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 ).
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Release files for mm-fitting 1.1
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