:mod:`test_growth` ================== .. py:module:: test_growth .. autoapi-nested-parse:: Perform tests on an instance of `cobra.Model` using growth data. Growth data comes from processed biolog experiments. Growth curves have to be converted into binary decisions whether or not an organism/strain was able to grow in a certain medium. .. !! processed by numpydoc !! Module Contents --------------- Functions ~~~~~~~~~ .. autoapisummary:: test_growth.test_growth_from_data_qualitative .. function:: test_growth_from_data_qualitative(model, experiment, threshold=0.95) Expect a perfect accuracy when predicting growth. The in-silico growth prediction is compared with experimental data and the accuracy is expected to be better than 0.95. In principal, Matthews' correlation coefficient is a more comprehensive metric but is a little fragile to not having any false negatives or false positives in the output. Implementation: Read and validate experimental config file and data tables. Constrain the model with the parameters provided by a user's definition of the medium, then compute a confusion matrix based on the predicted true, expected true, predicted false and expected false growth. The individual values of the confusion matrix are calculated as described in https://en.wikipedia.org/wiki/Confusion_matrix .. !! processed by numpydoc !!