Samuel King (@samuelhking) / X

Samuel King

160 posts

Samuel King

@samuelhking

@Stanford Bioengineering PhD candidate / Biological AI in @BrianHie’s lab @arcinstitute

Stanford, CA

samuelking.cargo.site

Joined April 2020

Pinned
Samuel King
@samuelhking
Sep 17, 2025
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 🧵
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Samuel King
@samuelhking
Sep 17, 2025
Grateful to be at Arc, it was the best place for this work!
Arc Institute
@arcinstitute
Sep 17, 2025
In a new preprint from @brianhie’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.
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
We chose to generate bacteriophage genomes, given their utility in biotechnology and therapeutics, and because they are safe and feasible to test in the lab. Phages are viruses that infect and kill bacteria, and are emerging as a promising strategy to combat rising antibiotic
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
We’re beyond excited for a new era of genome design and to see where researchers might take this. Read more in our preprint, and reach out if you have questions or thoughts!
biorxiv.org
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...
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
Also, check out our blog post giving a concise overview of the technical developments required for phage genome design arcinstitute.org/news/hie-king-…. Thanks to @arcinstitute, @bioe_stanford, and all the other amazing people who supported this work 🧬
How We Built the First AI-Generated Genomes | Arc Institute
From arcinstitute.org
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
I’ll start by recognizing that this work wouldn’t have been possible without the incredible support of my PhD advisor @brianhie, and the brilliant labmates and scientists who I had the honor of working with: @driscoll_cl @_david_li @dan_guo_ @aditimerch @garykbrixi @maxewilkinson
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
DNA language models such as Evo 1 and Evo 2, trained on millions of genomes, learn complex features of genomes at an unfathomable scale. These models work much like ChatGPT, except for DNA. We’ve previously shown that they can generate novel CRISPR-Cas systems, amongst others.
Brian Hie
@BrianHie
Feb 19, 2025
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.
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
Designing a genome is an incredibly complex task. The overwhelming number of considerations has limited what we’ve previously been able to achieve in synthetic biology.
7.5K
Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
The viable generated phages harbored hundreds of novel mutations, many of which do not map to any sequence seen in nature. The cryo-EM structure of one phage revealed a genome packaging mechanism designed by Evo that was previously found lethal in rational engineering attempts.
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
But can DNA language models generate complete, viable genomes? To investigate this, we developed a modular framework for designing phages targeting a chosen bacteria, to maximize benefit for phage-based biotechnologies and therapeutics.
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
We synthesized and tested 285 generated phage genomes in E. coli C. 16 generated phages inhibited growth in E. coli C but showed no off-target infection in E. coli strains outside of ΦX174’s natural range, demonstrating the intended host specificity.
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
We first needed clear design criteria to guide our genome generation process. As a design template, we chose ΦX174, a classic phage in molecular biology, which was the first genome ever sequenced and synthesized.
5.7K
Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
To explore the utility of our genome design method for creating resilient phage therapies, we evolved a generated phage cocktail against three different ΦX174-resistant E. coli strains. The generated cocktail rapidly overcame resistance against all strains while ΦX174 did not.
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Samuel King
@samuelhking
Sep 17, 2025
Replying to @samuelhking
By fine-tuning Evo 1 and Evo 2 on Microviridae sequences, we honed the models’ understanding of ΦX174-like genomes, which allowed us to generate sequences fulfilling our design criteria with a high success rate.
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