using only a few observations of cells within a tissue P(z|y), along with the more readily available probability P(y) of a cell being in a specific state. This development illustrates one way to bridge landscapes across biological scales. While the proposed framework provides a theoretical basis for bridging scales, more applied approaches are available 436 (e.g. coarse grained 437 , agent-based 438 , cell-cell communication models , or virtual cells 440 ). In silico representations aim to complement experimental evidence and facilitate the understanding, prediction, simulation, and optimization of biological tissues over time 226 . In the meantime, valuable insights can be gained from the models themselves. For instance, how cellular collectives, or decentralized multi-agent systems, are programmed to reach specific outcomes 441 . Improved modeling, prediction and simulation approaches could accelerate the development of new organoids.