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smfsb (version 1.5)

Stochastic Modelling for Systems Biology

Description

Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.

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Install

install.packages('smfsb')

Monthly Downloads

176

Version

1.5

License

LGPL-3

Maintainer

Darren Wilkinson

Last Published

January 13th, 2024

Functions in smfsb (1.5)

Run an ABC-SMC algorithm for infering the parameters of a forward model

Convert a time series object to a timed data matrix

Simulate a sample path from a stochastic kinetic model described by a stochastic Petri net

Create a function for advancing the state of an ODE model by using the deSolve package

Summarise and plot tabular MCMC output

Create a function for advancing the state of an SPN by using a simple approximate Poisson time stepping method

Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data set

Run a set of simulations initialised with parameters sampled from a given prior distribution, and compute statistics required for an ABC analaysis

Simulate a finite state space Markov chain

Simulate a sample path from a stochastic kinetic model described by a stochastic Petri net

Simulate a continuous time finite state space Markov chain

Simulate a sample path from a univariate diffusion process

Simulate a sample path from an ODE model

Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model

Create a function for advancing the state of an SDE model by using a simple Euler-Maruyama integration method

Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model

Simulate a sample path from the homogeneous immigration-death process

A function for advancing the state of a Lotka-Volterra model by using the Gillespie algorithm

A simple Gibbs sampler for Bayesian inference for the mean and precision of a normal random sample

metropolisHastings

Run a Metropolis-Hastings MCMC algorithm for the parameters of a Bayesian posterior distribution

Run a simple Metropolis sampler with standard normal target and uniform innovations

Create a function for computing the log of an unbiased estimate of marginal likelihood of a time course data set

Simulate a model on a regular grid of times, using a function (closure) for advancing the state of the model

Simple example data frame

Stochastic Modelling for Systems Biology

Simulate a many realisations of a model at a given fixed time in the future given an initial time and state, using a function (closure) for advancing the state of the model

Example SPN models

Simulate a model at a specified set of times, using a function (closure) for advancing the state of the model

Create a function for advancing the state of an SPN by using a simple continuous deterministic Euler integration method

Create a function for advancing the state of an SPN by using the Gillespie algorithm

Create a function for advancing the state of an SPN by using a simple Euler-Maruyama discretisation of the CLE on a 1D regular grid

Create a function for advancing the state of an ODE model by using a simple Euler integration method

Create a function for advancing the state of an SPN by using a simple Euler-Maruyama integration method for the approximating CLE

Create a function for advancing the state of an SPN by using Gillespie's first reaction method

Example simulated time courses from a stochastic Lotka--Volterra model

StepGillespie2D

Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 2D regular grid

StepGillespie1D

Create a function for advancing the state of an SPN by using the Gillespie algorithm on a 1D regular grid

Create a function for advancing the state of an SPN by using a simple Euler-Maruyama discretisation of the CLE on a 2D regular grid

Discretise output from a discrete event simulation algorithm