means.approximation.lna package

Submodules

class means.approximation.lna.lna.LinearNoiseApproximation(model)

Bases: means.approximation.approximation_baseclass.ApproximationBaseClass

A class to performs Linear Noise Approximation of a model.

Initialise the approximation.

Parameters:model (Model) – Model to approximate

run()

Overrides the default _run() private method. Performs the complete analysis :return: A fully computed set of Ordinary Differential Equations that can be used for further simulation :rtype: ODEProblem

means.approximation.lna.lna.lna_approximation(model)

A wrapper around LinearNoiseApproximation. It performs linear noise approximation (MEA).

Returns:an ODE problem which can be further used in inference and simulation. Return type:ODEProblem

Module contents

Linear Noise Approximation

This part of the package implements Linear Noise Approximation as described in [Komorowski2009].

Example:

>>> from means.approximation.lna.lna import lna_approximation
>>> from means.examples.sample_models import MODEL_P53
>>> ode_problem = lna_approximation(MODEL_P53)
>>> print ode_problem

The result is an means.core.problems.ODEProblem. Typically, it would be further used to perform simulations (see simulation) and inference (see inference).

[Komorowski2009]

Komorowski, B. Finkenstadt, C. V. Harper, and D. A. Rand,”Bayesian inference of biochemical kinetic parameters using the linear noise approximation,”BMC Bioinformatics, vol. 10, no. 1, p. 343, Oct. 2009.

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