The Experts below are selected from a list of 193350 Experts worldwide ranked by ideXlab platform
Robert A Alberty - One of the best experts on this subject based on the ideXlab platform.
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determination of rapid equilibrium Kinetic Parameters of ordered and random enzyme catalyzed reaction a b p q
Journal of Physical Chemistry B, 2009Co-Authors: Robert A AlbertyAbstract:This article deals with the rapid-equilibrium Kinetics of the forward and reverse reactions together for the ordered and random enzyme-catalyzed A + B = P + Q and emphasizes the importance of reporting the values of the full set of equilibrium constants. Equilibrium constants that are not in the rate equation can be calculated for random mechanisms using thermodynamic cycles. This treatment is based on the use of a computer to derive rate equations for three mechanisms and to estimate the Kinetic Parameters with the minimum number of velocity measurements. The most general of these three programs is the one to use first when the mechanism for A + B = P + Q is studied for the first time. This article shows the effects of experimental errors in velocity measurements on the values of the Kinetic Parameters and on the apparent equilibrium constant calculated using the Haldane relation.
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determination of rapid equilibrium Kinetic Parameters of ordered and random enzyme catalyzed reaction a b p q
Journal of Physical Chemistry B, 2009Co-Authors: Robert A AlbertyAbstract:This article deals with the rapid-equilibrium Kinetics of the forward and reverse reactions together for the ordered and random enzyme-catalyzed A + B = P + Q and emphasizes the importance of reporting the values of the full set of equilibrium constants. Equilibrium constants that are not in the rate equation can be calculated for random mechanisms using thermodynamic cycles. This treatment is based on the use of a computer to derive rate equations for three mechanisms and to estimate the Kinetic Parameters with the minimum number of velocity measurements. The most general of these three programs is the one to use first when the mechanism for A + B = P + Q is studied for the first time. This article shows the effects of experimental errors in velocity measurements on the values of the Kinetic Parameters and on the apparent equilibrium constant calculated using the Haldane relation.
Yanming Ding - One of the best experts on this subject based on the ideXlab platform.
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the accuracy and efficiency of ga and pso optimization schemes on estimating reaction Kinetic Parameters of biomass pyrolysis
Energy, 2019Co-Authors: Yanming Ding, Wenlong ZhangAbstract:Abstract Reaction Kinetic Parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are applied to estimate the Kinetic Parameters of three-component parallel reaction mechanism based on the thermogravimetric experiment in wide heating rates. The accuracy and efficiency of GA and PSO algorithms are compared with each other under the identical optimization conditions. The results indicate the better optimization abilities of PSO with the closer convergence solution to the global optimum and quicker convergence to the solution than GA based on the three-component parallel reaction mechanism of biomass pyrolysis. Especially, the improvement of best fitting value of PSO reaches up to 30% compared with that of GA. Furthermore, 14 estimated Kinetic Parameters of best fitting value are obtained and the mass loss rate predicted results including three separate components (hemicellulose, cellulose and lignin) are compared with experimental data.
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the effect of chemical reaction Kinetic Parameters on the bench scale pyrolysis of lignocellulosic biomass
Fuel, 2018Co-Authors: Yanming Ding, Ofodike A Ezekoye, Jiaqing Zhang, Changjian WangAbstract:Abstract The pyrolysis of lignocellulosic biomass has received extensive attention due to its potential as an alternative and renewable energy source. The chemical reaction Kinetic Parameters, obtained by micro-scale thermogravimetric experiments and optimized by the Shuffled Complex Evolution method, are one of the key factors to represent the pyrolysis process. The bench-scale Fire Propagation Apparatus experiment with sample size of 0.1 m × 0.1 m is conducted to investigate the scale effect of these Parameters during the pyrolysis process in a N2 environment. These optimized Parameters are applied to the pyrolysis model based on Gypro considering the three-component parallel reaction mechanism, moisture and volume change to simulate the bench-scale experiment based on FireFOAM coupled with the dynamic mesh technology. Eventually, the predicted results agree well with experimental data, validating the effectiveness of the current Parameters. Moreover, the effects of chemical reaction Kinetic Parameters from different references or models are further analyzed based upon the predicted results.
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estimation of beech pyrolysis Kinetic Parameters by shuffled complex evolution
Bioresource Technology, 2016Co-Authors: Yanming Ding, Changjian Wang, Marcos Chaos, Ruiyu ChenAbstract:The pyrolysis Kinetics of a typical biomass energy feedstock, beech, was investigated based on thermogravimetric analysis over a wide heating rate range from 5K/min to 80K/min. A three-component (corresponding to hemicellulose, cellulose and lignin) parallel decomposition reaction scheme was applied to describe the experimental data. The resulting Kinetic reaction model was coupled to an evolutionary optimization algorithm (Shuffled Complex Evolution, SCE) to obtain model Parameters. To the authors' knowledge, this is the first study in which SCE has been used in the context of thermogravimetry. The Kinetic Parameters were simultaneously optimized against data for 10, 20 and 60K/min heating rates, providing excellent fits to experimental data. Furthermore, it was shown that the optimized Parameters were applicable to heating rates (5 and 80K/min) beyond those used to generate them. Finally, the predicted results based on optimized Parameters were contrasted with those based on the literature.
Jae Kyoung Kim - One of the best experts on this subject based on the ideXlab platform.
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beyond the michaelis menten equation accurate and efficient estimation of enzyme Kinetic Parameters
Scientific Reports, 2017Co-Authors: Boseung Choi, Grzegorz A Rempala, Jae Kyoung KimAbstract:Examining enzyme Kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the enzyme Kinetic Parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of Parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify Parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the Kinetic Parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing such accurate and efficient Bayesian inference for enzyme Kinetics is provided.
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beyond the michaelis menten equation accurate and efficient estimation of enzyme Kinetic Parameters
bioRxiv, 2017Co-Authors: Boseung Choi, Grzegorz A Rempala, Jae Kyoung KimAbstract:Examining enzyme Kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the enzyme Kinetic Parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of Parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify Parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the Kinetic Parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing the Bayesian inference for such accurate and efficient enzyme Kinetics is provided.
Boseung Choi - One of the best experts on this subject based on the ideXlab platform.
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beyond the michaelis menten equation accurate and efficient estimation of enzyme Kinetic Parameters
Scientific Reports, 2017Co-Authors: Boseung Choi, Grzegorz A Rempala, Jae Kyoung KimAbstract:Examining enzyme Kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the enzyme Kinetic Parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of Parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify Parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the Kinetic Parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing such accurate and efficient Bayesian inference for enzyme Kinetics is provided.
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beyond the michaelis menten equation accurate and efficient estimation of enzyme Kinetic Parameters
bioRxiv, 2017Co-Authors: Boseung Choi, Grzegorz A Rempala, Jae Kyoung KimAbstract:Examining enzyme Kinetics is critical for understanding cellular systems and for using enzymes in industry. The Michaelis-Menten equation has been widely used for over a century to estimate the enzyme Kinetic Parameters from reaction progress curves of substrates, which is known as the progress curve assay. However, this canonical approach works in limited conditions, such as when there is a large excess of substrate over enzyme. Even when this condition is satisfied, the identifiability of Parameters is not always guaranteed, and often not verifiable in practice. To overcome such limitations of the canonical approach for the progress curve assay, here we propose a Bayesian approach based on an equation derived with the total quasi-steady-state approximation. In contrast to the canonical approach, estimates obtained with this proposed approach exhibit little bias for any combination of enzyme and substrate concentrations. Importantly, unlike the canonical approach, an optimal experiment to identify Parameters with certainty can be easily designed without any prior information. Indeed, with this proposed design, the Kinetic Parameters of diverse enzymes with disparate catalytic efficiencies, such as chymotrypsin, fumarase, and urease, can be accurately and precisely estimated from a minimal amount of timecourse data. A publicly accessible computational package performing the Bayesian inference for such accurate and efficient enzyme Kinetics is provided.
Ron Milo - One of the best experts on this subject based on the ideXlab platform.
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revisiting trade offs between rubisco Kinetic Parameters
Biochemistry, 2019Co-Authors: Avi I Flamholz, Noam Prywes, Uri Moran, Dan Davidi, Yinon M Baron, Luke M Oltrogge, Rui Alves, David F Savage, Ron MiloAbstract:Rubisco is the primary carboxylase of the Calvin cycle, the most abundant enzyme in the biosphere, and one of the best-characterized enzymes. On the basis of correlations between Rubisco Kinetic Parameters, it is widely posited that constraints embedded in the catalytic mechanism enforce trade-offs between CO2 specificity, SC/O, and maximum carboxylation rate, kcat,C. However, the reasoning that established this view was based on data from ≈20 organisms. Here, we re-examine models of trade-offs in Rubisco catalysis using a data set from ≈300 organisms. Correlations between Kinetic Parameters are substantially attenuated in this larger data set, with the inverse relationship between kcat,C and SC/O being a key example. Nonetheless, measured Kinetic Parameters display extremely limited variation, consistent with a view of Rubisco as a highly constrained enzyme. More than 95% of kcat,C values are between 1 and 10 s-1, and no measured kcat,C exceeds 15 s-1. Similarly, SC/O varies by only 30% among Form I Rubiscos and <10% among C3 plant enzymes. Limited variation in SC/O forces a strong positive correlation between the catalytic efficiencies (kcat/KM) for carboxylation and oxygenation, consistent with a model of Rubisco catalysis in which increasing the rate of addition of CO2 to the enzyme-substrate complex requires an equal increase in the O2 addition rate. Altogether, these data suggest that Rubisco evolution is tightly constrained by the physicochemical limits of CO2/O2 discrimination.
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revisiting tradeoffs in rubisco Kinetic Parameters
bioRxiv, 2018Co-Authors: Avi I Flamholz, Noam Prywes, Uri Moran, Dan Davidi, Yinon M Baron, Luke M Oltrogge, Rui Alves, David F Savage, Ron MiloAbstract:Ribulose-1,5-Bisphosphate Carboxylase/Oxygenase (Rubisco) is not only the dominant enzyme in the biosphere, responsible for the vast majority of carbon fixation, but also one of the best characterized enzymes. Enhanced Rubisco catalysis is expected to increase crop yields, but a substantially improved enzyme has evaded bioengineers for decades. Based on correlations between Rubisco9s Kinetic Parameters, it is widely posited that tradeoffs stemming from the catalytic mechanism strictly constrain Rubisco9s maximum catalytic potential. Though compelling, the reasoning that established that view was based on data from only ~20 organisms. Here we re-examine these tradeoffs with an expanded dataset including data from >200 organisms. We find that most correlations are substantially attenuated, with the inverse relationship between carboxylation k_cat and specificity S_C/O being a key example. However, the correlation predicted by one tradeoff model is stronger and more significant in our expanded dataset. In this model, increased catalytic efficiency (k_cat/K_M) for carboxylation requires a similar increase in catalytic efficiency for the competing oxygenation reaction, evidenced here by a strong power-law correlation between those catalytic efficiencies. In contrast to previous work, our results imply that Rubisco evolution is constrained mostly by the physicochemical limits of O2/CO2 recognition, which should reframe efforts to understand and engineer this very central enzyme.
