An aside from this week’s Unfinished Business.
Whilst writing yesterday’s post, I realised how often I (and I imagine many other modellers) resort to using “typical parameter values”, when no experimentally-determined numbers are available. If you read one of my papers, you’ll probably find a table listing these typical values and their provenance. But if you dig a little deeper, and follow these references back (all self-references, of course), you’ll eventually return to the paper you started with. Strange.
No, I don’t know where those numbers came from either. Still, we can derive them now using Brenda: a huge database of enzyme kinetic parameters. Database-level statistics in Brenda are presented as histograms; for example, there are over 105 experimentally-determined Michaelis-Menten constants KM, and we can use their statistics page to generate a histogram of the distribution of log10 KM:
We can calculate the mean and standard deviation of log10 KM from this curve, and then use a Taylor expansion to approximate the mean μ and standard deviation σ of KM. This process can be applied to all the functional parameters in Brenda:
I’m pleased (OK: surprised) to see that my typical, typical parameter values of KM = KI = 0.1 mM, and kcat = 10/s are close to the mark, though an order of magnitude estimate of KM = 1 mM might be a better choice in the future.