Misuse of the Michaelis-Menten rate law for protein interaction networks and its remedy

For over a century, the Michaelis-Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right...

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Bibliographic Details
Published in:PLoS computational biology 2020-10, Vol.16 (10), p.e1008258-e1008258
Main Authors: Kim, Jae Kyoung, Tyson, John J
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Animals
Biology and Life Sciences
Bistability
Chemical reactions
Computer and Information Sciences
Computer applications
Computer Simulation - standards
Enzyme kinetics
Errors, Scientific
Gene expression
Interactomes
Kinases
Kinetics
Legislation
Models, Statistical
Ordinary differential equations
Oscillations
Parameter estimation
Prevention
Protein Interaction Mapping - standards
Protein Interaction Maps - physiology
Proteins
Quasi-steady states
Reproducibility of Results
Review
Stochastic Processes
Stochasticity
Substrates
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Summary:For over a century, the Michaelis-Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right condition, namely, that the substrate is in large excess over the enzyme-substrate complex. Unfortunately, in circumstances where its validity condition is not satisfied, especially so in protein interaction networks, the MM rate law has frequently been misused. In this review, we illustrate how inappropriate use of the MM rate law distorts the dynamics of the system, provides mistaken estimates of parameter values, and makes false predictions of dynamical features such as ultrasensitivity, bistability, and oscillations. We describe how these problems can be resolved with a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA). Furthermore, we show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost than using the full set of mass-action rate laws. This review describes how to use quasi-steady state approximations in the right context, to prevent drawing erroneous conclusions from in silico simulations.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1008258