Decoding knee osteoarthritis through multiscale in silico modeling: an FNRS aspirant project for Jeanne Delhez
Jeanne Delhez explains her background and her research topic in the context of obtaining her FNRS Aspirant mandate (Research Fellow).
Her background
I began my studies in engineering at the University of Liège in September 2019. I quickly chose to specialize in the biomedical field, driven by the desire to apply engineering skills to improve patients’ health. In June 2024, I obtained my Master’s degree in Biomedical Engineering, with a specialization in biomechanics, biomaterials, and tissue engineering.
During my Master’s, I completed my thesis within Professor Ponthot’s laboratory, focusing on the application of the PFEM method for the numerical modeling of blood flows and their interactions with vessel walls in various cardiovascular situations. This project gave me the opportunity to participate in, and win, the Super Prix FABI (Royal Federation of Belgian Engineers’ Associations). This competition, dedicated to scientific communication and vulgarization, offered me a unique chance to practice conveying complex ideas effectively and to share my work with passion and conviction.
These experiences opened my eyes to the many possibilities for collaboration between engineers and medical professionals, as well as to the potential of digital twins to better understand human physiology, refine diagnosis, and improve treatments. Motivated by the desire to contribute to meaningful and impactful research, I applied for a fellowship from the FNRS. In October 2025, I began a mandate as an FRS-FNRS Aspirant (Research Fellow) within the « Biomech Research Unit » of Professor Liesbet Geris, where I am now pursuing my PhD.
Her research
My research focuses on multiscale in silico modeling of knee osteoarthritis. Osteoarthritis (OA) is indeed a highly prevalent disease, affecting 1 in 8 adults worldwide, and with projections indicating a concerning rise in overall cases in the next decades.
Its dynamics is driven by a complex interplay of mechanical, genetic, metabolic, and inflammatory factors, making the development of effective treatments particularly challenging. Given this complexity, in silico modeling is likely to play an important role for designing, guiding, and evaluating new therapeutic approaches. Currently, no available models mechanistically couple the multiscale mechanics of the joint with the intricate biological signaling, limiting their potential for regenerative strategies design. My project aims to bridge this gap by developing a multiscale model of the knee joint capable of capturing the complex processes underlying cartilage homeostasis and OA progression. A particular focus will be placed on mechanotransduction and inflammation, enabling the linkage of organ-, tissue-, and cellular-level mechanical dynamics with biological intracellular signaling pathways. Available experimental results from in vitro and in vivo tools will serve to complete the model and validate its predictions. The model will be applied to explore endogenous repair strategies by identifying relevant druggable targets, paving the way for effective OA treatments.
