A decade ago, when Emily was a director of research at the life-sciences giant, a pair of scientist-engineers in their 50s came to her with an idea for a company. The Bills were dubbed "biotech veterans". Banyai was a Semiconductor expert who had worked in genomics since the mid-2000s, facilitating the transition from old-school Sanger Sequencing, which processes a single DNA fragment at a time, to next-generation Sequencing, which is a mechanical engineer by training. When the chemistry was small and put on a chip, it became cheaper and more widespread to read it. The Bills realized that there was an opportunity to make the process of making synthetic genes more cost-effective by doing something similar to writing DNA.
The process of DNA synthesis was difficult at the time. Each of the 96 pits held roughly 50 microliters of the famous bases that make up DNA. In a 96-well plate, you have to put liquid in, mix, wait, apply some heat and then take the liquid out. The Bills proposed to put the same process on a chip that could hold a million tiny wells, each with a volume of 10 picoliters, 888-282-0465 888-282-0465 888-282-0465 888-282-0465 888-282-0465 888-282-0465 888-282-0465.
The wells were so small that they couldn't pipette liquids into them. Instead, they used an inkjet printer to fill them, instead of using pigmented ink. The bases were bound into a single-strand sequence of DNA using a catalyst called tetrazole. They were able to print millions instead of 96 pieces of DNA at the same time.
The concept was simple, but difficult to engineer. She explains that the success rate goes down with every base added. A's and T's bond is more weakly than G's and C's, so large numbers of A's and T's are often unstable. The longer your strand of DNA, the greater the chance of errors. The company founded by the Bills and Leproust is currently synthesizing up to 300 base pairs of DNA. They can be joined together to form genes.
The industry standard for a base pair of DNA is now nine cents, a tenfold decrease from a decade ago. If you are a customer, you can use a credit card to pay for the DNA sequence you want. After a few days, the DNA is delivered to your lab door. At that point, you can insert the synthetic DNA into the cells and get them to make the target molecule. The basis for new drugs, food flavorings, fake meat, next-gen fertilizers, industrial products, and other things eventually come from these molecules. Twist is one of a number of companies that sell synthetic genes, betting on a future filled with bio engineered products with DNA as their building blocks.
The future has arrived in a way. The two biggest products of the past year were the mRNA vaccines from Pfizer and Moderna. The Chinese C.D.C. released the genomic sequence of the disease in January 2020 and the two pharmaceutical companies were able to synthesise the spike protein from it. This meant that their vaccines could deliver genetic instructions to the immune system to recognize a virus, so that it will be attacked, unlike traditional analogues, which teach the immune system to recognize a virus by introducing a weakened version.
This would have been difficult a decade ago. It would have been difficult for researchers to make a long enough sequence to make a full spike. Technical advances in the last few years allowed the vaccine developers to make longer pieces of DNA and RNA at a lower cost. Within weeks we had vaccine prototypes and shots in arms.
Gene synthesis will be used to tackle a variety of other problems in the future. If the first phase of the revolution focused on reading genes, the second phase is about writing genes. The rise of gene synthesis promises to be an equally powerful development as Crispr, the technology whose inventors won a Nobel Prize last year, has received far more attention. Crispr is like editing an article, allowing us to make precise changes to the text at specific spots; gene synthesis is like writing the article from scratch.
Gene synthesis has sparked a lot of speculation and start-up activity, like many technologies in their infancy. Most of the companies are in experimental phases, with their applications yet to return conclusive results. Even though the possibilities are still intriguing, investors and scientists still prefer to look at them in a different way. Trillion-dollar markets are possible if a small percentage of these efforts succeed. We are in the Apple II days of synthetic biology, with the equivalent of iMacs and iPhones still to come, according to an analogy frequently used by biotech venture capitalists. It is a grandiose claim, but not implausible, because Covid has tested some of the underlying technologies. Our digital lives were created by personal computing, while reading and writing DNA could mean control over our physical ones.
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Credit by Jaedoo Lee.
Nature is the best innovator according to synthetic biology. CaS-9 was a defense thatbacteria evolved to fight off viruses. Nature has been opaque for most of human history, requiring that humanity stumbles upon its inventions by chance. Many of our medicine-cabinet staple have been discovered from leaving food out for too long or finding the active ingredients in herbal remedies. Since the advent of modern chemistry, we have been able to write down the formulas that are common in physics and math.
The genomics revolution came after that. The emergence of companies like 23andMe focused on reading genes in the first phase. The second phase is about writing genes. Crispr and DNA synthesis can be used to create genetic recipes that produce the outputs we want. What does this look like in practice?
Ginkgo Bioworks, a cell-engineering company that went public to much fanfare in September and was valued at $25 billion, is one of Twist's biggest clients. The main offices of Ginkgo are in Boston. Patrick Boyle, a Ginkgo executive, walked me through their five "foundries", which are named after chip fabrication plants. We passed two machines that use different technologies to quickly analyze the chemical composition of liquids.
The labor unit of biological research has been the graduate student, who toils away pipetting liquids, taking measurements, and looking through results. Ginkgo has brought an assembly line's efficiency to the lab, using machines that can pipette, mix and assays with far more precision than any human could, making it possible to run thousands of different experiments at the same time.
Ginkgo is a platform company, instead of producing end products for itself, it engineers cells for its clients. A client calls up Ginkgo and says they want to make a rose scent for their perfumes that is cheaper than making it from flowers. Ginkgo's designers comb through a library of genes and pick out those that are known from previous observation or Sequencing to produce the characteristics of rose oil. After the sequence is laid out on a computer, Ginkgo orders the DNA from Twist or other providers who do most of the base pairs.
At Ginkgo, the synthesized DNA is inserted into a host cell, which in turn starts producing something. The trial and error are followed. Maybe the outputs from the first and second genes are not spicy enough, or maybe the cells don't produce enough of it. Once an effective prototype is found, Ginkgo increases its production by growing the yeast in large vats and streamlining the process for getting the desired molecule from the soup. The winning genetic code, the host cell and the conditions in which the cells have to be nurtured are what Ginkgo delivers.
Ginkgo first attracted customers in the fragrance industry, but in the last two years it has been partnering with pharmaceutical companies to search for new drugs. One project is trying to find the next generation of antibiotics in order to counter antibiotic resistance. Lucy Foulston is leading the effort and Tom Keating is working with her. Most antibiotics and antibiotic resistance come frombacteria themselves. The antibiotics that kill otherbacteria carry genetic snippets. They have a capacity for self-resistance, so that if a particular antibiotic is used, it will not kill itself, but it can be transferred to otherbacteria so that it becomes widespread.
Two paths have been taken to come up with new antibiotics. In Alexander Fleming's stories, the first thing scientists do when they get to the natural world is to seek out the soil from a geyser or coral reef and see if it kills any interestingbacteria. The second approach is to comb through the libraries for the molecule that has the most antibacterial activity. The two approaches gave us a steady supply of new antibiotics until the 1980s and ’90s, when discoveries began to dry up.
Keating says there was a lot of speculation. Did we find all the useful ones? Did we find everything easy to find? Did we run intobacteria that are difficult to kill that the new ones we find don't really work on them? We have been running out of antibiotics in the face of growing resistance.
The antibiotics project at Ginkgo is looking for segments that can be used to generate novel antimicrobials. Large databases of the genomes ofbacteria have given scientists a better understanding of which genes produce which molecule. Foulston says that scientists have developed techniques to take genes out, put them in another strain, and then get that particular strain to produce a molecule of interest.
Keating says that we don't need the organisms anymore. We don't need it to grow on a plate. We don't need it to be doing anything else. The code is all we need.
No matter how many programming metaphors you use, DNA is messier than code. You expect the computer to return "hello world" if you type "print". If you put a DNA sequence in a cell, you might be able to predict what will come out of it, but you never really know.
There is a new moment in which software, hardware, data science and lab science are all mature enough to work together and reinforce one another. Machine learning and computer modeling will only increase the possibilities. As small as one of Twist's 10-picoliter wells might seem, Leproust points out that from the perspective of the 21st-century Semiconductor industry, it's "a Grand Canyon, almost like being in the Stone Age." The company is experimenting with smaller chips with diameters of 150 nanometers. Intel is now making seven-nanometer chips for computers. It is a progression that promises to lower the cost of gene synthesis a millionfold and make it accessible to ever more researchers and useful in ever more experiments and applications.
The next frontier for synthetic biology is to go where nature has not gone. Can we combine genes to create even more fragrant flowers? Is it possible to turn DNA into circuits that allow cells to act like computers? Keating says that they are copying what nature has already invented. He wants to be like the chemists who can synthesise whatever can be diagrammed. He says that they are just scratching the surface of the question, can we program biology to do what chemists have traditionally done? If you can draw a molecule on a piece of paper, can we engineer an organisms to produce that molecule, even if it is something that nature has never seen before? We are nowhere near that, but baby steps.
Yiren Lu is based in New York. She wrote about start-ups trying to fix virtual meetings.
