.

Model predictive controller for biodegradable polyhydroxyalkanoate production in fed-batch culture

The aim of this study is to develop a model predictive controller (MPC) accompanied with a metabolic reaction model controller for controlling ethanol and n-pentanol concentrations and the mole fraction of monomer units in the production of poly(β-hydroxybutyrate-co-β-hydroxyvalerate), P(HB-co-HV), a biodegradable copolyester. The controller consists of two parts: one is for alcohol concentration control and the other one is for mole fraction control, and is based on the concept of metabolic flux distribution control. For control of alcohol concentration, conventional proportional and integral (PI) controller and feedforward/feedback controller did not function sufficiently because the large sampling interval of the biosensor led to a severe overshoot of concentration. A single-input and single-output (SISO) MPC is constructed for control of ethanol concentration in the growth phase, whereas a multi-input and multi-output (MIMO) MPC is constructed for control of both alcohol concentrations in the production phase. Specific ethanol consumption rate was estimated by the MPC using the past time series data of ethanol concentration. By means of simulations and experiments, the weighting parameters of the noise filters in the MPC were well adjusted. Ethanol and n-pentanol concentrations were well controlled by the MPC, compared with PI controller and feedforward/feedback controller. As a result, P(HB-co-HV) production was maximized with a given value of mole fraction of 3HV units at the end of cultivation.

Metabolic Engineering. Integrating Methodologies of Molecular Breeding and Bioprocess Systems Engineering.

Metabolic engineering is an integrating methodology of analysis and synthesis for the improvement of flux distribution of metabolic pathways in complicated bioprocesses, which are highly multi-hierarchical systems to extend from macroscopic to microscopic levels. Recent progress in metabolic engineering methodologies to improve metabolic pathways in microorganisms was reviewed with many studies in this paper. Metabolic flux distribution was analyzed under different environmental conditions, using a metabolic reaction model. The physiological states of microorganisms were understood by interpreting metabolic flux analysis (MFA). This analysis was also used for development of process operation and control strategy. Cell capability to form a targeted product was analyzed with a metabolic reaction model and linear programming (LP). The use of a 13C-enriched carbon source and nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GCMS) analyses of intracellular and extracellular metabolites enabled determination of a metabolic flux distribution more accurately than the flux distribution determined only by the metabolic reaction model, which involves not only metabolite balances but also energy and redox balances. The comparison of metabolic flux distributions between before and after genetic modification of cells yielded information on the mechanism of regulation of metabolic flux in microorganisms. Finally, integration of bioinformatics and metabolic engineering is discussed, and cyclic modification of the complex bionetwork and process development were emphasized.

Quality Control of Polyhydroxyalkanoates in Fed-Batch Culture Based on a Metabolic Reaction Model

In order to control the mole fraction of monomer unit of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), P(3HB-co-3HV), a biodegradable polymer, a novel multivariable control strategy is developed in a fed-batch culture of Paracoccus denitrificans ATCC 17741. This controller consists of two parts: one is for model predictive ethanol concentration control and the other one is for mole fraction control of monomer units. When the mole fraction of 3HV units has a target value, the feed rate of n-pentanol becomes a function of the feed rate of ethanol and the set value of 3HV, based on a metabolic reaction (MR) model. The mole fraction of 3HV units successfully reaches to the target value using this strategy. Chemical distribution is also analyzed based on the first order Marcovian statistics.

Multivariable control of alcohol concentrations in the production of polyhydroxyalkanoates (PHAs) by Paracoccus denitrificans.

A novel multivariable control strategy is developed for alcohol (ethanol and n-pentanol) concentrations in the production of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), P(HB-co-HV), a biodegradable polymer by Paracoccus denitrificans ATCC 1774. This controller, which is developed to control the mole fraction of P(HB-co-HV), consists of two parts: one is for ethanol concentration control and the other is for mole fraction control, based on the concept of metabolic flux distribution control. A simple metabolic reaction (MR) model is constructed for flux distribution analysis. The relationship between mole ratio of specific consumption rate of the two alcohols (ethanol and n-pentanol) and the mole fraction of 3HV units in the polymer is linear. This result suggests that the split ratio at a branched point of 3-ketovaleryl-CoA in the P(HB-co-HV) synthetic pathway is constant for several fermentation conditions. When the mole fraction of 3HV units has a target value, the feed rate of n-pentanol becomes a function of the feed rate of ethanol and the set value of 3HV, based on the MR model. The mole fraction of 3HV units successfully reached the target value using this strategy. The mole fraction control strategy is combined with an optimal production strategy based on the optimal profile of the specific growth rate. The combined strategy is realized using multivariable controllers and P(3HB-co-3HV) production is maximized with a given value of mole fraction of 3HV units at the final step of fermentation.

Constrained optimization of l-lysine production based on metabolic flux using a mathematical programming method

Constrained optimization for microbial fermentation was studied. For optimization, we used not the maximum principle but a nonlinear programming method because of the need to consider many metabolic reactions. In the case of l-lysine fermentation, the optimization problem in l-lysine production was formulated as a nonlinear programming problem. In general, the state equations based on material balances are represented as differential equations, but such equations which are dependent on time can not be applied to a nonlinear programming problem. Therefore, the state equations were made discrete in a time base, and a new single vector which is not dependent on time was substituted. From these formulae, the objective function and the constraints using nonlinear programming problem were defined as the amount of l-lysine produced, and as a metabolic reaction model and empirical equations, respectively. Computer program was developed to solve this constrained nonlinear programming problem. The applied algorithm of the computer programming was a sequential quadratic programming method (SQP method). When the constrained nonlinear programming problem is solved using the SQP method, the maximum amount of l-lysine produced and the optimal feeding rate of l-threonine could be calculated. From the calculated results, it was clear that introduction of the equality and inequality constraints was easy. l-lysine at a concentration up to 75.3 g/l could be produced when the fermentation was carried out under optimal conditions.

Constrained Optimization of L-Lysine Production Based on Metabolic Flux Using a Mathematical Programming Method.

Constrained optimization for microbial fermentation was studied. For optimization, we used not the maximum principle but a nonlinear programming method because of the need to consider many metabolic reactions. In the case of L-lysine fermentation, the optimization problem in L-lysine production was formulated as a nonlinear programming problem. In general, the state equations based on material balances are represented as differential equations, but such equations which are dependent on time can not be applied to a nonlinear programming problem. Therefore, the state equations were made discrete in a time base, and a new single vector which is not dependent on time was substituted. From these formulae, the objective function and the constraints using nonlinear programming problem were defined as the amount of L-lysine produced, and as a metabolic reaction model and empirical equations, respectively. Computer program was developed to solve this constrained nonlinear programming problem. The applied algorithm of the computer programming was a sequential quadratic programming method (SQP method). When the constrained nonlinear programming problem is solved using the SQP method, the maximum amount of L-lysine produced and the optimal feeding rate of L-threonine could be calculated. From the calculated results, it was clear that introduction of the equality and inequality constraints was easy. L-Lysine at a concentration up to 75.3 g/l could be produced when the fermentation was carried out under optimal conditions.

Feeding Strategy of Required Components by Metabolic Engineering Approach in a Lysine Production Process

A metabolic reaction model was developed for on-line analysis of mole flux distribution in lysine metabolic pathway of Corynebacterium glutamicum AJ-3462 which was a leucine auxotroph mutant. The physiological states of the microorganisms were recognized based on the estimated mole flux distribution. In order to elongate the fermentation time keeping lysine productivity and production yield at high levels, effect of leucine addition on the mole flux distribution was analyzed. The intermittently addition strategy of leucine was developed based on the changes in the cell activities with respect to growth and lysine synthesis. Interval time of intermittent addition was changed from 30 h to 24 h when a decrease of cell activity was recognized. As the result, the fermentation time was elongated to 174 h, and lysine productivity and total molar production yield of lysine from glucose became 0.104 g/l/h and 29.2 %, respectively.

Method of Corynebacterium glutamicum fermentation time extension with high lysine production rate by leucine addition

The effect of leucine addition on the production yield of lysine by Corynebacterium glutamicum AJ-3462 was studied and a method of increasing a fermentation time while maintaining high lysine producing activity was developed. When leucine was not added during the lysine production phase, a high molar production yield of lysine from glucose of 35.6% was obtained. However, lysine production did not continue for a long time and almost stopped at around 90 h. When leucine was added to the medium in the fermentor at 24 h intervals, lysine producing activity was maintained for a long time; however, the production yield of lysine from glucose was reduced to 23.5%. When leucine was added at 30 h intervals, the production yield of lysine from glucose was improved to 32.9%. However, leucine addition did not recover lysine producing activity after 162 h. From these results, in order to extend the fermentation time while maintaining lysine productivity and production yield at high values, a leucine addition strategy was developed as follows. The initial leucine concentration was set as 0.1 g/ l. After depletion of leucine, it was added intermittently at 30 h intervals. The interval for intermittent addition was changed to 24 h when a decrease in cellular activity was recognized by on-line monitoring of the lysine production rate based on a metabolic reaction model. As a result, the fermentation time was extended to 174 h, and lysine productivity and total molar production yield of lysine from glucose became 0.104 g/ l/h and 29.2%, respectively.

An on-line physiological state recognition system for the lysine fermentation process based on a metabolic reaction model

A metabolic reaction model was developed for the lysine fermentation process by Corynebacterium glutamicum AJ-3462 to estimate the physiological state of the cells-that is, the growth and production activity, and the flux distribution of metabolites-from on-line measurable rates only. First, the extended Kalman filter was applied to eliminate noise in the measured rates. Then, using the metabolic reaction model, the lysine production rate and flux distribution were calculated. The estimation results allowed the physiological state of lysine production to be recognized, and an appropriate measure corresponding to the estimated state, such as intermittent addition of glucose and/or leucine, to be taken to maintain a high level of lysine productivity in batch culture. Finally, application of the recognition system enabled lysine to be produced from glucose at a higher yield than that from glucose- or leucine-limited exponential fed-batch cultures.

Comparison of estimation techniques for a time-dependent parameter in a metabolic reaction model

A time-dependent parameter, the oxidative phosphorylation, P O ratio, was estimated in a metabolic reaction model of aerobic yeast fermentation by two methods and the accuracies of the estimates were compared. In the first method, P O was estimated according to on-line physiological state recognition results by a fuzzy inferencing method based on error vectors. The amount of cell growth was then calculated using the metabolic reaction model with the estimated P O . In the second method, P O was estimated adaptively in the metabolic reaction model using the pseudo-inverse matrix (Least square form for total residual error minimum). When the two methods were compared with respect to the on-line estimation of cell growth and the ease of use on-line, the method based on fuzzy inferencing was better than that using the pseudo-inverse matrix.

On‐line state recognition in a yeast fed‐batch culture using error vectors

The physiological states with respect to cell growth and ethanol production in a yeast fed-batch culture expressed in linguistic form could be recognized on-line by fuzzy inferencing based on error vectors. The error vector was newly defined here in a macroscopic elemental balance equation. The physiological states for cell growth and ethanol production were characterized by error vectors using many experimental data from fed-batch cultures. Fuzzy membership functions were constructed from the frequency distributions of the error vectors and state recognition was performed by fuzzy inferencing. In particular, an unusual physiological state for a yeast cultivation, in which aerobic ethanol production was accompanied by very low cell growth, could be recognized accurately. According to the results of the state recognition, an energy parameter, the P/O ratio in the metabolic reaction model was adaptively estimated, and the cell growth was successfully evaluated with the estimated P/O. (c) 1995 John Wiley & Sons, Inc.

On-Line Physiological State Recognition and Parameter Estimation in the Metabolic Reaction Model Using Error Vectors

An on-line recognition system of the physiological states in a yeast fed-batch culture based on a fuzzy inferencing and a metabolic reaction model is discussed. State recognition of the yeast fermentation was performed by the fuzzy inferencing with error vectors. According to the results of the state recognition, P/O ratio in the metabolic reaction model was adaptively estimated. The amount of cell growth was estimated using the metabolic reaction model with the estimated P/O. The residual error by this method was compared with that by the least square parameter estimation.