In Silico Modeling of Osteochondral Pathophysiology and Regeneration. Identification of Molecular Signatures and Therapeutic Targets with Mechanistic and Data-Driven Network Models

“In silico modeling of osteochondral pathophysiology and regeneration:Identification of molecular signatures and drug targets with mechanistic and data-driven network models”

Chondrocyte maturation and cartilage to bone transition in the context of the growth plate development and osteoarthritis (OA) is a process that is thoroughly regulated with inputs from multiple biochemical and mechanical signals active at multiple length scales. We used mathematical and computational (in silico) approaches to integrate data and mechanistic information in order to study and understand that complexity and identify therapeutic strategies against cartilage degradation in OA or guide experiments in bone regenerative strategies. In particular, the focus of my research was on intracellular regulatory mechanisms. Four case studies were covered: 1) identification of typical OA intracellular network signatures with training and validation of a machine learning classifier for quantitative evaluation of in vivo disease models. 2) development and experimental validation of a virtual articular chondrocyte to screen in silico molecular target perturbations and to predict conditions against hypertrophy, a driver of cartilage degradation in OA. 3) Prediction of the experimental conditions with the most bone forming potential in bone TE experiments thanks numerical simulations. 4) Extension and adaptation of the regulatory network model as an intracellular module for integration in a multi-scale biomechanical model of the knee joint and study of the role of inflammatory and mechanobiological pathways on extracellular matrix status and chondrocyte phenotype.