Abstract A new paradigm is emerging at the intersection of artificial intelligence and experimental biology, where cells are no longer merely observed, but comprehensively modeled, queried, and predicted in silico. New measurement technologies and the ability to genetically manipulate cells precisely have opened the way to measure cells in extraordinary detail under tens of thousands of perturbations. Concomitantly, AI foundation models are learning to represent, simulate, and even anticipate cellular behavior. This essay traces the convergence of these revolutions, showing how they are giving rise to “virtual cells”: integrative models that unify diverse molecular and spatial data into coherent, functional representations that can generalize across biological contexts and conditions. Beyond representing and interpreting biological lab measurements, virtual cells aim to predict unseen outcomes, imagine new contexts, and guide discovery. In closing the loop between data generation and hypothesis testing, AI is transforming biology into a self-refining, interactive science, resulting in a profound shift: from observing life to actively modeling it, with implications for precision medicine, biotechnology, and the scientific method.
Bunne et al. (Thu,) studied this question.
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