PathWeightSampling

Julia implementation of Path Weight Sampling (PWS) to compute information transmission rates

https://github.com/manuel-rhdt/pathweightsampling.jl

Science Score: 54.0%

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Repository

Julia implementation of Path Weight Sampling (PWS) to compute information transmission rates

Basic Info

Host: GitHub
Owner: manuel-rhdt
License: mit
Language: Julia
Default Branch: master
Homepage:
Size: 41.6 MB

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Stars: 1
Watchers: 1
Forks: 1
Open Issues: 0
Releases: 1

Created about 6 years ago · Last pushed almost 2 years ago

Metadata Files

Readme License Citation

PathWeightSampling.jl

PathWeightSampling.jl is a Julia package to compute information transmission rates using the novel Path Weight Sampling (PWS) method.

Documentation

The documentation for PathWeightSampling.jl is hosted on github.

Installation

For instructions for how to install Julia itself, see the official website.

To install this package, type from the Julia REPL julia julia> import Pkg; Pkg.add("PathWeightSampling")

Alternatively, you can install this package by starting Julia, typing ] and then julia pkg> add PathWeightSampling

Quick Start

After installation, the package can be loaded from directly from julia. julia julia> using PathWeightSampling We then need a system of reactions for which we want to compute the mutual information. We can use one of the included example systems, such as a simple model for gene expression. julia julia> system = PathWeightSampling.gene_expression_system() SimpleSystem with 4 reactions Input variables: S(t) Output variables: X(t) Initial condition: S(t) = 50 X(t) = 50 Parameters: κ = 50.0 λ = 1.0 ρ = 10.0 μ = 10.0 This specific model is very simple, consisting of only 4 reactions:

∅ → S with rate κ
S → ∅ with rate λ
S → S + X with rate ρ
X → ∅ with rate μ

S represents the input and X represents the output. The values of the parameters can be inspected from the output above. For this system, we can perform a PWS simulation to compute the mutual information between its input and output trajectories:

julia julia> result = mutual_information(system, DirectMCEstimate(256), num_samples=1000)

Here we just made a default choice for which marginalization algorithm to use (see documentation for more details). This computation takes approximately a minute on a typical laptop. The result is a DataFrame with three columns and 1000 rows:

julia 1000×3 DataFrame Row │ TimeConditional TimeMarginal MutualInformation │ Float64 Float64 Vector{Float64} ──────┼────────────────────────────────────────────────────────────────── 1 │ 0.000180898 0.0508378 [0.0, -0.67167, 0.388398, -0.343… ⋮ │ ⋮ ⋮ ⋮ 1000 │ 0.00020897 0.0694072 [0.0, 0.254173, 0.362607, 0.2584… 998 rows omitted

Each row represents one Monte Carlo sample.

TimeConditional is the CPU time in seconds for the computation of the conditional probability P(x|s)
TimeMarginal is the CPU time in seconds for the computation of the marginal probability P(x|s)
MutualInformation is the resulting mutual information estimate. This is a vector for each sample giving the mutual information for trajectories of different durations. The durations to which these individual values correspond is given by

julia julia> system.dtimes 0.0:0.1:2.0

So we computed the mutual information for trajectories of duration 0.0, 0.1, 0.2, ..., 2.0.

We can plot the results (assuming the package Plots.jl is installed):

julia julia> using Plots, Statistics julia> plot( system.dtimes, mean(result.MutualInformation), legend=false, xlabel="trajectory duration", ylabel="mutual information (nats)" )

Plot of the mutual information as a function of trajectory duration for the simple gene expression system.

Here we plot mean(result.MutualInformation), i.e. we compute the average of our Monte Carlo samples, which is the PWS estimate for the mutual information.

More examples and a guide can be found in the documentation

Acknowledgments

This work was performed at the research institute AMOLF. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 885065) and was financially supported by the Dutch Research Council (NWO) through the “Building a Synthetic Cell (BaSyC)” Gravitation grant (024.003.019).

Owner

Name: Manuel Reinhardt
Login: manuel-rhdt
Kind: user
Location: Amsterdam
Company: AMOLF

Repositories: 8
Profile: https://github.com/manuel-rhdt

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: PathWeightSampling.jl
message: >-
  If you use this software, please cite the article from
  preferred-citation and the software itself.
type: software
authors:
  - given-names: Manuel
    family-names: Reinhardt
    affiliation: AMOLF
    email: reinhardt@amolf.nl
    orcid: 'https://orcid.org/0000-0001-8926-9513'
repository-code: 'https://github.com/manuel-rhdt/PathWeightSampling.jl'
license: MIT
preferred-citation:
  authors:
    - given-names: Manuel
      family-names: Reinhardt
      affiliation: AMOLF
      email: reinhardt@amolf.nl
      orcid: 'https://orcid.org/0000-0001-8926-9513'
    - given-names: Gašper
      family-names: Tkačik
      affiliation: Institute of Science and Technology Austria
      orcid: 'https://orcid.org/0000-0002-6699-1455'
    - given-names: Pieter Rein
      family-names: ten Wolde
      affiliation: AMOLF
      orcid: 'https://orcid.org/0000-0001-9933-4016'
  title: "Path Weight Sampling: Exact Monte Carlo Computation of the Mutual Information between Stochastic Trajectories"
  journal: Phys. Rev. X
  volume: 13
  issue: 4
  pages: 041017
  year: 2023
  month: 10
  publisher: "American Physical Society"
  doi: '10.1103/PhysRevX.13.041017'
  type: article

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Total Commits: 574
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Development Distribution Score (DDS): 0.429

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Name	Email	Commits
Manuel Reinhardt	m**t@g**m	328
Manuel Reinhardt	M**t@a**l	220
github-actions[bot]	4**]@u**m	16
CompatHelper Julia	c**y@j**g	7
Manuel Reinhardt	r**t@a**l	3

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Last synced: 12 months ago

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Total packages: 1
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Total dependent packages: 0
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Total versions: 1

juliahub.com: PathWeightSampling

Julia implementation of Path Weight Sampling (PWS) to compute information transmission rates

Documentation: https://docs.juliahub.com/General/PathWeightSampling/stable/
License: MIT
Latest release: 0.1.0
published over 4 years ago

Versions: 1
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 9.9%

Average: 38.4%

Dependent packages count: 38.9%

Forks count: 40.4%

Stargazers count: 64.2%

Last synced: 11 months ago

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PathWeightSampling

Science Score: 54.0%

Repository

Basic Info

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Metadata Files

README.md

PathWeightSampling.jl

Documentation

Installation

Quick Start

Acknowledgments

Owner

Citation (CITATION.cff)

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juliahub.com: PathWeightSampling

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