Academic profile
Postdoctoral researcher at the interface of machine learning, clinical trial design, single-cell multi-omics, and molecular networks. I am always happy to discuss research ideas, translational questions, collaborations, or opportunities where computational biology can make complex biomedical data actionable.
Network biology: inferring molecular mechanisms
from single-cell data.
Clinical data augmentation: exploring how synthetic
patients can make trial design smarter and more robust!
My medical training shapes the questions I ask: how models can support clinical reasoning, trials, and translational decisions.
Université Paris-Cité / INRIA
2025-present · Postdoctorate in ML for clinical trial design.
Institut Pasteur
Saez Lab
2022-2025 · PhD in computational biology and single-cell multi-omics.
ENS Paris
Institut Curie
2019-2021 · MD-PhD program, IMaLiS MSc, and cancer systems biology.
University of Strasbourg
2017-2019 · Early medical training and medicine-science track.
My work connects clinical questions, single-cell data, and network modeling through four complementary themes.
Evaluating VAEs, Gaussian mixture models, diffusion models, and mechanistic pharmacological models to generate synthetic patients and support clinical trial designs.
Bridging cell-cell communication and intracellular regulation to model how multiple cell types coordinate in tissues.
Integrating scRNA-seq, scATAC-seq, and multimodal single-cell data to infer regulatory and mechanistic networks.
Building heterogeneous multilayer networks that combine molecular, regulatory, and intercellular layers into explorable models.
Selected publications on molecular networks, single-cell multi-omics, gene regulation, and computational biology methods.
Badia-i-Mompel P., Casals-Franch R., Wessels L., Müller-Dott S., Trimbour R., Yang Y., Ramirez Flores O. R., Saez-Rodriguez J. bioRxiv. 10.1101/2024.12.20.629764
Trimbour R., Ramirez Flores O. R., Saez-Rodriguez J., Cantini L. bioRxiv. 10.64898/2026.01.20.700561
Trimbour R., Saez-Rodriguez J., Cantini L. Bioinformatics. 10.1093/bioinformatics/btag092
Trimbour R., Deutschmann I. M., Cantini L. Bioinformatics. 10.1093/bioinformatics/btae143
Badia-i-Mompel P., Wessels L., Müller-Dott S., Trimbour R., Ramirez Flores O. R., Argelaguet R., Saez-Rodriguez J. Nature Reviews Genetics. 10.1038/s41576-023-00618-5
Software and computational methods developed around single-cell data and molecular networks.
ReCoN is a new tool for reconstructing multicellular models. It combines gene regulatory networks and cell communication networks to explore molecular coordination between multiple cell types, all at once.
ReCoN uses heterogeneous multilayer networks and integrates several layers of information into a complex network ready to be explored and analyzed. Both GRNs and intercellular networks are inferred from single-cell RNA-seq data, with optional scATAC-seq integration.
GitHub · Documentation · Paper
Python package to predict cis-regulatory DNA interactions from single-cell chromatin accessibility data.
GitHub · Documentation · Paper
Method for reconstructing molecular mechanisms from single-cell multi-omics data using heterogeneous multilayer networks.
GitHub · Documentation · Paper
Benchmarking and evaluation framework for gene regulatory network inference from multimodal single-cell data.
Computational biology and medical training, with research experience across machine learning, single-cell omics, systems biology, and clinical medicine.
Université Paris-Cité / INRIA
With Stéphanie Allassonnière and Camille Schurtz, evaluating data augmentation methods for clinical trial design.
2025-present
Institut Pasteur / Heidelberg University
With Julio Saez-Rodriguez and Laura Cantini, integrating single-cell multi-omics and molecular networks.
2022-2025
Sorbonne Université ED515
Doctoral degree delivered by Sorbonne Université.
2025
IMaLiS, École Normale Supérieure, Paris
Computational biology, data analysis, cell biology, and systems biology.
2019-2021
ENS, Institut Pasteur, Institut Curie
Medical and scientific dual training track.
2019
University of Strasbourg
First year of medical studies, ranked 1st/1589, followed by the Double cursus Medicine-Sciences, ranked 2nd/15.
2017-2019
Computational Biology Institute of Heidelberg
Implemented a package to evaluate the quality and relevance of gene regulatory networks.
Feb-Jul 2022
Institut de Biologie de l'ENS
Designed a heterogeneous multilayer network approach and benchmarked joint scATAC-seq/scRNA-seq integration.
Sep 2021-Jan 2022
Institut Curie
Built and personalized a logical model to study cancer cell line responses to CHEK1 and MK2 inhibitors.
Jan-Jun 2021
Institut de Biologie de l'ENS
Built a logical model and performed dynamical simulations in plasmacytoid dendritic cells.
May-Aug 2020
University of Strasbourg
Benchmarked methods for inferring the pathogenicity of non-coding DNA variants.
Jun-Aug 2019
École de l'INSERM Liliane Bettencourt
Long-term grant for pursuing research projects while finishing medical school.
2026-2030
French government
Competitive PhD grant for École Polytechnique and ENS students.
2022-2025
DAAD
Funding for the Heidelberg internship in preparation for the PhD project.
Spring 2022