Michaelis–Menten kinetics

In biochemistry, Michaelis–Menten kinetics is one of the best-known models of enzyme kinetics.[1][2] It is named after German biochemist Leonor Michaelis and Canadian physician Maud Menten.[3] The model takes the form of an equation describing the rate of enzymatic reactions, by relating reaction rate $v$ (rate of formation of product, $[{\ce {P}}]$ ) to $[{\ce {S}}]$ , the concentration of a substrate S. Its formula is given by

v={\frac {\mathrm {d} [{\ce {P}}]}{\mathrm {d} t}}=V_{\max }{\frac {[{\ce {S}}]}{K_{\mathrm {M} }+[{\ce {S}}]}}

Michaelis–Menten saturation curve for an enzyme reaction showing the relation between the substrate concentration and reaction rate.

This equation is called the Michaelis–Menten equation. Here, $V_{\max }$ represents the maximum rate achieved by the system, happening at saturating substrate concentration for a given enzyme concentration. When the value of the Michaelis constant $K_{\mathrm {M} }$ is numerically equal to the substrate concentration, then the reaction rate is half of $V_{\max }$ .[4] Biochemical reactions involving a single substrate are often assumed to follow Michaelis–Menten kinetics, without regard to the model's underlying assumptions.

Model

Change in concentrations over time for enzyme E, substrate S, complex ES and product P

In 1901, French physical chemist Victor Henri found that enzyme reactions were initiated by a bond (more generally, a binding interaction) between the enzyme and the substrate.[5] His work was taken up by German biochemist Leonor Michaelis and Canadian physician Maud Menten, who investigated the kinetics of an enzymatic reaction mechanism, invertase, that catalyzes the hydrolysis of sucrose into glucose and fructose.[6] In 1913, they proposed a mathematical model of the reaction.[7] It involves an enzyme, E, binding to a substrate, S, to form a complex, ES, which in turn releases a product, P, regenerating the original enzyme. This may be represented schematically as

{\ce {E{}+S<=>[{\mathit {k_{f}}}][{\mathit {k_{r}}}]ES->[k_{\ce {cat}}]E{}+P}}

where $k_{f}$ (forward rate constant), $k_{r}$ (reverse rate constant), and $k_{\mathrm {cat} }$ (catalytic rate constant) denote the rate constants,[8] the double arrows between S (substrate) and ES (enzyme-substrate complex) represent the fact that enzyme-substrate binding is a reversible process, and the single forward arrow represents the formation of P (product).

Under certain assumptions – such as the enzyme concentration being much less than the substrate concentration – the rate of product formation is given by

v={\frac {\mathrm {d} [{\ce {P}}]}{\mathrm {d} t}}=V_{\max }{\frac {[{\ce {S}}]}{K_{\mathrm {M} }+[{\ce {S}}]}}=k_{\mathrm {cat} }[{\ce {E}}]_{0}{\frac {[{\ce {S}}]}{K_{\mathrm {M} }+[{\ce {S}}]}}.

The reaction order depends on the relative size of the two terms in the denominator. At low substrate concentration $[{\ce {S}}]\ll K_{M}$ , so that the reaction rate $v=k_{\mathrm {cat} }[{\ce {E}}]_{0}{\frac {[{\ce {S}}]}{K_{\mathrm {M} }}}$ varies linearly with substrate concentration ${\ce {[S]}}$ (first-order kinetics).[9] However at higher ${\ce {[S]}}$ with $[{\ce {S}}]\gg K_{M}$ , the reaction becomes independent of ${\ce {[S]}}$ (zero-order kinetics)[9] and asymptotically approaches its maximum rate $V_{\max }=k_{\ce {cat}}[{\ce {E}}]_{0}$ , where ${\ce {[E]_0}}$ is the initial enzyme concentration. This rate is attained when all enzyme is bound to substrate. $k_{\mathrm {cat} }$ , the turnover number, is the maximum number of substrate molecules converted to product per enzyme molecule per second. Further addition of substrate does not increase the rate which is said to be saturated.

The value of the Michaelis constant $K_{\mathrm {M} }$ is numerically equal to the ${\ce {[S]}}$ at which the reaction rate is at half-maximum,[4] and is a measure of the substrate's affinity for the enzyme—a small $K_{\mathrm {M} }$ indicates high affinity, meaning that the rate will approach $V_{\max }$ with lower ${\ce {[S]}}$ than those reactions with a larger $K_{\mathrm {M} }$ .[10] The constant is not affected by the concentration or purity of an enzyme.[11] The value of $K_{\mathrm {M} }$ is dependent on both the identity of enzyme and that of the substrate, as well as conditions such as temperature and pH.[12]

The model is used in a variety of biochemical situations other than enzyme-substrate interaction, including antigen–antibody binding, DNA–DNA hybridization, and protein–protein interaction.[10][13] It can be used to characterise a generic biochemical reaction, in the same way that the Langmuir equation can be used to model generic adsorption of biomolecular species.[13] When an empirical equation of this form is applied to microbial growth, it is sometimes called a Monod equation.

Applications

Parameter values vary widely between enzymes:[14]

Enzyme	$K_{\mathrm {M} }$ (M)	$k_{\text{cat}}$ (s⁻¹)	$k_{\text{cat}}/K_{\mathrm {M} }$ (M⁻¹s⁻¹)
Chymotrypsin	1.5 × 10⁻²	0.14	9.3
Pepsin	3.0 × 10⁻⁴	0.50	1.7 × 10³
T-RNA synthetase	9.0 × 10⁻⁴	7.6	8.4 × 10³
Ribonuclease	7.9 × 10⁻³	7.9 × 10²	1.0 × 10⁵
Carbonic anhydrase	2.6 × 10⁻²	4.0 × 10⁵	1.5 × 10⁷
Fumarase	5.0 × 10⁻⁶	8.0 × 10²	1.6 × 10⁸

The constant $k_{\text{cat}}/K_{\mathrm {M} }$ (catalytic efficiency) is a measure of how efficiently an enzyme converts a substrate into product. Diffusion limited enzymes, such as fumarase, work at the theoretical upper limit of 10⁸ – 10¹⁰ M⁻¹s⁻¹, limited by diffusion of substrate into the active site.[15]

Michaelis–Menten kinetics have also been applied to a variety of spheres[1] outside of biochemical reactions,[8] including alveolar clearance of dusts,[16] the richness of species pools,[17] clearance of blood alcohol,[18] the photosynthesis-irradiance relationship, and bacterial phage infection.[19]

The equation can also be used to describe the relationship between ion channel conductivity and ligand concentration.[20]

Biological Oceanography

Michaelis Menten can also be applied to limiting nutrients and phytoplankton growth in the global ocean. The relationship is

V={\frac {V_{\max }S}{K_{\mathrm {M} }+S}}

where V is uptake of the nutrient by phytoplankton, S is the concentration of the limiting nutrient, Vmax is the maximum uptake rate by phytoplankton, and K is the half saturation constant.[21] The relationship can also be understood for growth rate [22] Vmax and K also depend on phytoplankton physiology and genetics. Oligotrophic regions—regions that are nutrient depleted—often have phytoplankton populations characterized by low Vmax and low K, which help them to efficiently take up the nutrient that is at low concentrations. These phytoplankton tend to be small, with large area to volume ratios. Phytoplankton in eutrophic regions—nutrient-replete regions—are often characterized by high Vmax and K, which make them less efficient at taking up a given nutrient, but allows them to take up more of that nutrient. These phytoplankton tend to be large (e.g. diatoms) with a smaller area to volume ratio.[23][24] Their larger Vmax enables them to become larger. This relationship is generally applied to nitrate concentrations, as that is the limiting nutrient in much of the ocean, but other nutrients—for example, iron and phosphate—might also be limiting.

Derivation

Applying the law of mass action, which states that the rate of a reaction is proportional to the product of the concentrations of the reactants (i.e. $[E][S]$ ), gives a system of four non-linear ordinary differential equations that define the rate of change of reactants with time $t$ [25]

{\begin{aligned}{\frac {\mathrm {d} [{\ce {E}}]}{\mathrm {d} t}}&=-k_{f}[{\ce {E}}][{\ce {S}}]+k_{r}[{\ce {ES}}]+k_{\mathrm {cat} }[{\ce {ES}}]\\[4pt]{\frac {\mathrm {d} [{\ce {S}}]}{\mathrm {d} t}}&=-k_{f}[{\ce {E}}][{\ce {S}}]+k_{r}[{\ce {ES}}]\\[4pt]{\frac {\mathrm {d} [{\ce {ES}}]}{\mathrm {d} t}}&=k_{f}[{\ce {E}}][{\ce {S}}]-k_{r}[{\ce {ES}}]-k_{\mathrm {cat} }[{\ce {ES}}]\\[4pt]{\frac {\mathrm {d} [{\ce {P}}]}{\mathrm {d} t}}&=k_{\mathrm {cat} }[{\ce {ES}}].\end{aligned}}

In this mechanism, the enzyme E is a catalyst, which only facilitates the reaction, so that its total concentration, free plus combined, $[E]+[ES]=[E]_{0}$ is a constant (i.e. $[E]_{0}=[E]_{total}$ ). This conservation law can also be observed by adding the first and third equations above.[25][26]

Equilibrium approximation

In their original analysis, Michaelis and Menten assumed that the substrate is in instantaneous chemical equilibrium with the complex, which implies[7][26]

k_{f}[{\ce {E}}][{\ce {S}}]=k_{r}[{\ce {ES}}].

From the enzyme conservation law, we obtain[26]

[{\ce {E}}]=[{\ce {E}}]_{0}-[{\ce {ES}}].

Combining the two expressions above, gives us

{\begin{aligned}k_{f}([{\ce {E}}]_{0}-[{\ce {ES}}])[{\ce {S}}]&=k_{r}[{\ce {ES}}]\\[4pt]k_{f}[{\ce {E}}]_{0}[{\ce {S}}]-k_{f}[{\ce {ES}}][{\ce {S}}]&=k_{r}[{\ce {ES}}]\\[4pt]k_{r}[{\ce {ES}}]+k_{f}[{\ce {ES}}][{\ce {S}}]&=k_{f}[{\ce {E}}]_{0}[{\ce {S}}]\\[4pt][{\ce {ES}}](k_{r}+k_{f}[{\ce {S}}])&=k_{f}[{\ce {E}}]_{0}[{\ce {S}}]\\[4pt][{\ce {ES}}]&={\frac {k_{f}[{\ce {E}}]_{0}[{\ce {S}}]}{k_{r}+k_{f}[{\ce {S}}]}}\\[4pt][{\ce {ES}}]&={\frac {k_{f}[{\ce {E}}]_{0}[{\ce {S}}]}{k_{f}({\frac {k_{r}}{k_{f}}}+[{\ce {S}}])}}\\[4pt]\end{aligned}}

Upon simplification, we get

[{\ce {ES}}]={\frac {[{\ce {E}}]_{0}[S]}{K_{d}+[{\ce {S}}]}}

where $K_{d}=k_{r}/k_{f}$ is the dissociation constant for the enzyme-substrate complex. Hence the velocity $v$ of the reaction – the rate at which P is formed – is[26]

v={\frac {\mathrm {d} [{\ce {P}}]}{\mathrm {d} t}}=V_{\max }{\frac {[{\ce {S}}]}{K_{d}+[{\ce {S}}]}}

where $V_{\max }=k_{\mathrm {cat} }[{\ce {E}}]_{0}$ is the maximum reaction velocity.

Quasi-steady-state approximation

An alternative analysis of the system was undertaken by British botanist G. E. Briggs and British geneticist J. B. S. Haldane in 1925.[27][28] They assumed that the concentration of the intermediate complex does not change on the time-scale of product formation – known as the quasi-steady-state assumption or pseudo-steady-state-hypothesis. Mathematically, this assumption means $k_{f}[{\ce {E}}][{\ce {S}}]=k_{r}[{\ce {ES}}]+k_{\mathrm {cat} }[{\ce {ES}}]=(k_{r}+k_{\mathrm {cat} })[{\ce {ES}}]$ . This is mathematically the same as the previous equation, with $k_{r}$ replaced by $k_{r}+k_{\mathrm {cat} }$ . Hence, following the same steps as above, the velocity $v$ of the reaction is[26][28]

v=V_{\max }{\frac {[{\ce {S}}]}{K_{\mathrm {M} }+[{\ce {S}}]}}

where

K_{\mathrm {M} }={\frac {k_{r}+k_{\mathrm {cat} }}{k_{f}}}

is known as the Michaelis constant.[25]

Assumptions and limitations

The first step in the derivation applies the law of mass action, which is reliant on free diffusion. However, in the environment of a living cell where there is a high concentration of proteins, the cytoplasm often behaves more like a viscous gel than a free-flowing liquid, limiting molecular movements by diffusion and altering reaction rates.[29] Although the law of mass action can be valid in heterogeneous environments,[30] it is more appropriate to model the cytoplasm as a fractal, in order to capture its limited-mobility kinetics.[31]

The resulting reaction rates predicted by the two approaches are similar, with the only difference being that the equilibrium approximation defines the constant as $K_{d}$ , whilst the quasi-steady-state approximation uses $K_{\mathrm {M} }$ . However, each approach is founded upon a different assumption. The Michaelis–Menten equilibrium analysis is valid if the substrate reaches equilibrium on a much faster time-scale than the product is formed or, more precisely, that [26]

\varepsilon _{d}={\frac {k_{\mathrm {cat} }}{k_{r}}}\ll 1.

By contrast, the Briggs–Haldane quasi-steady-state analysis is valid if [25][32]

\varepsilon _{m}={\frac {\ce {[E]_{0}}}{[{\ce {S}}]_{0}+K_{\ce {M}}}}\ll 1.

Thus it holds if the enzyme concentration is much less than the substrate concentration or $K_{\mathrm {M} }$ or both.

In both the Michaelis–Menten and Briggs–Haldane analyses, the quality of the approximation improves as $\varepsilon \,\!$ decreases. However, in model building, Michaelis–Menten kinetics are often invoked without regard to the underlying assumptions.[26]

Importantly, while irreversibility is a necessary simplification in order to yield a tractable analytic solution, in the general case product formation is not in fact irreversible. The enzyme reaction is more correctly described as

{\ce {E{}+S<=>[{\mathit {k_{f_{1}}}}][{\mathit {k_{r_{1}}}}]ES<=>[{\mathit {k_{f_{2}}}}][{\mathit {k_{r_{2}}}}]E{}+P.}}

In general, the assumption of irreversibility is a good one in situations where one of the below is true:

1. The concentration of substrate(s) is very much larger than the concentration of products:

{\ce {[S]\gg [P].}}

This is true under standard in vitro assay conditions, and is true for many in vivo biological reactions, particularly where the product is continually removed by a subsequent reaction.

2. The energy released in the reaction is very large, that is

\Delta {G}\ll 0.

In situations where neither of these two conditions hold (that is, the reaction is low energy and a substantial pool of product(s) exists), the Michaelis–Menten equation breaks down, and more complex modelling approaches explicitly taking the forward and reverse reactions into account must be taken to understand the enzyme biology.

Determination of constants

The typical method for determining the constants $V_{\max }$ and $K_{\mathrm {M} }$ involves running a series of enzyme assays at varying substrate concentrations $[S]$ , and measuring the initial reaction rate $v_{0}$ . 'Initial' here is taken to mean that the reaction rate is measured after a relatively short time period, during which it is assumed that the enzyme-substrate complex has formed, but that the substrate concentration held approximately constant, and so the equilibrium or quasi-steady-state approximation remain valid.[32] By plotting reaction rate against concentration, and using nonlinear regression of the Michaelis–Menten equation, the parameters may be obtained.[33]

Before computing facilities to perform nonlinear regression became available, graphical methods involving linearisation of the equation were used. A number of these were proposed, including the Eadie–Hofstee diagram, Hanes–Woolf plot and Lineweaver–Burk plot; of these, the Hanes–Woolf plot is the most accurate.[33] However, while useful for visualization, all three methods distort the error structure of the data and are inferior to nonlinear regression.[34] Assuming a similar error $dv_{0}$ on $v_{0}$ , an inverse representation leads to an error of $dv_{0}/v_{0}^{2}$ on $1/v_{0}$ (Propagation of uncertainty). Without proper estimation of $dv_{0}$ values, linearisation should be avoided. In addition, regression analysis using Least squares assumes that errors are normally distributed, which is not valid after a transformation of $v_{0}$ values. Nonetheless, their use can still be found in modern literature.[35]

In 1997 Santiago Schnell and Claudio Mendoza suggested a closed form solution for the time course kinetics analysis of the Michaelis–Menten kinetics based on the solution of the Lambert W function.[36] Namely,

{\frac {[{\ce {S}}]}{K_{\mathrm {M} }}}=W(F(t))\,

where W is the Lambert W function and

F(t)={\frac {[{\ce {S}}]_{0}}{K_{\mathrm {M} }}}\exp \!\left({\frac {[{\ce {S}}]_{0}}{K_{\mathrm {M} }}}-{\frac {V_{\max }}{K_{\mathrm {M} }}}\,t\right)\,.

The above equation, known nowadays as the Schnell-Mendoza equation,[37] has been used to estimate $V_{\max }$ and $K_{\mathrm {M} }$ from time course data.[38][39]

Role of substrate unbinding

The Michaelis-Menten equation has been used to predict the rate of product formation in enzymatic reactions for more than a century. Specifically, it states that the rate of an enzymatic reaction will increase as substrate concentration increases, and that increased unbinding of enzyme-substrate complexes will decrease the reaction rate. While the first prediction is well established, the second is more elusive. Mathematical analysis of the effect of enzyme-substrate unbinding on enzymatic reactions at the single-molecule level has shown that unbinding of an enzyme from a substrate can reduce the rate of product formation under some conditions, but may also have the opposite effect. As substrate concentrations increase, a tipping point can be reached where an increase in the unbinding rate results in an increase, rather than a decrease, of the reaction rate. The results indicate that enzymatic reactions can behave in ways that violate the classical Michaelis-Menten equation, and that the role of unbinding in enzymatic catalysis still remains to be determined experimentally.[40]

References

Srinivasan, Bharath (2021-07-16). "A Guide to the Michaelis‐Menten equation: Steady state and beyond". The FEBS Journal. 289 (20): 6086–6098. doi:10.1111/febs.16124. ISSN 1742-464X. PMID 34270860.
Srinivasan, Bharath (18 March 2021). "Explicit Treatment of Non‐Michaelis‐Menten and Atypical Kinetics in Early Drug Discovery". ChemMedChem. 16 (6): 899–918. doi:10.1002/cmdc.202000791. PMID 33231926. S2CID 227157473.
Srinivasan, Bharath (2020-09-27). "Words of advice: teaching enzyme kinetics". The FEBS Journal. 288 (7): 2068–2083. doi:10.1111/febs.15537. ISSN 1742-464X. PMID 32981225.
"Substrate Concentration (Introduction to Enzymes)". www.worthington-biochem.com.
Henri, Victor (1903). Lois Générales de l'Action des Diastases. Paris: Hermann.
"Victor Henri". Whonamedit?. Retrieved 24 May 2011.
Michaelis, L.; Menten, M.L. (1913). "Die Kinetik der Invertinwirkung". Biochem Z. 49: 333–369. (recent translation, and an older partial translation)
Chen, W.W.; Neipel, M.; Sorger, P.K. (2010). "Classic and contemporary approaches to modeling biochemical reactions". Genes Dev. 24 (17): 1861–1875. doi:10.1101/gad.1945410. PMC 2932968. PMID 20810646.
Laidler K.J. and Meiser J.H. Physical Chemistry (Benjamin/Cummings 1982) p.430 ISBN 0-8053-5682-7
Lehninger, A.L.; Nelson, D.L.; Cox, M.M. (2005). Lehninger principles of biochemistry. New York: W.H. Freeman. ISBN 978-0-7167-4339-2.
J., Ninfa, Alexander (1998). Fundamental laboratory approaches for biochemistry and biotechnology. Ballou, David P. Bethesda, Md.: Fitzgerald Science Press. ISBN 978-1891786006. OCLC 38325074.
"Km & Vmax". mofetsrv.mofet.macam98.ac.il. Retrieved 2017-12-18.
Chakraborty, S. (23 Dec 2009). Microfluidics and Microfabrication (1 ed.). Springer. ISBN 978-1-4419-1542-9.
Mathews, C.K.; van Holde, K.E.; Ahern, K.G. (10 Dec 1999). Biochemistry (3 ed.). Prentice Hall. ISBN 978-0-8053-3066-3.
Stroppolo, M.E.; Falconi, M.; Caccuri, A.M.; Desideri, A. (Sep 2001). "Superefficient enzymes". Cell Mol Life Sci. 58 (10): 1451–60. doi:10.1007/PL00000788. PMID 11693526. S2CID 24874575.
Yu, R.C.; Rappaport, S.M. (1997). "A lung retention model based on Michaelis–Menten-like kinetics". Environ Health Perspect. 105 (5): 496–503. doi:10.1289/ehp.97105496. PMC 1469867. PMID 9222134.
Keating, K.A.; Quinn, J.F. (1998). "Estimating species richness: the Michaelis–Menten model revisited". Oikos. 81 (2): 411–416. doi:10.2307/3547060. JSTOR 3547060.
Jones, A.W. (2010). "Evidence-based survey of the elimination rates of ethanol from blood with applications in forensic casework". Forensic Sci Int. 200 (1–3): 1–20. doi:10.1016/j.forsciint.2010.02.021. PMID 20304569.
Abedon, S.T. (2009). "Kinetics of phage-mediated biocontrol of bacteria". Foodborne Pathog Dis. 6 (7): 807–15. doi:10.1089/fpd.2008.0242. PMID 19459758.
Ding, Shinghua; Sachs, Frederick (1999). "Single Channel Properties of P2X2 Purinoceptors". The Journal of General Physiology. 113 (5): 695–720. doi:10.1085/jgp.113.5.695. PMC 2222910. PMID 10228183.
Dugdale, RCJ (1967). "Nutrient limitation in the sea: Dynamics, identification, and significance". Limnology and Oceanography. 12 (4): 685–695. Bibcode:1967LimOc..12..685D. doi:10.4319/lo.1967.12.4.0685.
Eppley, Richard W and Rogers, Jane N and McCarthy, James J (1969). "Half-saturation constants for uptake of nitrate and ammonium by marine phytoplankton". Limnology and Oceanography. 14 (6): 912–914. Bibcode:1969LimOc..14..912E. doi:10.4319/lo.1969.14.6.0912.{{cite journal}}: CS1 maint: multiple names: authors list (link)
Eppley, Richard W and Rogers, Jane N and McCarthy, James J (1969). "Half-saturation constants for uptake of nitrate and ammonium by marine phytoplankton". Limnology and Oceanography. 14 (6): 912–914. Bibcode:1969LimOc..14..912E. doi:10.4319/lo.1969.14.6.0912.{{cite journal}}: CS1 maint: multiple names: authors list (link)
Harrison, WG and Harris, LR and Irwin, BDJ (1996). "The kinetics of nitrogen utilization in the oceanic mixed layer: nitrate and ammonium interactions at nanomolar concentrations". Limnology and Oceanography. 41 (1): 6–32. Bibcode:1996LimOc..41...16H. doi:10.4319/lo.1996.41.1.0016.{{cite journal}}: CS1 maint: multiple names: authors list (link)
Murray, J.D. (2002). Mathematical Biology: I. An Introduction (3 ed.). Springer. ISBN 978-0-387-95223-9.
Keener, J.; Sneyd, J. (2008). Mathematical Physiology: I: Cellular Physiology (2 ed.). Springer. ISBN 978-0-387-75846-6.
Briggs, G.E.; Haldane, J.B.S. (1925). "A note on the kinematics of enzyme action". Biochem J. 19 (2): 338–339. doi:10.1042/bj0190338. PMC 1259181. PMID 16743508.
Laidler, Keith J. (1978). Physical Chemistry with Biological Applications. Benjamin/Cummings. pp. 428–430. ISBN 0-8053-5680-0.
Zhou, H.X.; Rivas, G.; Minton, A.P. (2008). "Macromolecular crowding and confinement: biochemical, biophysical, and potential physiological consequences". Annu Rev Biophys. 37 (1): 375–97. doi:10.1146/annurev.biophys.37.032807.125817. PMC 2826134. PMID 18573087.
Grima, R.; Schnell, S. (Oct 2006). "A systematic investigation of the rate laws valid in intracellular environments". Biophys Chem. 124 (1): 1–10. doi:10.1016/j.bpc.2006.04.019. PMID 16781049.
Schnell, S.; Turner, T.E. (2004). "Reaction kinetics in intracellular environments with macromolecular crowding: simulations and rate laws". Prog Biophys Mol Biol. 85 (2–3): 235–60. CiteSeerX 10.1.1.117.1997. doi:10.1016/j.pbiomolbio.2004.01.012. PMID 15142746.
Segel, L.A.; Slemrod, M. (1989). "The quasi-steady-state assumption: A case study in perturbation". SIAM Review. 31 (3): 446–477. doi:10.1137/1031091.
Leskovac, V. (2003). Comprehensive enzyme kinetics. New York: Kluwer Academic/Plenum Pub. ISBN 978-0-306-46712-7.
Greco, W.R.; Hakala, M.T. (1979). "Evaluation of methods for estimating the dissociation constant of tight binding enzyme inhibitors". J Biol Chem. 254 (23): 12104–12109. doi:10.1016/S0021-9258(19)86435-9. PMID 500698.
Hayakawa, K.; Guo, L.; Terentyeva, E.A.; Li, X.K.; Kimura, H.; Hirano, M.; Yoshikawa, K.; Nagamine, T.; et al. (2006). "Determination of specific activities and kinetic constants of biotinidase and lipoamidase in LEW rat and Lactobacillus casei (Shirota)". J Chromatogr B. 844 (2): 240–50. doi:10.1016/j.jchromb.2006.07.006. PMID 16876490.
Schnell, S.; Mendoza, C. (1997). "A closed form solution for time-dependent enzyme kinetics". Journal of Theoretical Biology. 187 (2): 207–212. Bibcode:1997JThBi.187..207S. doi:10.1006/jtbi.1997.0425.
Olp, M.D.; Kalous, K.S.; Smith, B.C. (2020). "ICEKAT: an interactive online tool for calculating initial rates from continuous enzyme kinetic traces". BMC Bioinformatics. 21: 186. doi:10.1186/s12859-020-3513-y. S2CID 218624836.
Goudar, C. T.; Sonnad, J. R.; Duggleby, R. G. (1999). "Parameter estimation using a direct solution of the integrated Michaelis–Menten equation". Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology. 1429 (2): 377–383. doi:10.1016/s0167-4838(98)00247-7. PMID 9989222.
Goudar, C. T.; Harris, S. K.; McInerney, M. J.; Suflita, J. M. (2004). "Progress curve analysis for enzyme and microbial kinetic reactions using explicit solutions based on the Lambert W function". Journal of Microbiological Methods. 59 (3): 317–326. doi:10.1016/j.mimet.2004.06.013. PMID 15488275.
Reuveni, Shlomi; Urbakh, Michael; Klafter, Joseph (2014). "Role of Substrate Unbinding in Michaelis-Menten Enzymatic Reactions". Proceedings of the National Academy of Sciences. 111 (12): 4391–4396. Bibcode:2014PNAS..111.4391R. doi:10.1073/pnas.1318122111. PMC 3970482. PMID 24616494.

External links

Online $K_{\mathrm {M} }$ $V_{\max }$ Vmax calculator (ic50.tk/kmvmax.html) based on the C programming language and the non-linear least-squares Levenberg–Marquardt algorithm of gnuplot
Alternative online $K_{\mathrm {M} }$ $V_{\max }$ calculator (ic50.org/kmvmax.html) based on Python, NumPy, Matplotlib and the non-linear least-squares Levenberg–Marquardt algorithm of SciPy

Enzymes
Activity	Active site Binding site Catalytic triad Oxyanion hole Enzyme promiscuity Diffusion-limited enzyme Coenzyme Cofactor Enzyme catalysis
Regulation	Allosteric regulation Cooperativity Enzyme inhibitor Enzyme activator
Classification	EC number Enzyme superfamily Enzyme family List of enzymes
Kinetics	Enzyme kinetics Eadie–Hofstee diagram Hanes–Woolf plot Lineweaver–Burk plot Michaelis–Menten kinetics
Types	EC1 Oxidoreductases (list) EC2 Transferases (list) EC3 Hydrolases (list) EC4 Lyases (list) EC5 Isomerases (list) EC6 Ligases (list) EC7 Translocases (list)