Congrats to @brianhie.bsky.social and @samuelhking.bsky.social at the @arcinstitute.org on a remarkable publication in @science.org. Time will demonstrate the philosophical importance of this landmark paper.

One of our most popular preprints of 2025 is now published in @science.org. Congrats to @brianhie.bsky.social, @samuelhking.bsky.social, and team on establishing a framework for generating and evaluating AI-generated genomes. - Access the paper: www.science.org/doi/10.1126/...

Generative design of bacteriophages with genome language models

Many important biological functions arise not from single genes but from complex interactions encoded by entire genomes. We report the first generative design of complete bacteriophage genomes using g...

science.org

Arc Institute@arcinstitute.org · 11mo ago

In a new preprint from @brianhie.bsky.social's lab, the team reports the first generative design of viable bacteriophage genomes. Leveraging Evo 1 & Evo 2, they generated whole genome sequences, resulting in 16 viable phages with distinct genomic architectures.

Imagine programming protein, DNA, and RNA systems like you would write computer code, or even by natural language prompting of an AI agent. @brianhie.bsky.social and team just made this a reality with Proto: a high-level programming language for generative biology.

Tomorrow, at the #SystemsVirologyJournalClub, @samuelhking.bsky.social will present his work with @brianhie.bsky.social using AI genome language models to generate novel, high-fitness bacteriophages.

Shira Weingarten-Gabbay@shiraweingarten.bsky.social · 10mo ago

🚨 Systems Virology Journal Club – 8th Series! 🚨 #BillSchneider and I are delighted to announce another round of cutting-edge talks in #SystemsVirology! 🦠💡 Join us and an outstanding lineup of speakers, starting Oct 30. Free registration: shiraweingartengabbay.com/systems-viro... 🔬✨

What if we could autocomplete DNA based on function? Today in @Nature, we share semantic design—a strategy for function-guided design with genomic language models that leverages genomic context to create de novo genes and systems with desired functions. 🧵 www.nature.com/articles/s41...

In a new preprint from @brianhie.bsky.social's lab, the team reports the first generative design of viable bacteriophage genomes. Leveraging Evo 1 & Evo 2, they generated whole genome sequences, resulting in 16 viable phages with distinct genomic architectures.

A landmark paper from Brian Hie’s group at the Arc Institute. The de novo design of the synthetic genome of an entirely novel biological entity www.biorxiv.org/content/10.1...

Generative design of novel bacteriophages with genome language models

Many important biological functions arise not from single genes, but from complex interactions encoded by entire genomes. Genome language models have emerged as a promising strategy for designing biol...

biorxiv.org

Genome foundation models, Evo 1 and Evo 2, have now generated viable bacteriophage genomes, demonstrating experimental validation of whole genomes designed by AI! @arcinstitute.org @brianhie.bsky.social @samuelhking.bsky.social Read more at GEN: www.genengnews.com/topics/artif...

AI Designs Viable Bacteriophage Genomes, Combats Antibiotic Resistance

AI-guided design of 16 functional bacteriophage genomes offers a path for phage-based therapies against antibiotic-resistant infections.

genengnews.com

Many of the most complex and useful functions in biology emerge at the scale of whole genomes. Today, we share our preprint “Generative design of novel bacteriophages with genome language models”, where we validate the first, functional AI-generated genomes 🧵

We trained a genomic language model on all observed evolution, which we are calling Evo 2. The model achieves an unprecedented breadth in capabilities, enabling prediction and design tasks from molecular to genome scale and across all three domains of life.

Excited to have the first project of my PhD out!! By leveraging genomic language model Evo’s ability to learn relationships across genes (i.e., "know a gene by the company it keeps"), we show that we can use prompt-engineering to generate highly divergent proteins with retained functionality. 🧵1/N