Digital twins for in vivo metabolic flux estimation in patients with brain cancer

The codes related to our manuscript "Digital twins for in vivo metabolic flux estimation in patients with brain cancer" can be found in this repository.

We organized the codes into following folders and provided a detailed description for using the codes in each folder. Please click on the links below to see the codes and README files.

1. Single-cell RNA-seq analysis

2. Single-cell metabolic interaction analysis

3. Modified scFEA: Quantification of exchange fluxes using scRNA-seq data

4. ¹³C-scMFA: Quantification of intra- and intercellular fluxes using integrated scRNA-seq and ¹³C-enrichment data

5. Development of patient digital twins: Simulation of fluxes and ¹³C-enrichment data

6. Metabolic CNN: Estimation of relative anabolic fluxes in bulk tissues

7. Informed MFA: Metabolic flux analysis informed by CNN-predicted fluxes

Requirements:

1. MATLAB R2021b with default installation on Windows 11
2. Artelys Knitro Optimizer version 12.4 (MATLAB version)
3. MATLAB Parallel Processing toolkit (optional)
4. R version 4.2.2
5. Python version 3.8 and 3.11

More detailed requirements can be found in each folder.

Data availability

scRNA-seq data are publicly available for download and visualization via the Single Cell Portal: SCP3323 (patients), SCP3333 (PDXs), SCP3334 (TRP). Raw scRNA-seq data are available at Gene Expression Omnibus (GEO): GSE311151 (patients) and GSE311464 (PDXs and TRP). Seurat objects containing processed scRNA-seq and simulated data are available at Zenodo: https://doi.org/10.5281/zenodo.17373726. Description of files deposited to Zenodo can be found in figure_descriptions.xlsx. All data used to generate display items in the manuscript are available in Data S1.

Citation

Meghdadi B. et al. Digital twins for in vivo metabolic flux estimation in patients with brain cancer. Cell Metabolism (2026). DOI: 10.1016/j.cmet.2025.10.022