Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1

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).

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. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of E. coli metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).

2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)

3. ODE_solutions.zip - self-explanatory (Figure 2c)

4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)

5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)

6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g

The remaining datasets are published in the following links

 - https://doi.org/10.5281/zenodo.7930084

 - https://doi.org/10.5281/zenodo.10391802