Abstract
In this presentation, I will share our progress in developing a cybergenetic platform aimed at optimizing therapies for Metabolic (dysfunction-) Associated Steatotic Liver Disease (MASLD)—a leading cause of liver transplantation that affects nearly 2 billion adults worldwide. I will begin by outlining the current challenges in disease management, and then explain how reframing therapy design as a control problem creates opportunities to automate the identification of personalized treatments and test their efficacy in real time. Next, I will describe our integrated in silico/in vivo platform, highlighting two key components: a high-throughput microfluidic device that enables long-term monitoring of both 2D and 3D mammalian cellular models, and human-relevant preclinical models of MASLD. Finally, I will outline our plans to deepen the understanding of chronic disease aetiology and leverage this insight to develop both single- and multi-agent interventions. This multidisciplinary approach promises to pave the way for more effective and precisely tailored treatments.