pauwels2014: Bayesian Experimental Design for Systems Biology.

Share:

Implementation of a Bayesian active learning strategy to carry out sequential experimental design in the context of biochemical network kinetic parameter estimation. This package gathers functions and pre-computed data sets to reproduce results presented in Pauwels E. et. al published in BMC Systems Biology, 2014. Scripts are given to compute all results from scratch or to draw pictures based on pre-computed data sets.

Author
Edouard Pauwels
Date of publication
2014-08-23 08:12:04
Maintainer
Edouard Pauwels <pauwelsed@gmail.com>
License
GPL-3
Version
1.0

View on CRAN

Man pages

active_design
Simulates the active design process.
add_infinitesimal
Finite difference function
add_noise
Noise generative process for the simulations
armijo
Performs armijo line searcc
BFGS_special
An implementation of BFGS method for posterior maximization.
compute_gradient
Finite difference function
compute_gradient_coordinate
Finite difference function
compute_mean_risks
Compute an average risk as a function of credit spent
dmvnorm
Gaussian multivariate density
dream6_design
Simulates the active design process using the comparison...
estimate_risk_dream6
Expected risk estimation (comparison with litterature).
estimate_risk_out_all
Expected risk estimation.
eval_kn_log_like
Evaluates the likelihood of a parameter value
eval_log_like_knobj
Posterior function.
experiment_list1
Molecular perturbations.
exps
List of possible experiments
generate_our_knowledge
Initialize a knowledge list.
generate_sample
An implementation of the Metropolis Hasting algorithm
knobjs
Knowledge lists
log_likelihood
User defined likelihood function.
log_normalize
Normalize in log space
log_prior
User defined log prior
observables
Observable quantities of the model
pauwels2014-package
Reproduce numerical experiments
proj_grad
Least square on the positive orthant
random_design
Simulates a randim design process.
read_knobjs
Summarizes pre-computed results.
reverse_params
Transform log space parameters back to the original space
risk_theta_fun
Risk function
risk_theta_vect
Expected risk based on a posterior sample
sample_function
Generates posterior samples
sample_function_multi_mod_weight
Sample function visiting multiple modes of the posterior
sample_function_single_mod
Sample function visiting a single mode of the posterior.
simulate_experiment
Simulates the dynamics of a molecular perturbation
simulate_experiment_no_transform
Link to the ode solver.
transform_params
User defined parameter transformation function.

Files in this package

pauwels2014
pauwels2014/inst
pauwels2014/inst/doc
pauwels2014/inst/doc/pauwels2014.pdf
pauwels2014/inst/doc/pauwels2014.Rnw
pauwels2014/inst/doc/pauwels2014.R
pauwels2014/src
pauwels2014/src/src.c
pauwels2014/src/model0_simplified_mrna_rates.c
pauwels2014/NAMESPACE
pauwels2014/data
pauwels2014/data/experiment_list1.rda
pauwels2014/data/knobjs.rda
pauwels2014/data/observables.rda
pauwels2014/data/exps.rda
pauwels2014/data/datalist
pauwels2014/R
pauwels2014/R/estimate_risk_out_all.R
pauwels2014/R/risk_theta_vect.R
pauwels2014/R/active_design.R
pauwels2014/R/sample_function_multi_mod_weight.R
pauwels2014/R/compute_gradient.R
pauwels2014/R/sample_function_single_mod.R
pauwels2014/R/eval_kn_log_like.R
pauwels2014/R/BFGS_special.R
pauwels2014/R/bed-internal.R
pauwels2014/R/generate_sample.R
pauwels2014/R/simulate_experiment.R
pauwels2014/R/estimate_risk_dream6.R
pauwels2014/R/random_design.R
pauwels2014/R/dmvnorm.R
pauwels2014/R/compute_mean_risks.R
pauwels2014/R/reverse_params.R
pauwels2014/R/compute_gradient_coordinate.R
pauwels2014/R/transform_params.R
pauwels2014/R/log_prior.R
pauwels2014/R/eval_log_like_knobj.R
pauwels2014/R/armijo.R
pauwels2014/R/add_infinitesimal.R
pauwels2014/R/log_normalize.R
pauwels2014/R/read_knobjs.R
pauwels2014/R/add_noise.R
pauwels2014/R/sample_function.R
pauwels2014/R/generate_our_knowledge.R
pauwels2014/R/dream6_design.R
pauwels2014/R/proj_grad.R
pauwels2014/R/simulate_experiment_no_transform.R
pauwels2014/R/risk_theta_fun.R
pauwels2014/R/log_likelihood.R
pauwels2014/vignettes
pauwels2014/vignettes/refs.bib
pauwels2014/vignettes/pauwels2014.Rnw
pauwels2014/MD5
pauwels2014/build
pauwels2014/build/vignette.rds
pauwels2014/DESCRIPTION
pauwels2014/man
pauwels2014/man/sample_function_single_mod.Rd
pauwels2014/man/proj_grad.Rd
pauwels2014/man/log_likelihood.Rd
pauwels2014/man/knobjs.Rd
pauwels2014/man/exps.Rd
pauwels2014/man/reverse_params.Rd
pauwels2014/man/generate_our_knowledge.Rd
pauwels2014/man/armijo.Rd
pauwels2014/man/log_normalize.Rd
pauwels2014/man/pauwels2014-package.Rd
pauwels2014/man/risk_theta_vect.Rd
pauwels2014/man/BFGS_special.Rd
pauwels2014/man/log_prior.Rd
pauwels2014/man/eval_log_like_knobj.Rd
pauwels2014/man/active_design.Rd
pauwels2014/man/simulate_experiment_no_transform.Rd
pauwels2014/man/random_design.Rd
pauwels2014/man/eval_kn_log_like.Rd
pauwels2014/man/estimate_risk_out_all.Rd
pauwels2014/man/add_noise.Rd
pauwels2014/man/add_infinitesimal.Rd
pauwels2014/man/experiment_list1.Rd
pauwels2014/man/risk_theta_fun.Rd
pauwels2014/man/sample_function_multi_mod_weight.Rd
pauwels2014/man/observables.Rd
pauwels2014/man/read_knobjs.Rd
pauwels2014/man/simulate_experiment.Rd
pauwels2014/man/transform_params.Rd
pauwels2014/man/generate_sample.Rd
pauwels2014/man/sample_function.Rd
pauwels2014/man/estimate_risk_dream6.Rd
pauwels2014/man/compute_gradient_coordinate.Rd
pauwels2014/man/dream6_design.Rd
pauwels2014/man/dmvnorm.Rd
pauwels2014/man/compute_mean_risks.Rd
pauwels2014/man/compute_gradient.Rd