Virtual Cells

Virtual cells are computational models designed to predict how cellular systems respond to a defined intervention. This working research object tracks the move from observing biology toward testing useful biological hypotheses in silico.

Drug development is constrained by the cost and speed of physical experimentation. Useful predictive cell models could narrow the search space before scarce laboratory resources are committed.

Single-cell measurements, perturbation datasets and multimodal foundation-model methods are beginning to converge into systems that can be evaluated on response prediction.

The frontier is moving from static maps of cellular state toward models that estimate how state changes after a perturbation.

Biology is becoming more queryable: models can increasingly propose a measurable next experiment rather than only summarize past observations.

• Arc Institute
• Tahoe Therapeutics
• BioMap

• How well do models generalize across cell types and patients?
• Which benchmarks predict real experimental utility?

• Sparse and inconsistent biological data
• Limited causal validation
• Costly wet-lab feedback

• Disease-specific prediction systems
• Experiment-prioritization infrastructure
• Model-to-lab workflow tools

• Continuously evaluated virtual-cell benchmarks
• Tools that rank experiments by predicted information gain
• Computational screening workflows coupled to validation

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