Mots-Clés
systems biology
transcriptomics
epigenomics
single-cell omics
machine learning
explainable AI
gene regulatory networks
aging
cellular senescence
Description
Position Overview
We seek a motivated and skilled PhD student with expertise in OMICS data analysis and/or machine learning to join our dynamic research team at the Mondor Institute of Biomedical Research (IMRB-INSERM U.955) of the University Paris-Est Créteil (UPEC) in the “Senescence, metabolism and cardiovascular diseases team” (Direction: Genevieve Derumeaux) under the supervision of José-Américo Freitas. The successful candidate will contribute to cutting-edge research to integrate multi-omics datasets and decode the gene-regulatory network controlling cellular senescence and its role in age-associated diseases. This position offers an exciting opportunity to work in a collaborative and stimulating research environment.
Context
Cellular senescence (CS) is a persistent cell cycle arrest triggered by various cellular stressors, such as hyperactive oncogenes, DNA damage, or replicative exhaustion. It is characterized by the secretion of several inflammatory molecules, whose chronic increase raises the incidence of age-related pathologies, including chronic cardiovascular and lung diseases. As a result, the elimination or reprogramming of senescent cells has proven beneficial in treating aged mouse models, and several senotherapeutics are currently being tested in clinical trials. Despite substantial advances in determining the molecular mechanisms regulating CS, a comprehensive framework that can explain and predict CS dynamics is still lacking.
Objectives
This project aims to unravel how epigenetic mechanisms control gene expression in cells undergoing senescence. Its outcomes are indispensable for minimizing the deleterious impact of senescent cells on their surrounding tissue and on the cross-talk between organs.
The successful candidate will utilize both in-house and publicly available repositories containing bulk and single-cell datasets to infer the underlying gene-regulatory networks in the context of CS. We will explore multiple approaches to integrate these datasets, which will be tailored to the candidate’s skills and preferences. These approaches include, but are not limited to:
- Trajectory inference tools such as pseudotime ordering and RNA velocity from transcriptome datasets;
- Transcription factor footprinting from DNA accessibility assays;
- Machine learning methods for exploratory and predictive analyses, focusing mainly on explainable architectures.
Relevant team publications:
[1] Computational Modeling of Aging-Related Gene Networks: A Review. Freitas, J. A. N. L. F., & Bischof, O. Frontiers in Applied Mathematics and Statistics. 10:1380996. DOI: 10.3389/fams.2024.1380996
[2] Dynamic Modeling of the Cellular Senescence Gene Regulatory Network. Freitas, J. A. N. L. F., & Bischof, O. Heliyon. E14007. (2023). DOI: 10.1016/j.heliyon.2023.e14007
[3] AP-1 imprints a reversible transcriptional program of senescent cells. Martínez-Zamudio, R. I., Roux, P. F., Freitas, J. A. N. L. F., Robinson, L., Doré, G., Sun, B., Belenki, D., Milanovic, M., Herbig, U., Gil, J. & Bischof, O. (2020). Nature Cell Biology, 1-14. DOI: 10.1038/s41556-020-0529-5
Qualifications
- Msc (or equivalent) in Bioinformatics, Biostatistics, Computer Science, or a related field;
- Proficient programming skills (Python, R, etc).
- Experience with bioinformatics tools and databases.
- Excellent communication and collaboration skills.
- Motivated, creative, and enthusiastic about pursuing innovative research questions in cellular senescence, aging, and systems biology.
Preferred Qualifications
- Unix-based systems;
- Virtual environments (Conda) and/or containers (Docker, ApptainerSingularity)
- Workload managers (e.g., Slurm, SGE);
- Machine learning frameworks (e.g., PyTorch, Tensorflow);
- Single-cell analyses (e.g., Seurat, Scanpy);
- Knowledge in cellular and molecular biology, especially epigenetics and transcriptome dynamics;
- Knowledge in calculus, optimization, and linear algebra;