Société Française de Bioinformatique

Mots-Clés

systems biology metabolomics spatial biology cancer autoimmune diseases

Description

The Cancer Bioinformatics and Systems Biology team at IRCM (Cancer Research Institute of Montpellier) is looking for a talented postdoctoral fellow.

The cellular metabolism is involved in numerous aspects of tumor biology and in inflammatory diseases in general. We are developing a research program where the computational inference of cell and more globally tissue metabolic states is central. We will design and implement algorithms and statistical models that will be challenged in two application domains, which are the analysis of the immunosuppressive microenvironment of different tumors (breast, colorectal, ovary, and pancreas primarily), and the mechanisms behind chronic inflammation in specific autoimmune diseases.

Different data modalities will be considered with a preferential scenario combining transcriptomics and targeted metabolomics at spatial resolution. The bulk analysis of samples or spatial transcriptomics without matching metabolomics will be considered as well as. The research program will be conducted in close collaboration with a metabolomics laboratory and experts in cancer and autoimmune diseases. Deep learning and/diffusive approaches will be applied.

Our team has developed a number of algorithms and machine learning models to infer both intra-cellular and cellular networks [1–4], and to integrate data over such networks to extract actionable biological information such as candidate targets or biomarkers [5–8]. The successful candidate will address the challenging question of inferring the metabolic states in addition [9-10].

Preferred qualifications are either a bioinformatics PhD and solid machine learning skills or a mathematics/physics/computer science PhD with strong interests in life science applications. Deep neural network practical experience would be a plus. The position is funded for 2,5 years. A first contract will be established for 1 year and extended upon performance.

Interested applicants should e-mail their CV, a letter of motivation and the names and e-mails of 2 references to Prof Jacques Colinge (jacques.colinge@umontpellier.fr). Application deadline is December 31, 2025. Expected starting date March or April 1, 2026.

References

1. Villemin J-P, Bassaganyas L, Pourquier D, Boissière F, Cabello-Aguilar S, Crapez E, et al. Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR. Nucleic Acids Res. 2023; gkad352. doi:10.1093/nar/gkad352

2. Cabello-Aguilar S, Alame M, Kon-Sun-Tack F, Fau C, Lacroix M, Colinge J. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 2020. doi:10.1093/nar/gkaa183

3. Villemin J-P, Giroux P, Maillard M, et al., Colinge J. Addressing multiple facets of ligand-receptor network inference including single-cell proteomics. bioRxiv, 2025. doi:10.1101/2025.10.05.680519

4. Borg J-P, Colinge J, Ravel P. Testing and overcoming the limitations of modular response analysis. Brief Bioinform. 2025. doi:10.1093/bib/bbaf098.

5. Alame M, Cornillot E, Cacheux V, Tosato G, Four M, Oliveira LD, et al. The molecular landscape and microenvironment of salivary duct carcinoma reveal new therapeutic opportunities. Theranostics. 2020;10: 4383–4394. doi:10.7150/thno.42986

6. Blomen VA, Majek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, et al. Gene essentiality and synthetic lethality in haploid human cells. Science. 2015;350: 1092–1096. doi:10.1126/science.aac7557

7. Giguelay A, Turtoi E, Khelaf L, Tosato G, Dadi I, Chastel T, et al. The landscape of cancer-associated fibroblasts in colorectal cancer liver metastases. Theranostics. 2022;12: 7624–7639. doi:10.7150/thno.72853

8. Honda CK, Kurozumi S, Fujii T, Pourquier D, Khellaf L, Boissiere F, et al. Cancer-associated fibroblast spatial heterogeneity and EMILIN1 expression in the tumor microenvironment modulate TGF-β activity and CD8+ T-cell infiltration in breast cancer. Theranostics. 2024;14: 1873–1885. doi:10.7150/thno.90627

9. Hu T, Allam M, et al. Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology. Nat Commun. 2023; 14:8260.

10. Wagner A, Wang C, et al. Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell. 2021 Aug 5;184(16):4168-4185.e21.