Alzheimer’s disease (AD) is increasingly defined not by clinical symptoms alone but by biomarker profiles reflecting amyloid accumulation, tau pathology, and neurodegeneration. While these biomarkers offer powerful diagnostic and prognostic tools, interpreting their complex and heterogeneous trajectories remains a challenge—one that cannot be fully addressed through raw measurements or clinical intuition. In this talk, I will present a personalized mechanistic modeling approach to characterize individual biomarker trajectories in AD. Using longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we applied a biologically grounded system of ordinary differential equations to model the interactions among amyloid, tau, neurodegeneration, and cognition. Personalization of these models yields digital twins—computational representations of individuals—that can forecast future biomarker evolution and cognitive decline. I will highlight how model parameters reveal distinct endophenotypic subtypes with divergent trajectories, and how this modeling framework outperforms simpler approaches in predicting individual progression. Finally, I will discuss how mechanistic modeling supports in silico simulation of therapeutic interventions, laying the foundation for precision medicine in AD.