``gpvolve``
===========

.. image:: img/comp_pipeline.png
  :align: right
  :scale: 50 %

*A Python API for simulating and analyzing evolution in genotype-phenotype space.*

GPvolve can used to:

1. Build a markov state model from a genotype-phenotype-map.
2. Find clusters of genotypes that represent metastable states of the system, using PCCA+.
3. Compute fluxes and pathways between pairs of genotypes and/or clusters of interest, using Transition Path Theory.
4. Visualize the outputs of all of the above.


The core-utilities of this library are built on top of the pyemma and msmtools packages.
For a deeper understanding of these tools, we recommend reading the docs and scientific
references of the respective libraries ([1]_, [2]_, [3]_).

A rationale for treating fitness landscapes as markov systems can be found in [4]_.

Currently, this package works only as an API. There is no command-line
interface. Instead, we encourage you use this package inside `Jupyter notebooks`_ .

.. _`Jupyter notebooks`: https://www.jupyter.org


User Documentation
------------------
.. toctree::
   :maxdepth: 1

   pages/pipeline
   pages/installation
   pages/selection
   pages/fixation
   api/main


References
----------

.. [1] https://github.com/markovmodel/PyEMMA
.. [2] https://github.com/markovmodel/msmtools
.. [3] M K Scherer, B Trendelkamp-Schroer, F Paul, G Pérez-Hernández, M Hoffmann, N Plattner, C Wehmeyer, J-H Prinz and F Noé: PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models, J. Chem. Theory Comput. 11, 5525-5542 (2015)
.. [4] G Sella, A E Hirsh: The application of statistical physics to evolutionary biology, Proceedings of the National Academy of Sciences Jul 2005, 102 (27) 9541-9546; DOI: 10.1073/pnas.0501865102