Speaker
Description
By 2030, an estimated 78 million people will be living with Alzheimer’s disease (AD). Despite decades of research, clinical trials continue to face failure rates exceeding $95\%$, highlighting the need for improved mechanistic understanding of disease progression and therapeutic response. Mathematical modelling provides a framework to integrate biological processes across scales and enable safe, cost-effective in silico experimentation.
AD is characterised by the aggregation of proteins into toxic species that propagate through the brain in a prion-like manner. These processes interact with biological pathways, including clearance mechanisms and neuroinflammatory responses, ultimately driving neurodegeneration.
In this talk, I present a mathematical framework for the spatiotemporal dynamics of AD that integrates protein aggregation, network-mediated transport, clearance, and therapeutic interventions. Reaction–diffusion models on human brain networks describe the propagation of protein pathology together with clearance and inflammation. Complementing this approach, models of aggregation are formulated as spatially extended nucleation–elongation–fragmentation systems. These models are integrated with quantitative systems pharmacology frameworks describing drug–target interactions and biomarker responses, allowing therapeutic interventions to be incorporated directly into disease dynamics. Calibrated to human and clinical data, this multiscale framework can reveal mechanistic links between clearance, protein propagation, and neurodegeneration. Analytical and computational results aim to provide a quantitative basis for identifying mechanisms whose modulation may slow AD progression.
