Published October 9, 2025
| Version v1
Dataset
Open
Dataset - Multi-omics-driven kinetic modeling reveals metabolic vulnerabilities and differential drug-response dynamics in ovarian cancer
-
1.
École Polytechnique Fédérale de Lausanne
- 2. Ludwig Institute for Cancer Research, Department of Oncology, University of Lausanne
- 3. Ecole Polytechnique Federale de Lausanne, Laboratory of Computational Systems Biotechnology
Description
Dataset required to reproduce the simulation runs from the study "Multi-omics-driven kinetic modeling reveals metabolic vulnerabilities and differential drug-response dynamics in ovarian cancer" by Toumpe et al.
The accompanying code is available at: https://github.com/EPFL-LCSB/human-cancer-kinetic-models
Dataset contains:
- Steady-state samples for both physiologies, used to represent the metabolic flux and metabolite concentration distributions for parametrizing the kinetic models.
- Kinetic parameter sets for the BRCA1_wt and BRCA1_mut physiologies, which were used to compute the distributions of control coefficients.
- Representative kinetic parameter sets for both physiologies, selected from the larger ensembles, which can be used to reproduce the case-study simulations, explore dynamic responses, and perform additional hypothesis-testing analyses.
The second part of the dataset can be found at https://zenodo.org/records/17349420.
Files
README.txt
Files
(39.4 GB)
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94.9 MB | Download |
Additional details
Funding
- European Commission
- SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway 814408
- Swiss National Science Foundation
- Models, Algorithms, Software and Repositories for Synthetic Biology and Biotechnology 188623
- Swiss National Science Foundation
- Functional chemoinformatic modelling of the host cell metabolome to fight apicomplexan parasites 198543
Software
- Repository URL
- https://github.com/EPFL-LCSB/human-cancer-kinetic-models
- Programming language
- Python
- Development Status
- Active