mrbles.kinetics module

Kinetics Classes and Functions

This file stores the kinetics classes and functions for the MRBLEs Analysis module.

class mrbles.kinetics.GlobalFit

Bases: future.types.newobject.newobject

Global non-linear regression.

This class is based on the lmfit module pipeline. For mor information and functionalilty, please visit: https://lmfit.github.io/lmfit-py/

conf_int()

Return confidence intervals.

fit(concentrations, fit_all, fit_all_se)

Fit data.

fit_metrics

Return fit metrics.

static model_bind(Mt, Kd, Rmax)

Langmuir Isothem model.

Assuming protein [M]total in excess model: [M] ≈ [M]total = [MP]+[M].

Model -> [MP] := ([M]total * Rmax) / (Kd + [M]total)

[M]total * Rmax

[MP] := ——————-

Kd + [M]total

All concentrations must be in the same units!

Parameters:

• Mt (float) – Total protein [M] concentration: [M]total = [M]free+[MP].
• Rmax (float) – Maximum response level in abitrary units.

classmethod model_dataset(params, i, Mt)

Model dataset function.

This extracts the data from the Parameters, used by lmfit.

classmethod objective(params, Mt, data, sigma=None)

Objective function of the Langmuir Isotherm model.

result

Return lmfit ModelResult object.

class mrbles.kinetics.KModelSim(c_substrate, c_complex, kd_init, tol=0.0001)

Bases: future.types.newobject.newobject

Kurt’s white paper for competive binding.

Assuming substrate (peptide on bead) or complex (protein) concentration in excess.

Parameters:

• c_substrate (array) – NumPy array of substrate (peptide) concentrations.
• c_complex (array) – NumPy array of complex (protein) concentrations.
• kd_init (int) – Set range max for Kd’s starting from 0.
• tol (float) – Set tolerance error. Defaults to 1E-4.

Variables:

• result (array) – Returns NumPy array of fit.
• Functions –
• --------- –
• fit (function) – Function to fit parameters.
• Examples –

  >>> Mmat = np.logspace(0, 3, 20)  # Matrix of protein concentrations,
      e.g. 20x between 0 to 3 uM.
  >>> Pt = np.array(([10]*10))      # Concentrations of each peptide,
      e.g. 10x 10 uM.
  >>> test_kshow = ba.kin.kshow(Pt, Mmat, 2, 1E-4)
  >>> test_kshow.fit()
  >>> plt.plot(test_kshow.result)
  ...
  

static comp_excess(Mmat, Pt, Kd)

Complex (e.g. added protein concentration) in excess.

fit()

Fit solution and save to results.

Access result with object.result.

static kd_init(kd_init, n_substrate)

Initialize Kd values.

result

Return result from fit.

static sub_excess(Mmat, Pt, Kd)

Substrate (e.g. peptides on bead) in excess.