Stories about Arc’s research, people, and initiatives

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.

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Respiration and fermentation are the two categories of pathways for maintaining electron flow.

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.

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Why Jingtian Zhou is building high-resolution maps of the 3D genome

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.

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The 2026 Virtual Cell Challenge

Programmable biology has arrived in a few different ways, each time defined by a new type of RNA-guided mechanism: RNA interference, then CRISPR systems for programmable cutting of nucleic acids. More recently, in 2024, we uncovered the first RNA-guided DNA recombinases. These bridge recombinases bind bridge RNAs: bispecific guide RNAs able to recognize two different DNA molecules simultaneously.

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Overcoming the insertion bias in natural bridge recombinase systems for efficient excision
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