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Virtual cell

Using models trained on omics data to estimate how a cell will respond before the experiment is run — Virtual Cell.

What this research is trying to do

A virtual cell moves the cell into a computable form, using models trained on omics data, so that an outcome can be estimated before the experiment is performed. How expression moves when a given gene is suppressed, which direction a condition pushes the cell state — run that on the model first, and spend the actual bench work on the conditions most worth confirming.

The point is not to replace experiments; it is to change their order. The model does the wide sweep across candidates, and hands and reagents concentrate on the few conditions it narrowed down to.

Why now

Single-cell and spatial omics have begun recording cell state at a resolution without precedent, and large perturbation datasets — along with foundation models trained on them — are appearing. "A digital twin of the cell" has stopped being a slogan and become a research question that can be tested. The open problem is not whether a model exists, but deciding from the data which of its predictions can be trusted.

What contextBio does

Model quality begins with the quality of the training data. AURORA processing raw omics reproducibly is the data-side foundation for this work, attaching tissue context connects to spatial biology in cancer, and the step from the cell up to the patient is described in virtual hospital.

Where it stands now

Work runs on two tracks. On the data side, omics processed by AURORA is being wired into a well-ordered store a model can read from. On the reasoning side, we are building and validating an in-lab co-scientist agent that weaves curated databases, our own analysis code and the literature into designing and interpreting analyses — still a research stage, before anything opens as a service.

References

89 items

This page draws on the review manuscript Perspectives on Virtual Cell to Digital Twin Technology; below is the literature it cites.

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  89. López-De-Castro M, García-Galindo A, González-Gomariz J, Armañanzas R. Conformal inference for reliable single cell RNA-seq annotation. Bioinformatics. 2025;41[10]:btaf521. https://doi.org/10.1093/bioinformatics/btaf521 Figure 1. The cell-to-organism digital twin continuum. Schematic overview of the central thesis: the AI virtual cell is the foundational building block of the biomedical digital twin, and the two integrate into a multiscale continuum spanning the cell, tissue, organ and whole-body scales. Three enabling technologies (top) make the continuum tractable. The central ladder shows the scales at which published systems now operate; at the cellular rung these are grouped by model class rather than by individual system, comprising foundation models, knowledge-grounded models that inject ontologies and graphs, multimodal integration across transcriptome, chromatin and protein, perturbation world models, and mechanistic whole-cell simulation. Upward arrows denote bottom-up compositional aggregation of learned cellular representations into higher-scale twins; downward arrows denote top-down propagation of physiological and clinical constraints. Applications (right) are aligned with the scale at which they principally operate. At the base, the lab-in-the-loop closes the defining feedback loop of a twin and is drawn as three nested loops: a computational inner loop that triages candidates before any assay is run and reports calibrated uncertainty; an experimental middle loop in which patient-derived organoids act as a physical twin whose measurements are assimilated; and a translational outer loop in which monitored clinical outcomes refine cellular and organ-scale priors. Results from every loop return to the model through active learning. A cross-cutting foundation of credibility and governance underpins clinical translation at and across every scale. Figure 2. The lab-in-the-loop as three nested validation loops. The quality of the lab-in-the-loop is the rate-limiting step of the cell-to-organism continuum; this figure makes its internal structure explicit. A virtual cell or digital twin proposes candidate perturbations, therapies or trajectories. These enter a computational inner loop [1] in which they are triaged in silico before any assay is run, using coverage of the phenotype space to detect mode collapse, rank correlation between predicted and measured responses, adversarial validation in which a classifier trained to separate generated from measured profiles should approach chance performance, and calibrated uncertainty, for which conformal prediction returns label sets whose size flags contexts the model has not seen. Surviving candidates enter an experimental middle loop [2] of CRISPR perturbation, patient-derived organoid and organ-on-chip assays executed under laboratory automation, whose measurements are assimilated by the model. Validated findings enter a translational outer loop [3] of in silico trials, precision oncology and monitored clinical outcomes assessed against risk-based regulatory expectations. Results from each loop return to the model through active learning, in which the acquisition rule ranks candidate experiments by the reduction in predictive error they are expected to yield rather than by the size of the predicted effect. The inner loop cannot be skipped: independent benchmarks report that self-supervised cellular embeddings are matched or exceeded by simple baselines, that accuracy degrades when models are transported across biological contexts, and that metric choice alone can reverse model rankings.

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