{"id":34656426,"url":"https://github.com/andim/evolimmune","last_synced_at":"2025-12-24T18:07:31.491Z","repository":{"id":72334574,"uuid":"57219749","full_name":"andim/evolimmune","owner":"andim","description":"Source code accompanying the paper \"Diversity of immune strategies explained by adaptation to pathogen statistics\" ","archived":false,"fork":false,"pushed_at":"2017-06-21T18:24:10.000Z","size":1073,"stargazers_count":8,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-09-09T06:41:55.147Z","etag":null,"topics":["biophysics","openscience"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/andim.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2016-04-27T14:26:02.000Z","updated_at":"2022-04-22T15:39:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"0ed854b0-d4d8-469a-91a2-a094a0410bdc","html_url":"https://github.com/andim/evolimmune","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/andim/evolimmune","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andim%2Fevolimmune","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andim%2Fevolimmune/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andim%2Fevolimmune/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andim%2Fevolimmune/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/andim","download_url":"https://codeload.github.com/andim/evolimmune/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/andim%2Fevolimmune/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28005981,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-12-24T02:00:07.193Z","response_time":83,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["biophysics","openscience"],"created_at":"2025-12-24T18:04:12.361Z","updated_at":"2025-12-24T18:07:31.483Z","avatar_url":"https://github.com/andim.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Diversity of immune strategies explained by adaptation to pathogen statistics\n\nThis repository contains the source code associated with the manuscript\n\nMayer, Mora, Rivoire, Walczak : [Diversity of immune strategies explained by adaptation to pathogen statistics](http://dx.doi.org/10.1073/pnas.1600663113), PNAS 2016\n\nIt allows reproduction of all numerical results reported in the manuscript.\n\n[![DOI](https://zenodo.org/badge/57219749.svg)](https://zenodo.org/badge/latestdoi/57219749)\n\n## Quick-start: Follow these links to see the analysis code producing the figures\n\n- [Figure 2](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/fig2/figure2.ipynb)\n- [Figure S1](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIopt/figure-SIopt.ipynb)\n- [Figure S2](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSInonfactorizing/figure-SInonfactorizing.ipynb)\n- [Figure S3](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIaltphases/figure-SIaltphases.ipynb)\n- [Figure S4](http://nbviewer.jupyter.org/github/andim/evolimmune/blob/master/figSIevol/figure-SIevol.ipynb)\n\n## Installation requirements\n\nThe code uses Python 2.7+.\n\nA number of standard scientific python packages are needed for the numerical simulations and visualizations. An easy way to install all of these is to install a Python distribution such as [Anaconda](https://www.continuum.io/downloads).\n\n- [numpy](http://github.com/numpy/numpy/)\n- [scipy](https://github.com/scipy/scipy)\n- [pandas](http://github.com/pydata/pandas)\n- [matplotlib](http://github.com/matplotlib/matplotlib)\n\nAdditionally the code also relies on these packages:\n\n- [shapely](http://github.com/Toblerity/Shapely)\n- [palettable](http://github.com/jiffyclub/palettable)\n- [scipydirect](http://github.com/andim/scipydirect/)\n- [noisyopt](http://github.com/andim/noisyopt)\n\nAnd optionally for nicer progress output install:\n\n- [pyprind](http://github.com/rasbt/pyprind)\n\n## Running the code\n\nThe time stepping of the population dynamics is accelerated by a Cython module, which needs to be compiled first. To compile it run `make cython` in the `lib` directory. In the directories for the different figures launch `make run` followed by `make agg` to produce the underlying data. Please copy the `paper.mplstyle` to your custom matplotlib style directory (likely `.config/matplotlib/stylelib/`). We provide both Jupyter notebooks with additional explanatory comments and plain python files for generating the figures.\n\n## Remarks\n\nIn the code we use the following simplified notations `c_constitutive = mu1, c_defense = mu2, c_infection = lambda_, c_uptake = cup` and we define the trade-off `c_defense(c_constitutive)` as a parametric function of a parameter `epsilon` in [0, 1], where 0 corresponds to fully constitutive and 1 to maximally regulated responses.\n\nNote: As the simulations are stochastic you generally will not get precisely equivalent plots.\n\n## Contact\n\nIf you run into any difficulties running the code, feel free to contact us at `andimscience@gmail.com`.\n\n## License\n\nThe source code is freely available under an MIT license. The plots are licensed under a Creative commons attributions license (CC-BY).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandim%2Fevolimmune","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandim%2Fevolimmune","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandim%2Fevolimmune/lists"}