lnar-package: Inference for stochastic kinetic genetic networks using the...

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A collection of functions that implement the Linear Noise Approximation for stochastic kinetic models with emphasis on genetic auto-regulatory networks.


Package: lnar
Type: Package
Version: 0.0.5
Date: 2011-05-31
License: GPL (>=2.0)
LazyLoad: yes

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 the compmod method. The compiled model can be fitted to a dataset using optmod, a Maximum Likelihood Estimation procedure. Try 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 v.giagos@kent.ac.uk


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.

lnar documentation built on May 2, 2019, 4:51 p.m.