"...a concise introduction to enzyme kinetics...recommended..." (Choice, Vol. 40, No. 9, May 2003)
"...extremely useful...particularly...the applications and practical examples to demonstrate the mathematical handling of experimental data..." (Clinical Chemistry, Vol. 49, No. 5, 2003)
"...a valuable companion for everyone seeking a quick overview of the theory of enzyme kinetics." (Angewandte Chemie International Edition , Vol. 42, 2003)
"...a useful manual for the experienced researcher and the developing enzymologist." (Journal of Food Quality, Vol. 26)
"...an innovative approach to the field of enzyme kinetic modeling, model transformation, and model analysis...a valuable reference for practitioners in this field." (SIM News, Vol. 53, No. 3, May/June 2003)
"...presents a new way of looking at an old subject... (Angewandte Chemie, 2003)
"...the approach is quite mathematical and will appeal to the reader versed in mathematics and kinetics..." (ChemBioChem, Vol 4(5), 2003)
As catalysts for the majority of metabolic and biochemical reactions in the body, enzymes are important drug targets as well as useful synthetic catalysts. Enzyme kinetics is the study of the speed of an enzyme-catalyzed reaction and provides useful knowledge that aids in the design of enzyme-based processes. A. G. Marangonis Enzyme Kinetics: A Modern Approach provides a practical, how-to guide for students, technicians, and nonspecialists to evaluate enzyme kinetics, using common software packages to perform easy enzymatic analyses.
The treatment of enzyme kinetics in this book is radically different from the way the topic is traditionally covered. Marangoni stresses an understanding of how researchers arrive at models, what the models limitations are, and how they can be used in practical ways to analyze enzyme kinetic data. With the advent of computers, linear transformations of models have become unnecessaryEnzyme Kinetics does away with all linear transformations of enzyme kinetic models, advancing the use of nonlinear regression techniques. Marangoni develops new ways to carry out analyses of enzyme kinetic data, particularly in the study of pH effects on catalytic activity and multisubstrate enzymes. Other topics addressed include:
Enzyme Kinetics is a handy, innovative resource for practicing researchers in the chemical, pharmaceutical, and food science industries.
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Most Helpful Customer Reviews
12 of 14 people found the following review helpful:
3.0 out of 5 stars
Modern?,
By
This review is from: Enzyme Kinetics: A Modern Approach (Hardcover)
The author has a view of how to analyse enzyme kinetic experiments that is very different from mine, but there are many who share his opinions, not mine, and many readers will find this an honourable and useful addition to the bookshelf. What the title means by "modern" is explained in the blurb: "With the advent of computers, linear transformations of models have become unnecessary -- Enzyme Kinetics does away with all linear transformations of enzyme kinetic models, advancing the use of nonlinear regression techniques." As I said, this view is not unique to the author, and those who share it will doubtless be glad to see the double-reciprocal plot and all the others consigned to the dustbin of history.
To me it is a case of throwing out the baby with the bath water. Yes, non-linear regression is a good step forward, but it needs to be applied with discretion, and it is dangerous to forget that even if computers are much better than humans at some tasks, they are worse at others. In particular, humans are much better than computers at noticing anomalies, but to do this they need something to look at, in other words a graph, and it is much easier to recognize an anomaly in the graph where the points ought to fall on a straight line than in one way they should fall on a curve.
2 of 3 people found the following review helpful:
3.0 out of 5 stars
Use all the modern tools or stick with linear,
By moderate user (Kentucky) - See all my reviews
This review is from: Enzyme Kinetics: A Modern Approach (Hardcover)
If you do nonlinear analyses, you must also use residual graphs. They are much better than eyeballing whether a graph is linear or eyeballing if the data seem to fit a curve.
If you cannot do residual graphs, you should not use nonlinear analyses and stick with linear graphs, though even they are more dependable with the addition of a residual graph to check how well the data and equation fit. If you can do residuals, nonlinear analyses are faster, but not necessarily better in many cases. So, the reader should choose whether to use the author's method based on whether it is appropriate for their circumstances.
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