Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).
Brixi, G. et al. Genome modelling and design across all domains of life with Evo 2. Nature 652, 1349–1361 (2026).
Avsec, Z. et al. Advancing regulatory variant effect prediction with AlphaGenome. Nature 649, 1206–1218 (2026).
Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024).This perspective article defines the concept of the AIVC and outlines the design principles and collaborative strategies needed to realize AI-driven, multiscale simulations of a living system.
Karr, J. R. et al. A whole-cell computational model predicts phenotype from genotype. Cell 150, 389–401 (2012).This pioneering study presents the first mechanistic whole-cell model that integrates all molecular processes of M. genitalium using diverse mathematical formalisms to unify fundamentally different cellular processes and experimental measurements, enabling genotype-to-phenotype prediction.
Macklin, D. N. et al. Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science 369, eaav3751 (2020).
Lu, H., Kerkhoven, E. J. & Nielsen, J. Multiscale models quantifying yeast physiology: towards a whole-cell model. Trends Biotechnol. 40, 291–305 (2022).
Sanchez, B. J. et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol. Syst. Biol. 13, 935 (2017).
Elsemman, I. E. et al. Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies. Nat. Commun. 13, 801 (2022).
... continue reading