Claire Plateaux
Digital Twins of organoids by multi-scale modeling
Team: Elisabeth Remy (I2M) - Anaïs Baudot (MMG)
Her background
October 2024 - Present | CENTURI PhD Student
2023-2024 | Master's (Research): Computational and Mathematical Biology (CMB), Aix-Marseille University (AMU), Marseille
2022-2023 | Master’s Year 2 in Higher Education Teaching Training; passed the external Agrégation in Mathematics (rank: 158), Université Paris-Saclay
2021–2022 | Master’s Year 1 in Fundamental Mathematics, Université Paris-Saclay
2020–2021 | Bachelor’s Year 3 (Magistère) in Mathematics, Université Paris-Saclay
2018–2020 | MPSI/MP preparatory classes (Classes préparatoires aux grandes écoles) at Collège Stanislas, Paris VI
About her PhD project
Digital twins are mathematical models designed to replicate the dynamics and behavior of their physical counterparts. In the field of cell biology, digital twins have primarily been implemented to model complex biological systems such as embryonic development and tumorigenesis. Digital twins models allow researchers to simulate and study the behavior of biological entities under various conditions, providing a powerful tool for understanding biological processes and predicting the effects of interventions.
This thesis aims to develop a digital twin model specifically for muscle organoids. Organoids are three-dimensional structures grown from stem cells that mimic the organization and function of organs. They are increasingly used as models in biological research due to their ability to replicate key aspects of organ physiology and pathology.
The project will focus on inferring intra-cellular and inter-cellular regulatory networks from a variety of omics data, including single-cell, multi-omics, and spatial omics. These networks will then be integrated into a multi-scale digital twin using advanced computational techniques such as agent-based modeling. The goal is to create a detailed and dynamic model of muscle organoids that can simulate the effects of genetic mutations and drug treatments.
Through this project, we aim to significantly advance our understanding of muscle organoids and their applications in studying muscle diseases, gene mutations, and drug discovery, ultimately contributing to better predictions and treatments for genetic conditions affecting muscle tissue.