The aging process represents one of biology's most complex system-level phenomena. A major challenge is moving from observing its correlates to identifying its fundamental, targetable bottlenecks. In this talk, I will explore a reverse-engineering approach, using pharmacological interventions in model organisms to deconstruct the mechanisms of aging and pinpoint promising avenues for intervention.  I will discuss how we can leverage existing biological data and what new, targeted measurements are required to fill critical gaps. A key question is the selection of appropriate model organisms that offer the right balance of physiological complexity, experimental tractability, and translational relevance for aging research. Furthermore, I will examine the role of artificial intelligence in this endeavor: while AI excels at finding generalizable patterns, its success is critically dependent on the quality and nature of the underlying data—an area where significant improvements are needed.  I will present examples from my work across multiple species, including the development of a scalable high-throughput platform for pharmaco-biology in Daphnia. This system allows us to characterize drug-induced perturbations and link them to lifespan and healthspan outcomes. We will discuss a computational framework to regress macro-phenotypes to the molecular pathways. Finally, I will outline central challenges in the field and propose concrete directions for researchers interested in joining the effort to reverse engineer aging.

If you would like to attend the seminar, please register here.