gpvolve

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 .

User Documentation

• Typical Pipeline
• Installation and Dependencies
• Writing a fitness function
• Writing a fixation function
• API documentation

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