Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2
Authors/Creators
- 1. EPFL
- 2. Harvard
Description
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.
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. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).
2. scripts.zip - scripts to generate figure 2-5 on toy data
Notes
This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).
Files
param_fixing.zip
Additional details
Related works
- Is supplement to
- Preprint: 10.1101/2023.02.21.529387 (DOI)
Funding
- European Commission
- SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway 814408
- Swiss National Science Foundation
- Computational Methods for modeling and analysis of large-scale metabolic networks 315230_163423
- Swedish Research Council
- A new paradigm for versatile cell factories 2016-06160