scholarly journals On the relationships between the Michaelis–Menten kinetics, reverse Michaelis–Menten kinetics, equilibrium chemistry approximation kinetics, and quadratic kinetics

2015 ◽  
Vol 8 (12) ◽  
pp. 3823-3835 ◽  
Author(s):  
J. Y. Tang

Abstract. The Michaelis–Menten kinetics and the reverse Michaelis–Menten kinetics are two popular mathematical formulations used in many land biogeochemical models to describe how microbes and plants would respond to changes in substrate abundance. However, the criteria of when to use either of the two are often ambiguous. Here I show that these two kinetics are special approximations to the equilibrium chemistry approximation (ECA) kinetics, which is the first-order approximation to the quadratic kinetics that solves the equation of an enzyme–substrate complex exactly for a single-enzyme and single-substrate biogeochemical reaction with the law of mass action and the assumption of a quasi-steady state for the enzyme–substrate complex and that the product genesis from enzyme–substrate complex is much slower than the equilibration between enzyme–substrate complexes, substrates, and enzymes. In particular, I show that the derivation of the Michaelis–Menten kinetics does not consider the mass balance constraint of the substrate, and the reverse Michaelis–Menten kinetics does not consider the mass balance constraint of the enzyme, whereas both of these constraints are taken into account in deriving the equilibrium chemistry approximation kinetics. By benchmarking against predictions from the quadratic kinetics for a wide range of substrate and enzyme concentrations, the Michaelis–Menten kinetics was found to persistently underpredict the normalized sensitivity ∂ ln v / ∂ ln k2+ of the reaction velocity v with respect to the maximum product genesis rate k2+, persistently overpredict the normalized sensitivity ∂ ln v / ∂ ln k1+ of v with respect to the intrinsic substrate affinity k1+, persistently overpredict the normalized sensitivity ∂ ln v / ∂ ln [E]T of v with respect the total enzyme concentration [E]T, and persistently underpredict the normalized sensitivity ∂ ln v / ∂ ln [S]T of v with respect to the total substrate concentration [S]T. Meanwhile, the reverse Michaelis–Menten kinetics persistently underpredicts ∂ ln v / ∂ ln k2+ and ∂ ln v / ∂ ln [E]T, and persistently overpredicts ∂ ln v / ∂ ln k1+ and ∂ ln v / ∂ ln [S]T. In contrast, the equilibrium chemistry approximation kinetics always gives consistent predictions of ∂ ln v / ∂ ln k2+, ∂ ln v / ∂ ln k1+, ∂ ln v / ∂ ln [E]T, and ∂ ln v / ∂ ln [S]T, indicating that ECA-based models will be more calibratable if the modeled processes do obey the law of mass action. Since the equilibrium chemistry approximation kinetics includes advantages from both the Michaelis–Menten kinetics and the reverse Michaelis–Menten kinetics and it is applicable for almost the whole range of substrate and enzyme abundances, land biogeochemical modelers therefore no longer need to choose when to use the Michaelis–Menten kinetics or the reverse Michaelis–Menten kinetics. I expect that removing this choice ambiguity will make it easier to formulate more robust and consistent land biogeochemical models.

2015 ◽  
Vol 8 (9) ◽  
pp. 7663-7691 ◽  
Author(s):  
J. Y. Tang

Abstract. The Michaelis–Menten kinetics and the reverse Michaelis–Menten kinetics are two popular mathematical formulations used in many land biogeochemical models to describe how microbes and plants would respond to changes in substrate abundance. However, the criteria of when to use which of the two are often ambiguous. Here I show that these two kinetics are special approximations to the Equilibrium Chemistry Approximation kinetics, which is the first order approximation to the quadratic kinetics that solves the equation of enzyme-substrate complex exactly for a single enzyme single substrate biogeochemical reaction with the law of mass action and the assumption of quasi-steady-state for the enzyme-substrate complex and that the product genesis from enzyme-substrate complex is much slower than the equilibration between enzyme-substrate complexes, substrates and enzymes. In particular, I showed that the derivation of the Michaelis–Menten kinetics does not consider the mass balance constraint of the substrate, and the reverse Michaelis–Menten kinetics does not consider the mass balance constraint of the enzyme, whereas both of these constraints are taken into account in the Equilibrium Chemistry Approximation kinetics. By benchmarking against predictions from the quadratic kinetics for a wide range of substrate and enzyme concentrations, the Michaelis–Menten kinetics was found to persistently under-predict the normalized sensitivity ∂ ln v / ∂ ln k2+ of the reaction velocity v with respect to the maximum product genesis rate k2+, persistently over-predict the normalized sensitivity ∂ ln v / ∂ ln k1+ of v with respect to the intrinsic substrate affinity k1+, persistently over-predict the normalized sensitivity ∂ ln v / ∂ ln [ E ]T of v with respect the total enzyme concentration [ E ]T and persistently under-predict the normalized sensitivity ∂ ln v / ∂ ln [ S ]T of v with respect to the total substrate concentration [ S ]T. Meanwhile, the reverse Michaelis–Menten kinetics persistently under-predicts ∂ ln v / ∂ ln k2+ and ∂ ln v / ∂ ln [ E ]T, and persistently over-predicts ∂ ln v / ∂ ln k1+ and ∂ ln v / ∂ ln [ S ]T. In contrast, the Equilibrium Chemistry Approximation kinetics always gives consistent predictions of ∂ ln v / ∂ ln k2+, ∂ ln v / ∂ ln k1+, ∂ ln v / ∂ ln [ E ]T and ∂ ln v / ∂ ln [ S ]T. Since the Equilibrium Chemistry Approximation kinetics includes the advantages from both the Michaelis–Menten kinetics and the reverse Michaelis–Menten kinetics and it is applicable for almost the whole range of substrate and enzyme abundances, soil biogeochemical modelers therefore no longer need to choose when to use the Michaelis–Menten kinetics or the reverse Michaelis–Menten kinetics. I expect removing this choice ambiguity will make it easier to formulate more robust and consistent land biogeochemical models.


1973 ◽  
Vol 62 (2) ◽  
pp. 203-223 ◽  
Author(s):  
Robert Mesibov ◽  
George W. Ordal ◽  
Julius Adler

Attractant was added to a suspension of bacteria (the background concentration of attractant) and then these bacteria were exposed to a yet higher concentration of attractant in a capillary. Chemotaxis was measured by determining how many bacteria accumulated in the capillary. The response range for chemotaxis lies between the threshold concentration and the saturating concentration. The breadth of this range is different for attractants detected by different chemoreceptors. Attractants detected by the same chemoreceptor can have their response ranges in widely different places. Over the center of the response range (on a logarithmic scale), bacteria give similar sized responses to similar fractional increases of concentration, i.e. they respond to ratios of attractant concentration, but the response peaks at the center of the range. The size of the response is different for attractants detected by different chemoreceptors. For a detectable response, a smaller increase in attractant concentration is needed for attractants detected by some chemoreceptors than for attractants detected by others. Although the data are inadequate, it appears that the Weber law may be observed over a wide range of concentrations for some attractants but not for others. In the Appendix we aim to explain some of these results in terms of the interaction of an attractant with its chemoreceptor according to the law of mass action.


2001 ◽  
pp. 121-128
Author(s):  
Bruce Hannon ◽  
Matthias Ruth

Author(s):  
Leonard Adleman ◽  
Manoj Gopalkrishnan ◽  
Ming-Deh Huang ◽  
Pablo Moisset ◽  
Dustin Reishus

1994 ◽  
pp. 73-79
Author(s):  
Bruce Hannon ◽  
Matthias Ruth

2021 ◽  
Author(s):  
Jinyun Tang ◽  
William Riley

<p>In ecosystem biogeochemistry, Liebig’s law of the minimum (LLM) is one of the most widely used rules to model and interpret biological growth. Although it is intuitively accepted as being true, its mechanistic foundation has never been clearly presented. We here first show that LLM can be derived from the law of mass action, the state of art theory for modeling biogeochemical reactions. We further show that there are (at least) another two approximations (the synthesizing unit (SU) model and additive model) that are more accurate than LLM in approximating the law of mass action. We then evaluated the LLM, SU, and additive models against growth data of algae and plants. For algae growth, we found all three models are equally accurate, albeit with different parameter values. For plants, LLM failed to accurately model one dataset, and achieved equally good results for other datasets with very different parameters. We also find that LLM does not allow flexible elemental stoichiometry, which is an oft-observed characteristic of plants, when an organism’s growth is modeled as a function of substrate uptake flux. In summary, we caution the use of LLM for modeling biological growth if one is interested in representing the organisms’ capability in adapting to different nutrient conditions.   </p> <p><br /><br /></p>


1938 ◽  
Vol 68 (1) ◽  
pp. 73-81 ◽  
Author(s):  
R. B. Pennell ◽  
I. F. Huddleson

It has been shown that the precipitation by the endoantigens of the three species of brucella of their homologous antibodies may be described by equations developed from the law of mass action. The endoantigens may be used for the accurate calibration of brucella antisera. The nitrogen-containing constituent of the endoantigens does not always seem to be intimately connected with the ability to precipitate the specific antibodies.


Sign in / Sign up

Export Citation Format

Share Document