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Kinetic modelling: an integrated approach to analyze enzyme activity assays

Overview of attention for article published in Plant Methods, August 2017
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Title
Kinetic modelling: an integrated approach to analyze enzyme activity assays
Published in
Plant Methods, August 2017
DOI 10.1186/s13007-017-0218-y
Pubmed ID
Authors

Jelena Boeckx, Maarten Hertog, Annemie Geeraerd, Bart Nicolai

Abstract

In general, enzyme activity is estimated from spectrophotometric data, by taking the slope of the linear part of the progress curve describing the rate of change in the substrate or product monitored. As long as the substrate concentrations are sufficiently high to saturate the enzyme and, the velocity of the catalyzed reaction is directly proportional to the enzyme concentration. Under these premises, this velocity can be taken as a measure of the amount of active enzyme present. Estimation of the enzyme activity through linear regression of the data should only be applied when linearity is true, which is often not the case or has not been checked. In this paper, we propose a more elaborate method, based on a kinetic modelling approach, to estimate the in vitro specific enzyme activity from spectrophotometric assay data. As a case study, kinetic models were developed to estimate the activity of the enzymes pyruvate decarboxylase and alcohol dehydrogenase extracted from 'Jonagold' apple (Malus x domestica Borkh. cv. 'Jonagold'). The models are based on Michaelis-Menten and first order kinetics, which describe the reaction mechanism catalyzed by the enzymes. In contrast to the linear regression approach, the models can be used to estimate the enzyme activity regardless of whether linearity is achieved since they integrally take into account the complete progress curve. The use of kinetic models to estimate the enzyme activity can be applied to all other enzymes as long as the underlying reaction mechanism is known. The kinetic models can also be used as a tool to optimize the enzyme assays by systematically studying the effect of the various design parameters.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 201 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 51 25%
Student > Ph. D. Student 23 11%
Student > Master 17 8%
Researcher 15 7%
Student > Postgraduate 7 3%
Other 17 8%
Unknown 71 35%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 42 21%
Agricultural and Biological Sciences 25 12%
Chemistry 13 6%
Chemical Engineering 12 6%
Immunology and Microbiology 7 3%
Other 21 10%
Unknown 81 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 August 2017.
All research outputs
#13,290,035
of 23,140,503 outputs
Outputs from Plant Methods
#601
of 1,096 outputs
Outputs of similar age
#153,254
of 316,915 outputs
Outputs of similar age from Plant Methods
#16
of 21 outputs
Altmetric has tracked 23,140,503 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,096 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.4. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,915 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.