Bliss

class synergy.combination.Bliss(drug1_model=None, drug2_model=None)[source]

Bases: DoseDependentSynergyModel2D

Bliss independence synergy model.

Bliss synergy is defined as the difference between the observed E and the E predicted by the Bliss Independence assumption, which is that the effects of two drugs will multiply.

Members

synergyarray_like, float

(-inf,0)=antagonism, (0,inf)=synergism

E_reference(d1, d2)[source]

Return the expected effect of the combination of drugs at doses d1 and d2.

Parameters:

  • d1 (ArrayLike) – Concentration of drug 1

  • d2 (ArrayLike) – Concentration of drug 2

Return ArrayLike:

Reference (additive) values of E at the given doses

fit(d1, d2, E, **kwargs)

Fit the model to data.

Parameters:

  • d1 (ArrayLike) – Concentration of drug 1

  • d2 (ArrayLike) – Concentration of drug 2

  • E (ArrayLike) – Effect of the combination of drugs at doses d1 and d2

  • kwargs (dict) –

    • use_jacobian: whether to use the model jacobian when fitting single-drug models

    • Additional keyword arguments for scipy.optimize.curve_fit()

Return ArrayLike:

Synergy values

property is_fit

True if the model has been fit to data.

property is_specified

True if each single-drug model is specified.