Erm ... you have A T C G. You can have a gazillion of combinations there.
Of course BY DEFAULT it will always be slower than ANY combination you would desire to have - and you most definitely do not need AI slop to have that either. Do we need AI slop for generating any permutation of those 4 letters now? So what is the point of stating "can construct".
IF the synthesis method works, then that is the focus to be debated, not the AI slop is our master-thinker now.
> “We really want this to be an enabling platform,” says Robinson. “We want people to do cool things with the technology.”
And I think they patented this (if it really works), so ... enabling platform, right.
Interestingly the article omits many key questions to be asked here. If the method already works as-is, why isn't everyone using it? If it is cheaper and faster, then logically it would already be used or usable.
Never mind artificial genomes - let me have a snapshot of my DNA sequenced and re-created from scratch say 20 years later - telomeres and all.
It's hard to quantify the impact of new foundational tools like this at launch. Most of the time it falls flat, but even the successes are difficult. For example, CRISPR has led to interesting experiments and treatments on the way, but the effect does feel muted compared to the initial predictions. But there are many other related techniques that can be pulled out of this original research (e.g. dCas9 which lets you operate without cutting).
Similar story with cellular reprogramming.
Eventually one of these things will surface that will be GPU/transistor type innovations.
So... is the "Generative AI" tie in (Mentioned many times, starting at the top of the article) used mainly get funding and press? The core part of this seems to have nothing to do with AI. I am sus this is bullshit marketing based on #1: How many times I've clicked an article, and have AI blasted all over it for sus reasons, and #2: Being able to cheaply and reliably synthesis custom DNA seqs longer than a few hundred/thousand bps is a broadly useful tech for current and future applications.
So: #1: This is really good news. #2: Do better with the hype/bull. It undermines your credibility. So now I start questioning whether this works as well as advertised, and what else they are being shady about.
> Erm ... you have A T C G. You can have a gazillion of combinations there.
> Of course BY DEFAULT it will always be slower than ANY combination you would desire to have - and you most definitely do not need AI slop to have that either. Do we need AI slop for generating any permutation of those 4 letters now? So what is the point of stating "can construct".
The bit right before your quote says why:
giving scientists a fast, affordable, and accurate way to physically build the novel genetic sequences that predictive models are now producing faster than anyone can construct them.
Also, predictive models is broader than Transformers, but even then Transformers in the context of DNA is somewhat different from the context of natural (or even programming) languages; and even more than that, given how effective even mediocre early models were for code not useful to dismiss all of it even when it is definitely "slop" in other domains: https://www.nature.com/articles/s41592-024-02523-zThe answer I got was along the lines that they were simply going to get around to do the actual lab work at some point.
DNA synthesis technology hasn't really been a blocker for generative bio projects except at the full chromosome level.
And I think simply generating a full chromosome and booting it up without doing due diligence is probably a recipe for disaster.
We honestly aren't that far away from AI slop enzymes, AI slop ligases, and eventually AI slop bio weapons...
I broadly agree with you on the AI hyping. Data quality and quantity is not high enough in my opinion.
Yeah, it feels like we need a phase transition in the speed and practicality of the process. But I don't believe we need a single concrete lab tech.
Years ago when I did research, my impression was that there was complexity galore. A researcher on Drosophila developmental signaling would have a very disjoint knowledge domain than that of a researcher in horizontal gene transfer and antibiotic resistance. Both would exist in a different planet altogether than a clinician prescribing a cancer treatment. And the three of them would generally lack the tooling that somebody doing systems biology was used to.
So, to me, the key thing we need is some sort of "domain cement", or a good way to pull operative knowledge and usable skills from everywhere.
If you're talking about Twist's gene fragment product, they advertise that as maxing out at 5 kb. Most, if not essentially all, of that month delivery time is likely the combination, not the oligo pool production. I think the Sidewinder people are actually using Twist pools; they're doing up to 12.5 kb.
By comparison, we recently needed something in the 20 kb range, with a not-so-great sequence, and it was a multi-month process to have a company produce it.
Why do you think that?
are the limits on not-so-greatness for sidewinder known?
Isn't that what LLMs are shaping up to be? Once we manage to divorce the knowledge from the weights in some way we could have in effect a frontier model whose awareness was limited to the sum total of the scientific literature.
But there are a lot of analogies to computation in bio as a physical, atomic forces-driven, massively parallel computer, so it's possible there will be something related to electronics and computers that falls out. For example, there's also applications directly related to other fields including DNA storage of data and neuron-based computation.
We're speaking about gene synthesis, not about DNA sequencing
Biology has had many of these over the centuries.
> But there are a lot of analogies to computation
People have been saying this since pretty much the start of computation and I don't think anything's ever come of it.
A new method for writing DNA promises to unlock the potential of generative AI in biology, giving scientists a fast, affordable, and accurate way to physically build the novel genetic sequences that predictive models are now producing faster than anyone can construct them.
The technique, called Sidewinder, can assemble dozens of genetic sequences simultaneously in a single test tube, producing just one incorrect junction for every 10 million assembly events—a level of precision that far surpasses conventional methods, which misfire roughly once every 10 to 30 joins. Sidewinder also draws on cheap raw materials that have until now been too difficult to use reliably.
“It’s a step change,” says Thomas Gorochowski, a bioengineer at the University of Bristol, in England, who was not involved in the research. “It really opens up the feasibility of synthesizing large genetic systems, maybe even small genomes.” And that, he adds, “is uber-important for all of the AI stuff that’s coming out at the moment around generative genome sequences.”
The advance, presented earlier this month at SynBioBeta 2026 in San Jose, Calif., and detailed in a preprint posted to bioRxiv, addresses one of the more vexing mismatches in modern genomics research. Generative AI tools like Evo 2, trained on the genetic code of millions of organisms, can design new DNA sequences on demand at extraordinary speed. But physically constructing long DNA sequences in a laboratory has remained slow and expensive, especially when building not just one sequence at a time but dozens of different designs simultaneously, as testing AI predictions at scale demands.
RELATED: Can Biologists Rewrite the Genome’s Spaghetti Code?
In a demonstration of how squarely Sidewinder targets this bottleneck, the team behind the technique, led by Caltech synthetic biologist Kaihang Wang, harnessed the power of Evo 2 to redesign a 12,500-letter DNA sequence of the E. coli genome in silico and then used Sidewinder to build it from scratch—with no errors. Sequences of that length can encode entire biochemical pathways, laying the groundwork for engineered microbes that manufacture drugs, biofuels, or specialty chemicals, and eventually to the assembly of vast DNA constructs approaching complete artificial genomes.
In the past, says Brian Hie, the Stanford computational biologist whose lab developed Evo 2, a project like this would likely take more than a month, based on his team’s experience with conventional commercial methods. “With a technology like this,” he says, “you could probably achieve the same thing in a few days.”
To commercialize Sidewinder, [from left] Noah Robinson, Kaihang Wang, Adrian Woolfson, and Brian Hie cofounded a company called Genyro. Marcus Ubungen
The new method builds on a DNA synthesis strategy that Wang and his colleagues first outlined at the beginning of the year in Nature, but with substantially greater capacity.
Thanks to a new algorithm that automates the most computationally demanding part of the process and laboratory innovations in how raw ingredients are managed, it is now feasible to synthesize ever larger and more numerous DNA constructs simultaneously. This opens up applications including drug discovery, data storage, and the design of synthetic organisms.
“The pace at which you can start to explore these things just opened up massively,” Gorochowski says.
To understand how Sidewinder works, it helps to understand how DNA is typically made in a laboratory. The process begins with short, chemically manufactured strands called oligonucleotides, or oligos, the molecular alphabet blocks from which longer sequences are assembled.
Ordering oligos individually is reliable but expensive. Scientists discovered years ago that they could slash costs by synthesizing thousands of different oligos together in a single pool. But doing so creates a chaotic soup in which fragments tangle with unintended partners, leading to errors.
Sorting out specific sequences from such a pool has traditionally required elaborate separation steps: physically dividing up the fragments, isolating them in tiny droplets, or fishing them out one by one with laser light. Each approach added cost, time, and specialized equipment.
The Caltech team sidestepped the problem entirely.
Sidewinder also starts with oligos, the kind anyone can buy from DNA synthesis vendors such as GenScript or Twist Bioscience, but tags each fragment with a unique molecular barcode. This short identifying sequence ensures that each piece links up only with its intended neighbor in the order that will yield the desired genetic sequence. When two bar-coded fragments meet, they form what chemists call a three-way junction: a fleeting molecular knot that locks the pieces in alignment before being cleanly removed, leaving a seamless strand.
Wang likens these barcodes to page numbers. Whereas conventional assembly is like collating an unnumbered manuscript by matching the last line of one page to the first line of the next—workable for a short document, a recipe for chaos when sequences repeat—Sidewinder’s barcodes guide each fragment to its correct partner regardless of what sequence it carries.
The original Sidewinder protocol required a computationally intensive calculation to design those barcodes, however, and this became impractically slow as the number of fragments grew.
A former Caltech undergraduate student named Jean-Sebastien Paul developed a workaround. While working in Wang’s lab one summer, Paul, who is now pursuing a Ph.D. at Stanford, built a software tool called PyWinder that churns out the barcodes in minutes on a standard laptop, replacing a calculation that had previously been too slow to scale.
Bioengineer Noah Robinson, a postdoc in Wang’s lab who codeveloped the original Sidewinder method, also adapted the approach to work from cheap, mass-produced DNA ingredients, further cutting time and cost.
Wang and Robinson, together with Hie and entrepreneur Adrian Woolfson, cofounded a company called Genyro—to commercialize the technology, hoping to turn a profit through paying pharmaceutical and biotech clients. According to Robinson, however, they intend to make the Sidewinder platform broadly accessible to the academic research community.
“We really want this to be an enabling platform,” says Robinson. “We want people to do cool things with the technology.”