ArticleiScience2024
HetMM: A Michaelis-Menten model for non-homogeneous enzyme mixtures.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework.Patterns (New York, N.Y.) · 2026Article
- Tyrosinase Recovered from White Button Mushroom Waste: Extraction, Characterization, and Application in Casein Cross-Linking.Journal of agricultural and food chemistry · 2026Article
- Synthesis and Biological Activity Characterization of Vascular Endothelial Growth Factor Using an Optimized Wheat Germ Cell-Free System.Current issues in molecular biology · 2026Article
- Ethanol production from lignocellulosic waste materials: kinetics and optimization studies.RSC advances · 2025Article
- Structural Enzymology, Phylogenetics, Differentiation, and Symbolic Reflexivity at the Dawn of Biology.Genome biology and evolution · 2025Article
- Liquid Crystal Microcavity Biosensors for Real-Time Liver Injury Monitoring via Whispering Gallery Mode Laser.Research (Washington, D.C.) · 2025Article
- Base Pairing Promoted the Self-Organization of Genetic Coding, Catalysis, and Free-Energy Transduction.Life (Basel, Switzerland) · 2024Review
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Michaelis-Menten model requires its reaction velocities to come from a preparation of homogeneous enzymes, with identical or near-identical catalytic activities. However, this condition is not always met. We introduce a kinetic model that relaxes this requirement, by assuming there are an unknown number of enzyme species drawn from a probability distribution whose standard deviation is estimated. Through simulation studies, we demonstrate the method accurately discriminates between homogeneous and heterogeneous data, even with moderate levels of experimental error. We applied this model to three homogeneous and three heterogeneous biological systems, showing that the standard and heterogeneous models outperform respectively. Lastly, we show that heterogeneity is not readily distinguished from negatively cooperative binding under the Hill model. These two distinct attributes-inequality in catalytic ability and interference between binding sites-yield similar Michaelis-Menten curves that are not readily resolved without further experimentation. Our user-friendly software package allows homogeneity testing and parameter estimation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.