We are NetB(IO)², a bioinformatics and computational biology research group at the Cancer Research Center of Toulouse
We study and simulate the tumor microenvironment to understand the relationship between patients and cancer. We focus on how immune cells, especially innate ones, interact with the tumour, hoping to improve personalisation of immunotherapies.
Our final goal is to improve these therapies understanding how variability in the patients, in the tumours, and in their interactions affects their efficacy. We develop and use different computational approaches which includes the application of multi-omics data (transcriptomics, epigenomics, proteomics and metabolomics), spatial data, mathematical modeling, data simulations and network theory as useful frameworks to represent systems in which relationships such as interactions or similarity between objects are important. We apply network models to study such diverse systems as networks of patient-patient similarity, networks of interactions between different cell types in a tumour, and 3D interactions of genes in the nucleus.
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| tysserand Fast and accurate spatial network reconstruction |
GARDEN-NET Genome ARchitecture DNA Epigenome and Nucleome Network Exploration Tool |
MOSNA Patterns and community detection in spatial networks |
CellTFusion Integration of transcriptional regulatory networks for immune deconvolution |
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- Hurtado, M., Khajavi, L., Essabbar, A., Kammer, M., Xie, T., Coullomb, A., … Passioukov, A. (2024). Transcriptomics profiling of the non-small cell lung cancer microenvironment across disease stages reveals dual immune cell-type behaviors. Frontiers in Immunology, 15. https://doi.org/10.3389/fimmu.2024.1394965
- Messina, O., Raynal, F., Gurgo, J,. Fiche, J.B., Pancaldi, V., Nollmann, M. (2023). 3D chromatin interactions involving Drosophila insulators are infrequent but preferential and arise before TADs and transcription. Nat Commun. 21;14(1):6678. https://doi.org/10.1038/s41467-023-42485-y.
- Marku M, Pancaldi V (2023) From time-series transcriptomics to gene regulatory networks: A review on inference methods. PLoS Comput Biol 19(8): e1011254. https://doi.org/10.1371/journal.pcbi.1011254
- Senosain, M.F., Zou, Y., Patel, K., Zhao, S., Coullomb, A., Rowe, D. J., … López, C. F. (2023). Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness. Cancer Research Communications, 3(7), 1350–1365. https://doi.org/10.1158/2767-9764.crc-22-0373
- Rimailho L, Faria C, Domagala M, Laurent C, Bezombes C, Poupot M. 2023. T cells in immunotherapies for B-cell malignancies. Front. Immunol. June in press. https://doi.org/10.3389/fimmu.2023.1200003.
- Verstraete, N., Marku, M., Domagala, M., Hélène Arduin, Bordenave, J., Jean‐Jacques Fournié, … Vera Pancaldi. (2023). An agent-based model of monocyte differentiation into tumour-associated macrophages in chronic lymphocytic leukemia. IScience, 26(6), 106897–106897. https://doi.org/10.1016/j.isci.2023.106897
