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At Arc, we believe virtual cell models can fill a critical gap: predicting the effects of genetic mutations, environmental changes, and small molecule treatments in biological systems we cannot readily test at-scale or cost-effectively in the lab. Realizing that vision requires an ambitious effort, generating causal, single-cell resolution data at a scale that does not yet exist, so models can learn to reliably predict any cell type's biological response to a perturbation. We're building large-scale perturbation datasets as a resource toward that goal, the first step to generate the quality and breadth of data virtual cell models need to make trustworthy predictions.

Life as we know it depends on the flow of electrons. From single-celled organisms to human neurons, the thermodynamics of this energy flow remain the same: fuel oxidation strips electrons from nutrients, and those electrons move from lower to higher reduction potentials until they come to rest on a terminal electron acceptor (TEA). Without TEAs to clear electrons, NAD+ cannot be regenerated, fuel oxidation stalls, and energy production collapses.

Each cell in our body inherits the same DNA, yet becomes one of hundreds of cell types. We make immune cells that hunt down viruses, brain cells that pattern our memories, and pacemaker cells that generate electricity to drive our heartbeat. To understand how cells can have the exact same genetic code, but do completely different things, Arc Science Fellow Jingtian Zhou has built a powerful 3D genome toolkit, developing both the wet-lab assays that measure how DNA physically folds inside the nuclei of single cells and the algorithms that make that data interpretable.

Registration for the 2026 Virtual Cell Challenge is open at virtualcellchallenge.org. This year the task is zero-shot: we are not releasing a training set, and the evaluation dataset is far more expansive than last year’s challenge. Models must predict CRISPRi knockdown responses in six cell lines they have never seen perturbed, using only the unperturbed state of those cells and a list of genes to knock down. The grand prize is $100,000, and the Challenge is again sponsored by NVIDIA, 10x Genomics, and Ultima Genomics.

Four years ago, John Pluvinage was a neurology resident at UCSF trying to solve one patient's mystery: a 67-year-old woman whose memory and ability to walk were declining, whose bloodwork kept coming back normal, and whose diagnosis was inconclusive.