A new institution for curiosity-driven biomedical science and technology.

Headquartered in Palo Alto, California, Arc is a nonprofit research organization founded on the belief that many important scientific programs can be enabled by new organizational models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.

Arc gives scientists no-strings-attached, multi-year funding, so that they don’t have to apply for external grants, and invests in the rapid development of experimental and computational technological tools.

As individuals, Arc researchers collaborate across diverse disciplines to study complex diseases, including cancer, neurodegeneration, and immune dysfunction. As an organization, Arc strives to enable ambitious, long-term research agendas.

Arc’s mission is to accelerate scientific progress, understand the root causes of disease, and narrow the gap between discoveries and impact on patients.

The Arc Model

Arc is organized around three core concepts, each consisting of an institutional experiment in how research can be enabled.
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Core investigators

We fully fund investigators and their labs with complete freedom to pursue curiosity-driven research agendas. Arc investigators may hold faculty positions at our partner institutions, and graduate students at Stanford, UC Berkeley, and UCSF can pursue their PhD studies at Arc labs.

Technology Development

Our Technology Centers provide long-term career options for Arc scientists beyond their training period, enabling them to develop, optimize and deploy rapidly advancing experimental and computational technologies. Initial Centers will focus on machine learning, genome engineering, cellular and animal models of human disease, and multi-omics.

Translational Programs

In addition to curiosity-driven knowledge building, producing medically useful discoveries is a core mission of the Institute. We believe the current system for real-world impact of the life sciences can be greatly optimized. Arc will build translational infrastructure to accelerate the advancement of new biological insights or biotechnologies into impact on patients.

Arc Institute headquarters in Palo Alto, CaliforniaArc Institute headquarters in Palo Alto, California

Open positions

We have open positions for Technology Center group leaders, research scientists, and operational staff. We have open calls for Core Investigators as well as Science Fellows on a yearly basis. In the initial phase of the Institute, we will scale to a total headcount of approximately 250 scientific personnel. We expect to get there within three years.

Investigators

Silvana Konermann

Silvana Konermann

Stanford University

Patrick Hsu

Patrick Hsu

University of California, Berkeley

Lingyin Li

Lingyin Li

Stanford University

Luke Gilbert

Luke Gilbert

University of California, San Francisco

Hani Goodarzi

Hani Goodarzi

University of California, San Francisco

Jingtian Zhou

Jingtian Zhou

Arc Science Fellow

Uche Medoh

Uche Medoh

Arc Science Fellow

Recent work

Arc seeks to get important discoveries into the public domain as quickly as possible. Below is some recent work from labs led by Arc’s Core Investigators.

Sequence modeling and design from molecular to genome scale with Evo

PREPRINT (February 2024)

Eric Nguyen, Michael Poli, Matthew G. Durrant, Armin W. Thomas, Brian Kang, Jeremy Sullivan, Madelena Y. Ng, Ashley Lewis, Aman Patel, Aaron Lou, Stefano Ermon, Stephen A. Baccus, Tina Hernandez-Boussard, Christopher Ré, Patrick D. Hsu, and Brian L. Hie

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Abstract

The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on whole prokaryotic genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multi-scale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.

Bridge RNAs direct modular and programmable recombination of target and donor DNA

PREPRINT (January 2024)

Matthew G. Durrant*, Nicholas T. Perry*, James J. Pai, Aditya R. Jangid, Januka S. Athukoralage, Masahiro Hiraizumi, John P. McSpedon, April Pawluk, Hiroshi Nishimasu, Silvana Konermann, Patrick D. Hsu

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Abstract

Genomic rearrangements, encompassing mutational changes in the genome such as insertions, deletions, or inversions, are essential for genetic diversity. These rearrangements are typically orchestrated by enzymes involved in fundamental DNA repair processes such as homologous recombination or in the transposition of foreign genetic material by viruses and mobile genetic elements (MGEs). We report that IS110 insertion sequences, a family of minimal and autonomous MGEs, express a structured non-coding RNA that binds specifically to their encoded recombinase. This bridge RNA contains two internal loops encoding nucleotide stretches that base-pair with the target DNA and donor DNA, which is the IS110 element itself. We demonstrate that the target-binding and donor-binding loops can be independently reprogrammed to direct sequence-specific recombination between two DNA molecules. This modularity enables DNA insertion into genomic target sites as well as programmable DNA excision and inversion. The IS110 bridge system expands the diversity of nucleic acid-guided systems beyond CRISPR and RNA interference, offering a unified mechanism for the three fundamental DNA rearrangements required for genome design.

ENPP1 is an innate immune checkpoint of the anticancer cGAMP-STING pathway in breast cancer

PNAS (December 2023)

Songnan Wang, Volker Böhnert, Alby J. Joseph, Valentino Sudaryo, Gemini Skariah, Jason T. Swinderman, Feiqiao B. Yu, Vishvak Subramanyam, Denise M. Wolf, Xuchao Lyu, Luke A. Gilbert, Laura J. van’t Veer, Hani Goodarzi, and Lingyin Li

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Abstract

Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) expression correlates with poor prognosis in many cancers, and we previously discovered that ENPP1 is the dominant hydrolase of extracellular cGAMP: a cancer-cell-produced immunotransmitter that activates the anticancer stimulator of interferon genes (STING) pathway. However, ENPP1 has other catalytic activities and the molecular and cellular mechanisms contributing to its tumorigenic effects remain unclear. Here, using single-cell RNA-seq, we show that ENPP1 in both cancer and normal tissues drives primary breast tumor growth and metastasis by dampening extracellular 2′3′-cyclic-GMP-AMP (cGAMP)–STING-mediated antitumoral immunity. ENPP1 loss-of-function in both cancer cells and normal tissues slowed primary tumor growth and abolished metastasis. Selectively abolishing the cGAMP hydrolysis activity of ENPP1 phenocopied ENPP1 knockout in a STING-dependent manner, demonstrating that restoration of paracrine cGAMP–STING signaling is the dominant anti-cancer mechanism of ENPP1 inhibition. Finally, ENPP1 expression in breast tumors deterministically predicated whether patients would remain free of distant metastasis after pembrolizumab (anti-PD-1) treatment followed by surgery. Altogether, ENPP1 blockade represents a strategy to exploit cancer-produced extracellular cGAMP for controlled local activation of STING and is therefore a promising therapeutic approach against breast cancer.

Environmental challenge rewires functional connections among human genes

PREPRINT (August 2023)

Benjamin W. Herken, Garrett T. Wong, Thomas M. Norman, Luke A. Gilbert

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Abstract

A fundamental question in biology is how a limited number of genes combinatorially govern cellular responses to environmental changes. While the prevailing hypothesis is that relationships between genes, processes, and ontologies could be plastic to achieve this adaptability, quantitatively comparing human gene functional connections between specific environmental conditions at scale is very challenging. Therefore, it remains unclear whether and how human genetic interaction networks are rewired in response to changing environmental conditions. Here, we developed a framework for mapping context-specific genetic interactions, enabling us to measure the plasticity of human genetic architecture upon environmental challenge for ∼250,000 interactions, using cell cycle interruption, genotoxic perturbation, and nutrient deprivation as archetypes. We discover large-scale rewiring of human gene relationships across conditions, highlighted by dramatic shifts in the functional connections of epigenetic regulators (TIP60), cell cycle regulators (PP2A), and glycolysis metabolism. Our study demonstrates that upon environmental perturbation, intra-complex genetic rewiring is rare while inter-complex rewiring is common, suggesting a modular and flexible evolutionary genetic strategy that allows a limited number of human genes to enable adaptation to a large number of environmental conditions.