ArticleScientific reports2017
Beyond the Michaelis-Menten equation: Accurate and efficient estimation of enzyme kinetic parameters.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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60 citing papers in PubMed.
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- Optimizing enzyme inhibition analysis: precise estimation with a single inhibitor concentration.Nature communications · 2025Article
- Accuracy Versus Predominance: Reassessing the Validity of the Quasi-Steady-State Approximation.Bulletin of mathematical biology · 2025Article
- Building Localized NADP(H) Recycling Circuits to Advance Enzyme Cascadetronics.Angewandte Chemie (International ed. in English) · 2025Article
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3 authors.
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Abstract
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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