Evolution on rugged adaptive landscapes

This repository contains supporting material for

Obolski^eq, Ram^eq, and Hadany (2018) Key issues review: evolution on rugged adaptive landscapes, Reports on Progress in Physics, 81: 12602.

A preprint is available on bioRxiv.

The notebook files (.ipynb) include Python source code for reproduction of Figures 2, 3, 5, and 6.

View the notebooks

Interact with the notebooks on binder.

• nk_model.ipynb
  • Contains source code to generate random landscapes from an NK model and visualize their ruggedness to produce Figure 2.
• holey_landscape.ipynb
  • Contains source code to generate random holey landscapes, analyse their connectedness and produce Figure 3.
• simulations.ipynb:
  • Contains source code to load empirical adaptive landscape data, simulate evolution on the landscape using a Wright-Fisher model, and visualize the results of simulations to produce Figure 6.
  • Cotains source code to load results of simulations on a two-loci rugged landscape and analyse and visualize the results to produce Figure 5. Source code for running simulations is in simulation.py.

Data source

• Data for TEM landscape, Weinreich2006.csv, is from Weinreich, Delaney, Depristo, & Hartl. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science (80-. ). 312, 111–114 (2006)
• Data for A. nigeri landscape, Franke2011.csv, is from Franke, Klözer, de Visser, & Krug. Evolutionary Accessibility of Mutational Pathways. PLoS Comput. Biol. 7, e1002134 (2011).
• Data for two-loci landscape is from Ram, & Hadany. Stress-induced mutagenesis and complex adaptation. Proc. R. Soc. B Biol. Sci. 281, 20141025–20141025 (2014), retrieved from Dryad.

Run the notebook

You can interact with the notebooks on binder or on your own machine. Following are instructions for the latter.

Install dependencies

The easiest way to install the dependencies is to install Anaconda. You should use Python 3, preferably with version 3.5 or higher. The notebooks will probably not work on Python 2.

All required packages should then be available. However, if you get an ImportError due to a package not being installed, the following command will install all requirements using conda:

conda install jupyter notebook cython ipykernel matplotlib numpy pandas scipy seaborn

Get the source code repository

You can download all the source code by clicking the Clone or Download button and choosing Download ZIP. Then extract the ZIP to a folder on your machine.

If you use git you can clone the repo using:

git clone https://github.com/yoavram/UnderTheRug.git

Run the notebook

Start a Jupyter notebook server inside the repo folder:

cd <path to folder>
jupyter notebook

Your browser should open automatically. Choose one of the notebooks (.ipynb files).

Troubleshooting

Use the Issues page to report problems or suggest improvements or contact Yoav Ram.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.