Drug discovery often measures biology at the cell level, while therapies must ultimately work across tissues, organs, and whole patients. This scale mismatch means that even highly accurate cellular predictions can fail to translate in the clinic. This session explores strategies to bridge that gap. How do we connect single-cell dynamics to organ-level physiology and patient outcomes? How do we preserve biological context while scaling models? And how do we ensure that virtual biology does not stop at simulation, but informs real therapeutic decisions? Speakers will discuss multiscale modeling that links molecular and cellular systems to higher-order biology; spatial and high-dimensional phenotypic data that retain context; and integrated computational–experimental loops that translate cellular signals into clinically meaningful biomarkers. Together, we ask: how do we ensure virtual biology reflects not just what cells do in isolation, but how biology behaves in the full complexity of patients?

As we move toward the “virtual cell” and ultimately the “virtual organism,” the AIxBIO ecosystem faces a fundamental challenge: where does biology actually compute? While our ability to measure molecular events has advanced dramatically, predicting how those signals translate into emergent, system-level outcomes remains a core bottleneck in programmable biology. This session brings together leaders across AI, synthetic biology, and medicine to explore the computational bottleneck, mapping where predictive power breaks down from molecules to cells to organisms. It will examine how to measure emergence at scale by generating causal, time-resolved, perturbation-rich datasets across diverse biological contexts, and how to close the reality gap with in vivo feedback, using next-generation sensors and real-world data to continuously calibrate and validate models in living systems.