https://github.com/andim/optimmune

Source code accompanying the paper "How a well-adapted immune system is organized"

https://github.com/andim/optimmune

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 7 DOI reference(s) in README
  • Academic publication links
    Links to: zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (15.1%) to scientific vocabulary

Keywords

biophysics openscience
Last synced: 6 months ago · JSON representation

Repository

Source code accompanying the paper "How a well-adapted immune system is organized"

Basic Info
  • Host: GitHub
  • Owner: andim
  • License: other
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 72.3 KB
Statistics
  • Stars: 1
  • Watchers: 4
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
biophysics openscience
Created almost 11 years ago · Last pushed over 8 years ago
Metadata Files
Readme License

Readme.md

Optimal immune repertoires

This repository contains the code associated with the manuscript

Mayer, Balasubramanian, Mora, Walczak : How a well adapted immune system is organized, PNAS 2015

It allows reproduction of all numerical results reported in the manuscript.

DOI

Dependencies

The code is written in Python and depends on a number of numerical and scientific libraries. Below we give the version numbers of the packages for which the code is known to run.

  • Python 2.7.8
  • Numpy 1.8.2
  • Scipy 0.16.0
  • Matplotlib 1.3.1
  • Cython 0.20.2 and relevant development versions of libraries (only needed for figure 5)

Optionally pyFFTW can be used to speed up some of the calculations. In the absence of this package the code automatically falls back to the corresponding scipy functions.

Usage

Download the source code by cloning the repository git clone https://github.com/andim/optimmune. Follow the following set of instructions in the given order.

  • run make cython in library directory (only needed for figure 5)
  • run python run*.py in figure directories to produce data.
    • In a number of cases the optimization files produce results for a range of parameters. Which parameters are used is controlled by a command line argument. The command line argument is a single integer between one and the number of different parameter combinations. On a computing cluster on which a grid engine is installed the looping over different arguments can be performed via the provided submit files (qsub arrayjob.sh).
    • Warning: some of the optimizations run for a long time (> 1h).
  • for some figures the results need to be postprocessed by invoking python calc*.py
  • run python fig*.py in figure directories to produce figures

Here the * is a placeholder for the specific filenames. Note: As most of the simulations are stochastic you generally do not get precisely equivalent plots.

License

The source code is freely available under an MIT license, unless otherwise noted within a file. The plots are licensed under a Creative commons attributions license (CC-BY).

Owner

  • Name: Andreas Tiffeau-Mayer
  • Login: andim
  • Kind: user
  • Location: London
  • Company: University College London

Quantitative Immunology, Biological Physics

GitHub Events

Total
Last Year

Dependencies

environment.yml pypi
  • pyfftw *