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One of the goals of the Virtual Cell Challenge is to build a community of people who see the possibilities of applying machine learning to complex biology.

RNA-binding proteins (RBPs) guide nearly every step of an RNA molecule's life. From the moment a gene is transcribed, they help determine how the transcript is spliced, where it travels in the cell, how often it is translated into protein, and when it is broken down. Humans have roughly 1,500 RBPs, accounting for about 7.5% of our protein-coding genes, and their malfunction has been linked to a range of diseases, including cancer.

A truly useful virtual cell model should be able to predict how cells respond in conditions it has never seen.

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.