mm-fitting 1.1

Methods to compute Michaelis-Menten equation parameters and statistics.

Michaelis-Menten equation fitting to data

A Python module that implements five methods to fit the Michaelis-menten equation to a set of points of rate vs substrate concentration.

The methods are:

• Non-linear regression (Levemberg-Marquard algorithm applied to Michaelis-Menten equation)
• The Direct Linear Plot
• The Lineweaver-Burk linearization
• The Hanes linearization
• The Eddie-Hofstee linearization

Short usage:

Given two numpy 1D-arrays, a and v0 containing substrate concentrations and initial rates, respectively,

results = mm_fitting.compute_methods(a, v0)

will apply all five methods and generate a dict with keys a and v0 and results. The value of results will be a list of namedtuples containg the results for each method.

numpy-only dependency

All methods are implemented in numpy and do not require either scipy or data analysis module like pandas:

• linearizations are computed by a thin wrapper of numpy.polyfit() with degree one.
• non-linear regression is computed using a numpy-only version of the Levemberg-Marquard algorithm. Code was adapted from Abner Bogan's Github repo for a numpy-only version of the algorithm (abnerbog/ levenberg-marquardt-method ).