What it is
State is Arc Institute's first virtual-cell model: given a starting transcriptome and a perturbation (drug, gene edit or cytokine), it predicts how gene expression will shift. Trained on ~170M observational and 100M+ perturbational cells across 70 contexts, it pairs a State Embedding module with a State Transition transformer. Its 2026 successor, Stack, learns cell biology in-context to generalize to unseen conditions.
Evidence trail
BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.
Model passport
How State / Stack represents biology
Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.
Biological scale
Modalities & tasks
Registry, claims and frontier intelligence
Stack
2 version records · latest curated year 2026. Model-family identity remains separate from capability and access changes.
Explore version lineage →1 normalized claim
Cell perturbation prediction · Perturbation prediction datasets
Open claim intelligence →1 connected frontier
Virtual cells · Recent preprint
Inspect research horizon →Connected research frontiers
These records describe active research directions, not guaranteed capabilities of this model. Evidence stages and unresolved questions are preserved separately.
Virtual cells that predict perturbation response
Arc Institute · Virtual Cell research community · 2026-04-30Can models forecast how cell populations respond to unseen drugs, gene edits, cytokines and environmental changes across biological contexts?
Evidence boundary and unresolved questions
Recent strict evaluations show marked performance drops under unseen contexts and metric-dependent rankings; simple baselines remain competitive on some global trends.
- Can models recover perturbation-specific mechanisms rather than average expression shifts?
- How should cell distributions, dose and time be represented?
- Which metrics predict prospective experimental usefulness?
virtual cells · perturbation · single cell · OOD generalization · world modelsOpen frontier record →Inputs and outputs
Inputs
Starting cell stateDrug, gene-edit or cytokine perturbationOutputs
Predicted post-perturbation cell statesScientific and technical profile
Scientific principles
Technology
Scientific lineage
These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.
Gene regulation and the operon model
François Jacob & Jacques MonodTarget biology, perturbation models, transcriptomic response prediction and virtual cells all require an explicit model of regulated gene programs.
The epigenetic landscape and cell-fate trajectories
Conrad H. WaddingtonSingle-cell embeddings, trajectory inference, reprogramming and virtual-cell models often represent cell identity as movement through a learned state landscape.
Programmable CRISPR–Cas genome editing
Jennifer A. Doudna & Emmanuelle CharpentierCRISPR enables target validation, disease models, perturbation atlases, functional genomics and gene-editing therapeutics.
Cooperative ligand binding
Archibald V. HillDose–response curves, receptor occupancy, multisite binding and systems pharmacology still use Hill-type models.
Information, entropy and communication
Claude E. ShannonSequence modelling, cross-entropy training, language models, mutual information and representation learning all use Shannon’s framework.
The central dogma and directional information transfer
Francis CrickMulti-omic models and sequence foundation models connect genotype, transcript and protein through this information-flow framework.
Evaluation evidence
Task-specific evidence only; not comparable as a universal leaderboard score.
Perturbation prediction datasets
Stack · Perturbation-prediction datasetsOpen evaluation of cell-state prediction under perturbation.
Claim caveats
- Cell line, tissue, dose and timepoint shifts can dominate performance.
- In-silico perturbations require experimental validation.
Known limitations
- Performance depends on the evaluation dataset and operating conditions.
- Task-specific benchmark results should not be compared across unlike domains.
- Outputs require task-specific scientific and experimental validation.
Milestones
Trained on 270M+ cells — largest single-cell perturbation model at release.
Built on the Arc Virtual Cell Atlas.