Curveball: Predicting competition results from growth curves

Author: Yoav Ram

Curveball is an open-source software for analysis and visualization of high-throughput growth curve data and prediction of competition experiment results.

Curveball:

  • fits growth models to growth curve data to estimate values of growth traits

  • uses estimated growth traits and competition models to predict results of competition experiments

  • infers fitness and selection coefficients from predicted competition results

Who is this for?

Curveball is for researchers who want to analyze growth curve data using a framework that integrates population dynamics and population genetics, allowing the inference and interpretation of differences in fitness between strains in terms of differences in growth traits.

Curveball provides a command line interface (CLI) and an programmatic interface (API) that can directly work with collections of growth curve measurements (e.g., 96-well plates).

No programmings skills are required for using the CLI; basic familiarity with the Python programming language is recommended for using the API.

Note

This documentation provides technical details on using Curveball. For more information on the theoretical and computational aspects of Curveball, read the paper:

Ram, Dellus-Gur, Bibi, Karkare, Obolski, Feldman, Cooper, Berman, Hadany. (2019) Predicting microbial relative growth in a mixed culture from growth curve data. Proceedings of the National Academy of Science USA.

or the preprint:

Ram et al. (2015) Predicting competition results from growth curves. bioRxiv. doi:10.1101/022640.

Quickstart

Install Anaconda, then run:

>>> conda install -c conda-forge -c https://conda.anaconda.org/t/yo-4760086a-c28d-467d-bd46-53bea521edac/yoavram curveball
>>> curveball --help

For more detailed instructions, Proceed to the Installation instructions and then to the Tutorial.

References

Note

Curveball source code and examples are licensed under the terms of the MIT license.

Curveball documentation, examples, and other materials are licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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