Influence of mass transfer limitations on determination of the half saturation constant for hydrogen uptake in a mixed‐culture CH4‐producing enrichment
Abstract:There is strong evidence in the literature supporting the existence of significant mass transfer limitations on the kinetics of exogenous H(2) consumption by methanogens. The half saturation constant for H (2) uptake by a mixed-culture, CH(4) producing enrichment was measured using an experimental protocol that avoided internal mass transfer limitations. The value obtained was two orders of magnitude smaller than any other previously reported. A mathematical model for acetogenic syntrophic associations was dev… Show more
“…Thus, substrate threshold values appear to be a new and important parameter that may significantly influence anaerobic reactions such as hydrogen oxidation. The use of the H2 threshold value in kinetic models has also been proposed recently (Giraldo-Gomez et al, 1992). When the concentration of the substrates is far higher than that of the products and the reaction driving force is large, the ratio T/K becomes very small and the model predictions approach those of the classical Michaelis-Menten model (see also Figs.…”
Section: General Model Behaviormentioning
confidence: 97%
“…These threshold values indicated that bacterial reaction kinetics do not necessarily result in the complete utilization of the substrate. The existence of hydrogen threshold values indicated that v,,, values and k , values alone may not be sufficient to predict the efficiency of utilization of low hydrogen concentrations (Giraldo-Gomez et al, 1992;Lovley, 1985). These hydrogen threshold values were found to be related to the free energy change of the reaction (Cord-Ruwisch et al, 1988).…”
Section: General Model Behaviormentioning
confidence: 99%
“…Reaction conditions: HCOj (50 mM); acetate-(10 mM); SO,'-(10 mM); HS-(10 mM); CH2 (50 kPa); pH 7; 25°C. k,,,,, and v,,, values are assumed to be identical for all runs (1 mM and 2 mM/ h, respectively) "leftover" substrate has been established for bacterial hydrogen degradation in natural environments (Cord-Ruwisch et al, 1988;Giraldo-Gomez et al, 1992;Michaelis and Menten, 1913). Hydrogen is a major intermediate compound in the anaerobic degradation of organic matter.…”
Section: Time Course Prediction Of Reactions Approaching Equilibriummentioning
The classical Michaelis‐Menten model is widely used as the basis for modeling of a number of biological systems. As the model does not consider the inhibitory effect of endproducts that accumulate in virtually all bioprocesses, it is often modified to prevent the overestimation of reaction rates when products have accumulated. Traditional approaches of model modification use the inclusion of irreversible, competitive, and noncompetitive inhibition factors. This article demonstrates that these inhibition factors are insufficient to predict product inhibition of reactions that are close the dynamic equilibrium. All models investigated were found to violate thermodynamic laws as they predicted positive reaction rates for reactions that were endergonic due to high endproduct concentrations. For modeling of biological processes that operate close to the dynamic equilibrium (e.g., anaerobic processes), it is critical to prevent the prediction of positive reaction rates when the reaction has already reached the dynamic equilibrium. This can be achieved by using a reversible kinetic model. However, the major drawback of the reversible kinetic model is the large number of empirical parameters it requires. These parameters are difficult to determine and prone to experimental error. For this reason, the reversible model is not practical in the modeling of biological processes.
This article uses the fundamentals of steady‐state kinetics and thermodynamics to establish an equation for the reversible kinetic model that is of practical use in bio‐process modeling. The behavior of this equilibrium‐based model is compared with Michaelis‐Menten‐based models that use traditional inhibition factors. The equilibrium‐based model did not require any empirical inhibition factor to correctly predict when reaction rates must be zero due to the free energy change being zero. For highly exergonic reactions, the equilibrium‐based model did not deviate significantly from the Michaelis‐Menten model, whereas, for reactions close to equilibrium, the reaction rate was mainly controlled by the quotient of mass action ratio (concentration of all products over concentration of all substrates) over the equilibrium constant K. This quotient is a measure of the displacement of the reaction from its equilibrium. As the new equation takes into account all of the substrates and products, it was able to predict the inhibitor effect of multiple endproducts. The model described is designed to be a useful basis for a number of different model applications where reaction conditions are close to equilibrium.
“…Thus, substrate threshold values appear to be a new and important parameter that may significantly influence anaerobic reactions such as hydrogen oxidation. The use of the H2 threshold value in kinetic models has also been proposed recently (Giraldo-Gomez et al, 1992). When the concentration of the substrates is far higher than that of the products and the reaction driving force is large, the ratio T/K becomes very small and the model predictions approach those of the classical Michaelis-Menten model (see also Figs.…”
Section: General Model Behaviormentioning
confidence: 97%
“…These threshold values indicated that bacterial reaction kinetics do not necessarily result in the complete utilization of the substrate. The existence of hydrogen threshold values indicated that v,,, values and k , values alone may not be sufficient to predict the efficiency of utilization of low hydrogen concentrations (Giraldo-Gomez et al, 1992;Lovley, 1985). These hydrogen threshold values were found to be related to the free energy change of the reaction (Cord-Ruwisch et al, 1988).…”
Section: General Model Behaviormentioning
confidence: 99%
“…Reaction conditions: HCOj (50 mM); acetate-(10 mM); SO,'-(10 mM); HS-(10 mM); CH2 (50 kPa); pH 7; 25°C. k,,,,, and v,,, values are assumed to be identical for all runs (1 mM and 2 mM/ h, respectively) "leftover" substrate has been established for bacterial hydrogen degradation in natural environments (Cord-Ruwisch et al, 1988;Giraldo-Gomez et al, 1992;Michaelis and Menten, 1913). Hydrogen is a major intermediate compound in the anaerobic degradation of organic matter.…”
Section: Time Course Prediction Of Reactions Approaching Equilibriummentioning
The classical Michaelis‐Menten model is widely used as the basis for modeling of a number of biological systems. As the model does not consider the inhibitory effect of endproducts that accumulate in virtually all bioprocesses, it is often modified to prevent the overestimation of reaction rates when products have accumulated. Traditional approaches of model modification use the inclusion of irreversible, competitive, and noncompetitive inhibition factors. This article demonstrates that these inhibition factors are insufficient to predict product inhibition of reactions that are close the dynamic equilibrium. All models investigated were found to violate thermodynamic laws as they predicted positive reaction rates for reactions that were endergonic due to high endproduct concentrations. For modeling of biological processes that operate close to the dynamic equilibrium (e.g., anaerobic processes), it is critical to prevent the prediction of positive reaction rates when the reaction has already reached the dynamic equilibrium. This can be achieved by using a reversible kinetic model. However, the major drawback of the reversible kinetic model is the large number of empirical parameters it requires. These parameters are difficult to determine and prone to experimental error. For this reason, the reversible model is not practical in the modeling of biological processes.
This article uses the fundamentals of steady‐state kinetics and thermodynamics to establish an equation for the reversible kinetic model that is of practical use in bio‐process modeling. The behavior of this equilibrium‐based model is compared with Michaelis‐Menten‐based models that use traditional inhibition factors. The equilibrium‐based model did not require any empirical inhibition factor to correctly predict when reaction rates must be zero due to the free energy change being zero. For highly exergonic reactions, the equilibrium‐based model did not deviate significantly from the Michaelis‐Menten model, whereas, for reactions close to equilibrium, the reaction rate was mainly controlled by the quotient of mass action ratio (concentration of all products over concentration of all substrates) over the equilibrium constant K. This quotient is a measure of the displacement of the reaction from its equilibrium. As the new equation takes into account all of the substrates and products, it was able to predict the inhibitor effect of multiple endproducts. The model described is designed to be a useful basis for a number of different model applications where reaction conditions are close to equilibrium.
“… 1, Vroblesky and colleagues (1997); 2, Christensen and colleagues (2000); 3, Lovley and colleagues (1982); 4, Conrad and colleagues (1985); 5, Lovley and Goodwin (1988); 6, Hoehler and colleagues (1998); 7, Jakobsen and colleagues (1998); 8, Hoehler and colleagues (2002); 9, Roden and Wetzel (2003); 10, Kaspar and Wuhrmann (1978); 11, Fukuzaki and colleagues (1990); 12, Warikoo and colleagues (1996); 13, Hoh and Cord‐Ruwisch (1997); 14, Fennell and Gossett (1998); 15, Kristjansson and colleagues (1982); 16, Robinson and Tiedje (1984); 17, Dwyer and colleagues (1988); 18, Westermann and colleagues (1989); 19, Giraldo‐Gomez and colleagues (1992); 20, Schmidt and Ahring (1993); 21, Smatlak and colleagues (1996); 22, Ballapragada and colleagues (1997); 23, Voolapalli and Stuckey (1999); 24, van Bodegom and Scholten (2001); 25, Oude Elferink and colleagues (1998); 26, Scholten and colleagues (2002); 27, Ahring and Westermann (1988); 28, Boone and colleagues (1989); 29, Kleerebezem and Stams (2000); 30, Dolfing and Tiedje (1988); 31, Beaty and McInerney (1989). …”
Identification of the functional groups of microorganisms that are predominantly in control of fluxes through, and concentrations in, microbial networks would benefit microbial ecology and environmental biotechnology: the properties of those controlling microorganisms could be studied or monitored specifically or their activity could be modulated in attempts to manipulate the behaviour of such networks. Herein we present ecological control analysis (ECA) as a versatile mathematical framework that allows for the quantification of the control of each functional group in a microbial network on its process rates and concentrations of intermediates. In contrast to current views, we show that rates of flow of matter are not always limited by a single functional group; rather flux control can be distributed over several groups. Also, control over intermediate concentrations is always shared. Because of indirect interactions, through other functional groups, the concentration of an intermediate can also be controlled by functional groups not producing or consuming it. Ecological control analysis is illustrated by a case study on the anaerobic degradation of organic matter, using experimental data obtained from the literature. During anaerobic degradation, fermenting microorganisms interact with terminal electron-accepting microorganisms (e.g. halorespirers, methanogens). The analysis indicates that flux control mainly resides with fermenting microorganisms, but can shift to the terminal electron-accepting microorganisms under less favourable redox conditions. Paradoxically, halorespiring microorganisms do not control the rate of perchloroethylene and trichloroethylene degradation even though they catalyse those processes themselves.
“…The biological part of the model is described by the biomass production dependencies. In the literature (e.g., Angelidaki et al, 1993;Buffiere et al, 1995;Giraldo-Gomez et al, 1992) there is evidence to suggest that Monod-type kinetics would be sufficient to portray acidogenic growth on glucose, growth of the syntrophic association on hydrogen, and methanogenic growth on acetate,…”
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