Enzyme Kinetics: Understanding Basic Concepts and Relevant Equations

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SUMMARY

This discussion focuses on enzyme kinetics, specifically the derivation and application of the Michaelis-Menten equation. The participants emphasize the importance of the steady state assumption in deriving the equation and suggest methods for extracting parameters Km and Vmax from experimental data. They recommend plotting the dataset for a two-parameter fit or using well-separated data points to solve for the parameters. The discussion confirms that minimal biological knowledge is needed, but a background in kinetics and equilibrium chemistry is beneficial.

PREREQUISITES
  • Kinetics and equilibrium chemistry
  • Understanding of the Michaelis-Menten equation
  • Basic graphing and data analysis skills
  • Familiarity with parameter fitting techniques
NEXT STEPS
  • Learn how to derive the Michaelis-Menten equation using the steady state assumption
  • Explore methods for parameter extraction from datasets, specifically Km and Vmax
  • Study graphical representation of enzyme kinetics data
  • Investigate software tools for curve fitting and data analysis
USEFUL FOR

Students studying biochemistry, researchers in enzyme kinetics, and anyone interested in mathematical modeling of biochemical reactions.

tunabeast
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Homework Statement



Hi, having not studied biology I'm struggling to get to grasps with what are probably basic concepts. Looking through the notes i have i was just wondering if the equations/derivations i'v attached are relevant, and if so how. A link to the powerpoint file can be found here http://www.megaupload.com/?d=V3DVK5JX . Here is the question I'm stuck on


http://i1.tinypic.com/6xizsyx.jpg
 
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tunabeast said:

Homework Statement



Hi, having not studied biology I'm struggling to get to grasps with what are probably basic concepts. Looking through the notes i have i was just wondering if the equations/derivations i'v attached are relevant, and if so how. A link to the powerpoint file can be found here http://www.megaupload.com/?d=V3DVK5JX . Here is the question I'm stuck onhttp://i1.tinypic.com/6xizsyx.jpg
I haven't actually downloaded and looked at the ppt file - I'm a little nervous about things like that. I'd probably be more likely to look at an image file uploaded to an image hosting site, but maybe others here are more daring. The best option would be if you just posted the work here, using the on-site \LaTeX capability.

For (a), it looks like you are asked to derive the Michaelis-Menten equation using d[E]/dt ~ d[ES]/dt -> very small (i.e., steady state assumption).

For (b), you must simply extract the 2 parameters in the MM equation from the dataset. You can do this by plotting the dataset and doing a 2-parameter fit with the MM equation. Or just use two far away data points from the set and solve for Km and max. rate, then check that this gives you accurate predictions for the other rates in the set.

PS: You need essentially no knowledge of biology for this, but kinetics and equilibrium chemistry are probably a pre-requisite.
 
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Thanks very much for the information. I completely understand about the download link, and i'v now attached a PDF file of the presentation. I think the answer to part a may well be on the second page. However unfortunately I'm still at a dead end on (b). Would it require plotting a graph? If so what kind and what would i be trying to interpret from the graph. Thanks again
 

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Your solution to part (a) is spot on!

Part (b) is just a little bit of math - you can find the answers the rigorous way (graphically, but probably unnecessary) or the easy way.

1. Take your final equation for \nu vs. .
2. Notice that the table gives you vs. \nu
3. Pick any two well-separated pairs from the table, say (0.00005, 0.000625) & (0.0003, 0.00167)
4. Plug each pair of values into the equation; you should end up with 2 equations in 2 unknowns (Km and v(max)).
5. Solve for these unknowns.
6. With the values you get for Km and v(max), plug in the other values of into the equation and check that you get answers pretty close to the corresponding values of \nu in the table.
 

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