This repository contains the source code associated with the manuscript

Mayer, Mora, Rivoire, Walczak : Diversity of immune strategies explained by adaptation to pathogen statistics, PNAS 2016

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

• Figure 2
• Figure S1
• Figure S2
• Figure S3
• Figure S4

The code uses Python 2.7+.

A 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.

• numpy
• scipy
• pandas
• matplotlib

Additionally the code also relies on these packages:

• shapely
• palettable
• scipydirect
• noisyopt

And optionally for nicer progress output install:

• pyprind

The 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.

In 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.

Note: As the simulations are stochastic you generally will not get precisely equivalent plots.

If you run into any difficulties running the code, feel free to contact us at [email protected].

The source code is freely available under an MIT license. The plots are licensed under a Creative commons attributions license (CC-BY).