A collection of functions that implement the Linear Noise Approximation for stochastic kinetic models with emphasis on genetic auto-regulatory networks.
The lnar package provides inferential tools for a class of genetic
auto-regulatory networks based on the Linear Noise Approximation (Kurtz
1972). Two LNA-based estimation methods are provided: the
Restarting and the Non Restarting method, see (Giagos 2010)
for more details. Such networks, are specified as a system of biochemical
reactions in the
parsemod method which, in turn, outputs
the underlying Linear Noise Approximation as C code to be compiled with
compmod method. The compiled model can be fitted to
a dataset using
optmod, a Maximum Likelihood Estimation
demo(lv) for an example implementing the
Lotka-Voltera model and
demo(autoreg) for the implementation of
a prokaryotic transcription model (Golightly and Wilkinson 2005).
This is an experimental and unstable package. Most of the C code has been ported from an earlier version implemented in C using the Gnu Scientific Library (GSL).
All methods expect the parameters to be expressed in terms of thetas, i.e. scaled according to their order. Normally, in a biological model, e.g. a SBML file, the parameters (c) correspond to kinetics equations based on the number of molecules.
Vasileios Giagos firstname.lastname@example.org
Kurtz, T. G.: 1972, The relationship between stochastic and
deterministic models for chemical reactions, The Journal of Chemical
Physics 57(7), 2976-2978.
Golightly, A. and Wilkinson D. J.: 2005, Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation, Biometrics 61(3), 781-788.
Giagos, V.: 2010, Inference for auto-regulatory genetic networks using diffusion process approximations, Thesis, Lancaster University, 2010.
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