Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 3

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387) - Part 3

The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.

The dataset contains the following files:

1. Distribution_comparison - contains 2 folders
   1. No integration test - 10 repeats of RENAISSANCE generated models with 25 generations each with maximal eigenvalues
   2. All integration test - 5 repeats of RENAISSANCE with 108 Kms integrate with 25 generations each with maximal eigenvalues
2. Shikki bioreactor -contains 350000 RENAISSANCe generated parameter sets with the corresponding bioreactor simulation solutions. 
3. Slow and steady - contains 30 sampled slow steady states and their RENAISSANCE achieved maximal eigenvalues (originally sampled from index 1586 to 1581 from 5000 smaples already provided)