Julia Code Examples of Pseudo-marginal methods for computational inference

This repository contains useful example Julia functions and scripts as an introduction to pseudo-marginal methods for inference on biochemical reaction networks using the chemical Langevin formulation.

Developer

David J. Warne (david.warne@qut.edu.au), School of Mathematical Sciences, Science and Engineering Faculty, Queensland Univeristy of Technology

Google Scholar: (https://scholar.google.com.au/citations?user=t8l-kuoAAAAJ&hl=en)

Citation Information

This code is provided as supplementary information to the paper,

David J Warne, Ruth E Baker, and Matthew J Simpson (2020). A practical guide to pseudo-marginal methods for computational inference in systems biology. Journal of Theoretical Biology, 496(2020):110255 DOI:10.1016/j.jtbi.2020.110255.

Licensing

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.

Requirements

The following list contains the Julia version and required Modules used by this project. Older or newer versions may work, but this has not been tested.

Julia version 1.2.0
PyPlot version 2.8.2
LaTeXStrings version 1.0.3
Distributions version 0.21.1
StatsBase version 0.32.0
QuadGK version 2.1.0
KernelDensity version 0.5.1
JLD2 version 0.1.3

For instructions to install Julia and Modules see, Julia Download and Installation and Julia Documentation

This folder contains a number of instructive Julia implementations for Stochastic simulation and computational inference. Demonstration scripts showing typical usage are also provided

The directory structure is as follows
|-- Modules
    |-- SDE
        |-- SDE.jl
        |-- ChemicalReactionNetworkModels.jl
        |-- EulerMaruyama.jl
        |-- ChemicalLangevin.jl
    |-- MCMC
        |-- MCMC.jl
        |-- MetropolisHastings.jl
        |-- ABCMCMC.jl
        |-- PseudoMarginalMetropolisHastings.jl
        |-- BootstrapParticleFilter.jl
        |-- Diagnostics.jl
|-- Demonstrations
    |-- DemoProdDeg.jl
    |-- DemoMichMent.jl
    |-- DemoSchlogl.jl
    |-- DemoRepressilator.jl
    |-- DemoMH.jl
    |-- DemoABCMCMC.jl
    |-- DemoPMMH.jl
    |-- DemoMichMentPMCMC.jl
    |-- DemoSchloglPMCMC.jl
    |-- DemoRepressilatorPMCMC.jl
    |-- plotParticleFilter.jl
    |-- plotMichMentMCMCconv.jl
    |-- plotSchloglMCMCconv.jl
    |-- plotRepressilatorMCMCconv.jl

Usage

Follow these steps to run the demonstrations:

Browse to repository folder
Add Modules/SDE/ and Modules/MCMC/ to the JULIA_LOAD_PATH environment variable. For example, in bash [jbloggs@localhost]$ export JULIA_LOAD_PATH=./Modules/MCMC/:./Modules/SDE/:$JULIA_LOAD_PATH
Start Julia, e.g., [jbloggs@localhost]$ julia
To run a demo, use the Julia command prompt (REPL, read-execute-print loop), e.g., julia> include("./Demonstrations/DemoProdDeg.jl")

List of examples

The following list of examples shows how to reproduce the figures in the main paper. For more computationlly intensive examples approximate run times are given for an Intel(R) Core(TM) i7-5600U CPU (2.6 GHz).

Figure 2

Generate figure 2 julia> include("./Demonstrations/DemoProdDeg.jl")

Figures 3

Generate figure 3 julia> include("./Demonstrations/DemoMH.jl")

Figure 4

Generate figure 4 For Figure 3(A)--(F) julia> include("./Demonstrations/DemoABCMCMC.jl") For Figure 3(G)--(L) julia> include("./Demonstrations/DemoPMMH.jl")

Figure 5

Generate figure 5 julia> include("./Demonstrations/plotParticleFilter.jl")

Figure 6

Generate figure 6 julia> include("./Demonstrations/DemoMichMent.jl")

Figure 7 and 8

Generate Markov Chain trajectories (Warning: run time approx. 2 hrs) julia> include("./Demonstrations/DemoMichMentPMCMC.jl")

Generate figures using output data file julia> include("./Demonstrations/plotMichMentMCMCconv.jl")

Figure 9

Generate figure 9 julia> include("./Demonstrations/DemoSchlogl.jl")

Figure 10 and 11

Generate Markov Chain trajectories (Warning: run time approx. 48 hrs ) julia> include("./Demonstrations/DemoSchloglPMCMC.jl")

Generate figures using output data file julia> include("./Demonstrations/plotSchloglMCMCconv.jl")

Figure 12

Generate figure 12 julia> include("./Demonstrations/DemoRepressilator.jl")

Figure 14

Generate Markov Chain trajectories (Warning: run time approx. 32 hrs ) julia> include("./Demonstrations/DemoRepressilatorPMCMC.jl")

Generate figures using output data file julia> include("./Demonstrations/plotRepressilatorMCMCconv.jl")

idontgetoutmuch / Warne2019_GuideToPseudoMarginal