What does the synthetic biology startup landscape look like today?
In our synthetic biology market deck, you will find everything you need to understand the market
SUMMARY
The synthetic biology startup landscape today is entering a second cycle: AI-driven biological design, DNA infrastructure and high-value biomanufacturing are attracting capital, while factory-heavy commodity models remain much harder to finance.
The category itself is getting broader even as the “synthetic biology” label becomes less useful. Companies now describe themselves through generative biology, programmable biology, precision fermentation, AI protein design and engineering biology, but the common thread is still deliberate creation or reprogramming of biology.
The post-2021 crash did not prove that biological engineering was a dead end. It showed that investors had overvalued platform optionality and underestimated how much each product still needed its own customers, regulation, purification process, manufacturing plan and economics.
Funding is recovering selectively rather than broadly. Large rounds are still available, but they are clustering around companies such as Chai Discovery and Profluent, where AI, therapeutics, proprietary data or major commercial partnerships shorten the distance between technical capability and economic value.
AI is also changing where the bottleneck sits. Generative models can propose far more plausible proteins and genetic systems, so the scarce part increasingly becomes experimental validation, assay quality, wet-lab throughput and the physical DNA needed to test those designs.
That shift makes biological infrastructure unusually attractive. Twist Bioscience’s record revenue and Ansa Biotechnologies’ push into long, complex DNA suggest that picks-and-shovels businesses can benefit from almost every successful downstream biology model without betting the company on one molecule or one factory.
Pharma still captures the most synthetic-biology value because the economics tolerate expensive R&D. A successful therapeutic or pharmaceutical ingredient can support far more value per kilogram than a food protein, fuel or commodity chemical, even when the underlying engineering challenge is similar.
Industrial synthetic biology is becoming more believable when startups avoid owning every manufacturing asset themselves. Antheia and EVERY show a more disciplined model: engineer the biology, secure customers, then use experienced fermentation partners for large-scale production.
Precision-fermented food remains a difficult startup category. Funding fell sharply in 2025, and several companies ran into insolvency or restructuring, but the survivors increasingly look different: they sell functional ingredients, secure demand earlier and add capacity through partners rather than building speculative factories.
The geographic race is also asymmetric. The U.S. leads in startup formation, AI-biology funding and pharmaceutical networks; China has a formidable fermentation manufacturing base; and Europe and the UK are using public capital to close a scale-up financing gap. The strongest startups in this second cycle tend to own something scarce after biological design gets easier: proprietary data, experimental throughput, DNA infrastructure, regulatory progress, customers, valuable products or manufacturing access.
This market map, featured in our synthetic biology market deck, highlights top companies and startups in the synthetic biology market
What actually counts as a synthetic biology startup today?
The synthetic biology startup landscape today is much broader than companies that literally describe themselves as “synthetic biology,” so the useful definition is whether engineering biology sits at the core of what the company builds or sells.
The label has become surprisingly slippery. The OECD points out that there is still no internationally agreed definition of synthetic biology, while governments increasingly prefer “engineering biology.” Meanwhile, founders use terms such as generative biology, programmable biology, precision fermentation, AI protein design and advanced biosynthesis.
We would include companies that deliberately design biological molecules, cells, genetic systems or biological production processes. That covers relatively obvious names such as Ginkgo Bioworks and Antheia, but also Chai Discovery, Profluent and Ansa Biotechnologies. Chai designs new molecular structures computationally. Profluent generates proteins and gene-editing systems. Ansa physically manufactures custom DNA. They occupy different parts of the same design-build-test cycle.
We would draw the line before conventional biotech companies that simply use AI somewhere in drug discovery. Using machine learning to rank drug targets doesn't automatically make a company synthetic biology. The technology needs to help create or reprogram biology itself.
This boundary matters more now because some of the fastest-growing companies in the field barely use the synthetic-biology label. The startup landscape is expanding even as the label itself becomes less useful.
Why does synthetic biology look battered and hot at the same time?
Synthetic biology currently contains two very different markets: the capital-heavy startup model that was punished after 2021, and a newer AI-biology and infrastructure market that investors are funding aggressively.
Anyone looking mainly at the old public companies would reasonably think the sector went through a crash. Amyris entered Chapter 11. Zymergen, once valued at several billion dollars, ended up being acquired by Ginkgo. Ginkgo itself has been radically restructured after years of heavy spending and disappointing revenue growth. Several alternative-protein and industrial-biotech companies have also closed, sold assets or scaled back factories.
Then look at what investors are financing now. Chai Discovery recently raised $400 million at a $3.8 billion valuation. Profluent raised $106 million and later signed an Eli Lilly collaboration worth up to $2.25 billion if all milestones are reached. Generate Biomedicines raised $400 million in one of the largest recent biotech IPOs. DNA supplier Twist Bioscience just reported record quarterly revenue.
The contradiction is mostly about business models. Investors have become much colder toward companies that need years of strain engineering, large factories and uncertain commodity economics. They remain willing to spend heavily when engineered biology feeds into drugs, proprietary biological data, DNA infrastructure or AI models where each successful output can be extremely valuable.
These days, “synthetic biology is booming” and “synthetic biology crashed” can both sound correct depending on which part of the market someone is looking at.
As this chart shows, and as featured in our synthetic biology market deck, search interest in gene editing has grown significantly
What actually went wrong with the first synthetic biology startup boom?
The first synthetic biology startup boom mainly overestimated how quickly better biological engineering would turn into good businesses.
During the 2020-2021 cycle, investors gave a lot of value to platform optionality. A company that could engineer microorganisms might eventually make chemicals, food ingredients, pharmaceuticals, materials, fragrances and agricultural products. On a pitch deck, every additional market made the platform look more valuable.
Commercial reality worked differently. Each new product brought its own biology, purification process, factory requirements, regulation, customer base and unit economics. The tenth application wasn't simply another copy of the first one.
Amyris showed how painful that could become. The company became technically capable of producing multiple molecules through engineered organisms, then spread itself across fermentation, ingredients and consumer brands while continuing to consume large amounts of cash. The science produced real products; the corporate structure around those products eventually became unsustainable.
Zymergen exposed another weakness. A sophisticated automated biology platform still needed products that customers wanted badly enough, soon enough, to support the enormous infrastructure behind it.
Ginkgo is the clearest current stress test. Its latest quarter produced $20 million of revenue, down 48% year over year, alongside a $57 million GAAP net loss from continuing operations. The company is now pushing much harder into autonomous laboratories and scientific infrastructure after years of restructuring its original horizontal foundry model.
The lesson from that first wave is fairly brutal. Being able to program biology creates technological options; customers pay for specific outcomes. Investors now give far less credit to the gap between those two things.
If you want more recent data on this point, please see our latest synthetic biology market report.
Is synthetic biology funding actually coming back?
Synthetic biology funding has recovered from the worst of the downturn, but there is still no evidence of another broad 2021-style funding boom.
The latest full-sector SynBioBeta dataset publicly available shows investment rising from $10.7 billion in 2023 to $12.2 billion in 2024, roughly 14% growth. That was a meaningful rebound after the post-2021 contraction, although funding remained below the peak.
We should be careful with the word “recovery,” because the venture market around biology has changed dramatically since then. Crunchbase found that biotech attracted just over 8% of U.S. startup investment in 2025, the lowest share in more than two decades of its data. Biotech had commonly captured more than 15% in earlier years.
AI is absorbing much of that difference. The OECD calculated that AI companies captured 61% of global venture capital in 2025, or $258.7 billion out of $427.1 billion. A synthetic biology company asking for $100 million today is therefore competing against some of the most aggressively financed technology companies ever created.
That environment explains the strange funding market we see now. Large biology rounds still happen, sometimes extremely large ones, but investors are clustering around a smaller group of companies where AI, therapeutics, proprietary data or commercial traction gives them a reason to move quickly.
| Funding indicator | Latest useful evidence | What we take from it |
|---|---|---|
| Broad synthetic biology funding | $10.7B in 2023 → $12.2B in 2024 | The post-boom decline stopped |
| U.S. biotech share of startup funding | Just over 8% in 2025 | Biology is losing relative share of venture capital |
| Global AI share of VC | 61% in 2025 | Biology now competes with an extraordinary AI capital cycle |
| Current funding pattern | Large rounds concentrated in selected companies | The recovery is highly selective |
This chart, featured in our synthetic biology market deck, illustrates yearly VC funding for synthetic biology startups
Where is the new synthetic biology money going now?
The hottest synthetic biology money today is flowing toward AI-driven biological design, therapeutics and tools that make biological R&D faster.
Chai Discovery is the most extreme recent example. The company raised $70 million in a Series A, $130 million in a Series B only a few months later, and then $400 million in a Series C at a $3.8 billion valuation. Including its seed financing, Chai has now raised roughly $630 million. Its customers and partners include Eli Lilly, Pfizer, Novartis and argenx.
That financing pace goes beyond “AI biology is fashionable.” Chai's valuation moved from roughly $550 million at Series A to $3.8 billion at Series C in less than a year, almost a sevenfold increase. Investors are pricing the possibility that molecular design becomes one of the major commercial applications of frontier AI.
Profluent shows the same appetite from another angle. After raising $106 million, bringing total disclosed funding to $150 million, the company signed a deal with Eli Lilly to develop AI-designed recombinases for genetic medicines. The agreement carries potential payments of up to $2.25 billion, although most of that amount depends on future milestones rather than money Profluent receives today.
There is also a quieter data layer forming underneath these companies. Basecamp Research has built a proprietary biological dataset containing billions of protein sequences collected through biodiversity partnerships around the world. That kind of proprietary experimental or natural-world data becomes more valuable as model architectures themselves become easier to reproduce.
The money is moving toward the places where biology still has scarcity: unusual data, experimentally validated molecules, biological infrastructure and valuable downstream applications.
| Company | Recent evidence | What investors are betting on |
|---|---|---|
| Chai Discovery | $400M Series C at $3.8B | AI-designed molecules and antibodies |
| Profluent | $106M financing; Lilly deal worth up to $2.25B | AI-designed proteins and gene-editing systems |
| Basecamp Research | Proprietary database with billions of protein sequences | Scarce biological training data |
| Ansa Biotechnologies | $54.4M Series B | Faster access to long, complex synthetic DNA |
If you want more recent data on this point, please see our latest synthetic biology market report.
Has AI really changed synthetic biology yet?
AI has already changed how synthetic biology designs proteins and genetic systems, although the biggest commercial bottlenecks now sit increasingly on the experimental side.
Traditional protein engineering often involved starting with an existing protein, creating variants, testing them and iterating. Generative models can now propose entirely new sequences based on a desired function. That lets researchers search vastly larger design spaces before deciding which candidates deserve physical experiments.
Chai reported double-digit experimental hit rates for some de novo antibody-design tasks with Chai-2. We should treat company-reported benchmark results carefully, but a double-digit hit rate is commercially interesting because the point isn't to make the computer right every time. The point is to make each expensive laboratory round much more productive.
Profluent has pushed the idea into gene editing. Its researchers produced OpenCRISPR-1, an AI-generated CRISPR system that was subsequently described in Nature, and the company has since expanded into partnerships covering medicine and agriculture.
The bottleneck is consequently moving from “can we imagine a plausible sequence?” toward “can we prove that this sequence behaves correctly in the real world?” That makes wet-lab throughput, assay quality and experimental data more important.
Twist's latest results give us a useful commercial clue. Management has said AI-driven drug discovery is contributing to unusually strong growth in its therapeutics business. In the latest quarter, the company shipped about 369,000 genes, 56% more than a year earlier. More computational design can therefore create more physical experimentation rather than replacing it.
That is where AI is changing synthetic biology most visibly right now: computers generate more credible biological possibilities, while laboratories have to determine which ones deserve to exist outside the computer.
This chart, featured in our synthetic biology market deck, shows how Twist Bioscience is capturing share in synthetic biology
Are DNA synthesis and lab tools becoming some of the best synthetic biology businesses?
Synthetic biology tools currently have some of the strongest business economics in the sector because they get paid every time customers run another design-build-test cycle.
Twist Bioscience is the clearest public example. Its latest quarterly revenue reached a record $118.4 million, up more than 23% year over year, marking its fourteenth consecutive quarter of sequential revenue growth. DNA Synthesis and Protein Solutions revenue grew even faster, up 39% to $56.6 million.
The operational numbers are equally interesting. Twist shipped roughly 369,000 genes during the quarter compared with about 237,000 a year earlier. Gross margin reached 52.8%, and the company raised full-year revenue guidance to $456-457 million while maintaining its target of adjusted EBITDA breakeven in the following quarter.
Ansa Biotechnologies is attacking a different limitation in the same market. The company has commercialized clonal DNA sequences as long as 50 kilobases, with turnaround times under 25 business days, after raising a $54.4 million Series B to expand U.S. DNA manufacturing.
These businesses benefit from a simple industry dynamic. If AI companies generate more protein designs, drug companies run more experiments and industrial-biotech teams test more organisms, somebody has to make the DNA and other physical inputs.
We currently prefer that economic position to a startup whose entire value depends on one future fermentation plant working perfectly. Selling picks and shovels is an old analogy, but synthetic biology finally has enough real digging for it to fit.
If you want more recent data on this point, please see our latest synthetic biology market report.
Can industrial synthetic biology actually work at commercial scale now?
Industrial synthetic biology can work at commercial scale today, especially for high-value products, but companies are learning to scale through existing manufacturing infrastructure instead of automatically building giant factories themselves.
Antheia gives us one of the strongest examples. The company has already delivered a full commercial order of biosynthetic thebaine, a starting material used in important medicines, and its current pipeline lists thebaine at commercial stage. Antheia says its production has reached 116,000-liter scale.
The company's financing strategy is equally revealing. It completed its Series C after adding another $24 million, and says it secured more than $175 million of capital and non-dilutive funding over the previous year. It has also expanded U.S. government project agreements and partnered with TAPI for commercial-scale fermentation and API manufacturing.
Food startup EVERY is taking a similar asset-light approach. After demand for its OvoPro egg protein accelerated, EVERY quadrupled manufacturing capacity through Huvepharma. It then added ADM's large fermentation facility in Iowa as another commercial-scale production site.
These companies are effectively separating the biological invention from the most expensive manufacturing assets. Existing fermentation operators already know how to run tanks, manage contamination, source feedstock and maintain large industrial facilities. A startup can concentrate more capital on the organism, product, customers and process.
That model won't solve every scale-up problem. Fermentation still changes as tank size increases, downstream purification can dominate cost, and one badly designed process can remain uneconomic at any scale. But the industry is getting smarter about who should own those risks.
The old ambition was often “build the molecule and build the factory.” These days, we increasingly see startups bringing the biology while established manufacturers bring the tanks.
This chart, featured in our synthetic biology market deck, illustrates yearly funding for synthetic biology startups
Is precision-fermented food still a bad place to build a startup?
Precision-fermented food remains one of the hardest synthetic biology startup categories, although a few companies are finally showing what a workable version of the business can look like.
The funding picture is still weak. The Good Food Institute calculated that fermentation-focused alternative-protein companies raised $357 million in 2025, down from $632 million the year before. That's a decline of roughly 44%.
The sector also spent several years learning expensive lessons from companies that scaled ahead of demand or struggled to finance the next stage. Motif FoodWorks shut down. Arkeon entered insolvency. NovoNutrients went through an assignment process. Meati, which uses biomass fermentation, also ran into severe financial trouble after raising large amounts of capital and investing heavily in production.
Yet the latest commercial evidence is better than the funding numbers alone suggest. EVERY raised $55 million to support commercialization, then reported that annual orders secured during the first four months of 2026 were equivalent to 550% of its total 2025 order volume. Products using its OvoPro ingredient are now sold through Walmart and Target, and its capacity expansion involves both Huvepharma and ADM.
Vivici offers another version of the model. Backed by Fonterra and dsm-firmenich, it raised €32.5 million after already securing initial customer offtake agreements for its precision-fermented dairy proteins.
The survivors increasingly sell functional ingredients to food manufacturers, line up customers before adding capacity and lean on industrial partners for production. That looks much healthier than raising hundreds of millions to build a factory around the assumption that consumers will eventually arrive.
| Fermentation evidence | What happened | What it says about the market |
|---|---|---|
| Sector funding | $632M → $357M in one year | Investors remain cautious |
| EVERY | Orders surged; capacity expanded with Huvepharma and ADM | Commercial demand can unlock scale |
| Vivici | €32.5M raise after initial offtake agreements | Customers are increasingly expected before major expansion |
| Multiple distressed companies | Closures, insolvencies and restructurings | Manufacturing capacity alone doesn't create demand |
If you want more recent data on this point, please see our latest synthetic biology market report.
Why does pharma capture so much more synthetic biology value than food or chemicals?
Pharma currently gives synthetic biology startups the easiest place to justify expensive R&D because one successful molecule can be worth vastly more than a tonne of food protein or commodity chemicals.
The difference starts with value density. A pharmaceutical company may willingly spend millions developing a molecule that eventually gets manufactured in kilograms. A food company can sell millions of kilograms and still lose money if its ingredient costs slightly too much.
Generate Biomedicines shows how far investors will go when generative biology is attached to clinical assets. The company raised $400 million in its IPO at $16 per share, valuing it at roughly $2 billion at pricing. Its lead antibody program has already advanced into Phase 3 for severe asthma.
The financial burden is still huge. Generate's first quarter as a public company produced $7.2 million of collaboration revenue while R&D spending reached $57.8 million and operating cash use reached $80.4 million. AI clearly doesn't make clinical development cheap.
The potential payoff explains why investors tolerate that burn. One successful drug can support billions of dollars of lifetime sales. A new commodity ingredient usually needs excellent manufacturing economics from the beginning.
Antheia occupies an interesting middle ground. It uses synthetic biology for pharmaceutical manufacturing rather than discovering new drugs, so it can sell high-value ingredients while avoiding the full cost of taking a therapy through clinical trials.
The same engineering breakthrough can be commercially attractive in medicine and hopeless in bulk manufacturing simply because the end markets put completely different values on each kilogram produced.
This chart, featured in our synthetic biology market deck, compares the main business model options for synthetic biology platforms
Is government money becoming part of the synthetic biology startup model?
Government support now plays a meaningful role in synthetic biology scale-up, particularly when biomanufacturing overlaps with drug security, defense, domestic production or strategic competition with China.
The U.S. National Security Commission on Emerging Biotechnology made the shift explicit in its 49 recommendations to Congress. One of its main priorities is helping private companies reach industrial scale rather than concentrating public support only on basic research.
Actual money is starting to follow that logic. Antheia's expanded BioMaP agreement brought the total federal investment in that project to $21 million for domestic production of critical pharmaceutical ingredients. Separately, the U.S. is putting hundreds of millions of dollars into AI-enabled cloud laboratory infrastructure.
The UK has gone further in making engineering biology an explicit industrial strategy. The government has committed £644 million, including £184 million for scale-up infrastructure and £196 million for a national engineering biology R&D program.
Europe is responding to its own financing gap. The European Commission and European Investment Bank launched BioTechEU with the aim of mobilizing up to €10 billion for biotechnology in 2026-27. The European Commission also cites an estimated €40 billion annual investment gap in health biotechnology.
Public money can't turn a bad product into a good business. It can, however, change the financing equation for shared laboratories, first commercial plants and strategically important products where private investors would otherwise carry all the early infrastructure risk.
For industrial synthetic biology, that distinction is becoming increasingly important.
Is the U.S. still winning synthetic biology, or is China catching up?
The U.S. currently has the strongest synthetic biology startup ecosystem, while China has built a manufacturing position that is much harder to dismiss than its venture funding numbers suggest.
The U.S. advantage is especially visible in the current AI-biology wave. The Bay Area and Boston concentrate many of the best-funded companies, experienced biotech investors, pharmaceutical buyers, universities and AI talent. Chai, Profluent, Generate and a large part of the synthetic-biology tooling ecosystem sit inside that network.
Europe remains scientifically strong but has a financing problem. The European Commission's estimate of a €40 billion annual health-biotech investment gap explains why European policymakers are now explicitly trying to keep companies from moving their later-stage growth elsewhere. The UK remains one of the strongest ecosystems outside the U.S. and is putting new money into scale-up infrastructure.
China looks different again. According to the U.S.-China Economic and Security Review Commission, China accounted for nearly 60% of highly cited synthetic-biology academic papers between 2019 and 2023. More importantly for industrial biology, the Commission estimates that China produces more than 30 million tonnes of fermentation products annually, representing around 70% of global fermentation output.
That manufacturing base could become a huge advantage if biological design gets easier. Better AI models and cheaper gene engineering can spread internationally; thousands of industrial fermentation assets, supply chains and trained operators take much longer to reproduce.
Chinese private synthetic-biology investment has actually been weak recently, according to the same U.S. Commission, falling sharply from its 2022 peak. So the current race has an unusual shape: the U.S. leads startup formation and private capital, China has extraordinary manufacturing depth, and Europe is trying to prevent its strong science from disappearing during scale-up.
This chart, featured in our synthetic biology market deck, illustrates the share of revenue generated by each customer segment in the synthetic biology market
So what does the synthetic biology startup landscape actually look like today?
The synthetic biology startup landscape today looks healthier than the post-2021 collapse suggests, but the winners are emerging in very different places from the ones investors originally expected.
The first major group is AI-driven biological design. Chai, Profluent and similar companies are making proteins, antibodies and genetic machinery increasingly designable on computers. These companies currently attract some of the largest valuations because they combine frontier AI economics with pharmaceutical upside.
The second group sells the infrastructure needed to turn those designs into physical experiments. Twist's latest growth and Ansa's push into longer synthetic DNA show that biological tools can build substantial businesses without betting everything on one downstream product.
A third group is finally proving industrial biology in narrower markets. Pharmaceutical ingredients, specialty products and functional food ingredients make more sense when customers value performance, supply reliability or scarcity enough to support fermentation economics. Partnerships with established manufacturers are also reducing the amount of factory risk startups have to carry themselves.
The difficult end of the market remains large-volume, low-value production. Food proteins have made progress, yet recent funding and company failures show how hard the economics remain. Commodity chemicals, fuels and materials face an even tougher version of the same problem whenever conventional alternatives are cheap.
We also see the meaning of “platform” changing. A few years ago, a startup could argue that one biological engineering platform would eventually serve dozens of industries. Investors now want a much shorter bridge between the platform and somebody paying for it.
Our read is straightforward. Synthetic biology has moved into a second startup cycle. The technology is getting better quickly, capital is returning selectively, AI has opened an important new design layer, and commercial products are reaching real customers. At the same time, investors have stopped treating biological programmability itself as enough of a business model.
The strongest synthetic biology startups now tend to own something scarce after biological design becomes easier: proprietary data, experimental throughput, DNA infrastructure, regulatory progress, high-value products, customers or manufacturing access.
That makes today's synthetic biology landscape less spectacular than the original “biology will become software” story. It also makes the companies being built now considerably more believable.
If you want more recent data on this point, please see our latest synthetic biology market report.
OUR METHODOLOGY
This analysis looks at what the synthetic biology startup landscape actually looks like today by separating the field into the areas that reveal the clearest structural differences: capital, business models, AI-driven biological design, biological infrastructure, industrial scale-up, end-market economics and geographic competition.
We use a practical definition of synthetic biology rather than relying on company labels. A company belongs in the landscape when engineering biology is central to what it builds or sells: designing biological molecules, cells, genetic systems or biological production processes. Conventional biotech companies that simply use AI to rank targets are outside the core group unless the technology is being used to create or reprogram biology itself.
We prioritized the freshest evidence that could distinguish a durable shift from an isolated headline. Funding rounds show where investors are willing to place capital; revenue, orders and commercial agreements show where customers are paying; production milestones and manufacturing partnerships show whether biology is moving beyond the laboratory; experimental results help separate computational promise from demonstrated biological performance; and government programs show where biological capacity has become strategically important.
We did not treat every large round, failure or technical milestone as representative of the whole market. The broader conclusions come from convergence across several types of evidence. A trend became more convincing when financing, customer activity, operating performance, scientific progress and manufacturing evidence pointed in the same direction.
Freshness matters especially here because the sector has changed substantially since the 2020-2021 investment cycle. We therefore gave more weight to current operating results, recent financings, production milestones and present government programs than to older valuations or the platform narratives that shaped the first synthetic-biology boom.
Key sources include the OECD's Synthetic Biology in Focus for the definition problem and policy framing, the OECD's 2025 AI venture-capital analysis for the wider funding context, Ginkgo Bioworks' Q2 2026 SEC filing for current operating evidence, Nature's paper on AI-designed genome editors and Profluent's Eli Lilly collaboration for the AI-biology layer, Ansa Biotechnologies for long-DNA synthesis, and the Good Food Institute's 2026 fermentation report for the precision-fermentation funding picture.
For government and geographic context, we also used the U.S. National Security Commission on Emerging Biotechnology, UK government engineering-biology material, and the U.S.-China Economic and Security Review Commission for China's research and fermentation-manufacturing position. Generate Biomedicines' 2026 company overview was used to assess how generative biology is being commercialized in therapeutics.
This chart, featured in our synthetic biology market deck, shows how gene therapy technology has evolved over time
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