• Asimov will help build out protein production and characterization capabilities at Boston University's AI-programmable cloud laboratory, one of 20 nodes in the new NSF national network.
  • Asimov will use these capabilities to generate experimental data and computational models that predict a protein's manufacturability directly from its DNA sequence.

BOSTON, MA, July 24, 2026–Asimov, the company building an AI-native synthetic biology platform to advance therapeutic development, today announced that it will build out the therapeutic protein capabilities of Boston University's (BU) node in the National Science Foundation's new Programmable Cloud Laboratory (PCL) Test Bed.

The PCL Test Bed is a core NSF contribution to the U.S. government's Genesis Mission, the national effort to harness AI for scientific discovery, and represents a $380 million investment across 20 nodes nationwide. The BU node will receive up to $20 million over four years, and will be built around the Design, Automation, Manufacturing, and Processes (DAMP) Laboratory, led by Professor Douglas Densmore, a co-founder of Asimov.

Designing biology is still largely trial and error. Unlike other industries, biotechnology has lacked standardized, broadly accessible infrastructure purpose-built to build and test designs at scale, capabilities rarely within reach for academic labs and costly even for industry, so progress depends heavily on each team's own lab and intuition. The cost of this approach is especially high in therapeutic proteins, where promising biologics are often delayed or abandoned because manufacturing liabilities surface late in development, after significant time and capital have been spent. Predicting those liabilities from a protein's sequence would reshape the economics of drug development, but doing so demands large, systematic datasets that require generation capabilities that are hard to access.

The BU node is built to bring this capability within reach across academia and industry. Asimov will stand up high-throughput protein production and characterization within the DAMP Lab node, generating experimental data at massive scale. That data will drive a lab-in-the-loop cycle: Asimov will design candidates computationally, produce and test them on the cloud lab, and feed the results back into models that predict manufacturing outcomes directly from protein sequence. As one of the network's first users, Asimov will run this loop end to end. The 20 nodes are built to interoperate, so an experiment can move between labs as it progresses, an idea the BU team calls "round tripping."

"The semiconductor industry was completely transformed when chip design decoupled from chip manufacturing. Once a company like NVIDIA could design a processor without ever building a fab, while a foundry like TSMC could manufacture designs from anyone, that separation drove decades of innovation," said Alec Nielsen, co-founder and CEO at Asimov. "Scaling biological science through cloud labs enables a similar ‘separation of concerns’. It democratizes access to the frontier, which is otherwise only reachable through vertical integration and massive investment, putting it out of reach for most."

"For academic cloud labs to gain large-scale adoption, commercial partnerships are essential to transition early-stage ideas into viable industrial applications,” said Densmore. “Asimov is building the core operational foundation to make cloud-based therapeutic manufacturing a reality."

About Asimov

Asimov's mission is to advance humanity's ability to design living systems, enabling biotechnologies with outsized societal benefit. The company is developing a synthetic biology platform, from cells to software, to design and manufacture next-generation therapeutics, including biologics, cell/gene therapies, and RNA through a combination of products, services, and collaborations.

Founded by bioengineers from MIT and Boston University and headquartered in Boston, the company has raised over $200 million from top institutional investors including Andreessen Horowitz, CPP Investments, Horizons Ventures, and Fidelity Management & Research Company. For more information, visit www.asimov.com.

Highlights

  • Virtual Private Network (VPN): Users connect to the cluster, provide some credentials and are then able to access internal tools.
  • Single Sign-On: A tool like Kerberos allows you to use the same account across various components.
  • Home-grown user accounts: You implement an authentication system and users have a separate username/password for your computing infrastructure.

Asimov, the synthetic biology company building a full-stack platform to program living cells, announced today it has been awarded a contract as part of the Defense Advanced Research Projects Agency (DARPA) Automating Scientific Knowledge Extraction (ASKE) opportunity.

Through ASKE, Asimov will work to develop a physics-based artificial intelligence (AI) design engine for biology. The goal of the initiative is to improve the reliability of programming complex cellular behaviors.

“To achieve truly predictive engineering of biology, we require dramatic advances in computer-aided design. Machine learning will be critical to bridge genome-scale experimental data with computational models that accurately capture the underlying biophysics. As genetically engineered systems grow in complexity, they become difficult for humans to design and understand. For simple genetic systems with only a couple of genes, synthetic biologists typically use high-throughput screening and basic optimization algorithms. But to engineer more complex applications in health, materials, and manufacturing, we need radically new algorithms to intelligently design the DNA and simulate cell behavior.”

Alec Nielsen, Phd, Asimov CEO
Over the past 50 years, DARPA has been a world leader in spurring innovation across the field of AI, including statistical-learning and rule-based approaches. We are proud to work with DARPA to advance the state-of-the-art in AI-assisted genetic engineering.

Asimov’s founders previously built a hybrid genetic engineering and computer-aided design platform called Cello to program logic circuit behaviors in cells. The ASKE opportunity will seek to support an ambitious expansion in the types of biological behaviors that can be engineered.

Asimov’s approach will leverage “multi-omics” cellular measurements, structured biological metadata, and novel AI architectures that combine deep learning, reinforcement learning, and mechanistic modeling. Over the past year, the company has ramped up hiring in experimental synthetic biology, machine learning, and data science to accelerate development of their genetic design platform.

Highlights

Headering 3

DARPA recently announced a multi-year investment of $2B into innovative artificial intelligence research called the AI Next campaign. A part of this wide-ranging AI strategy is DARPA’s Artificial Intelligence Exploration program, which was developed to help expeditiously move pioneering AI research from idea to exploration in fewer than 90 days. DARPA’s ASKE opportunity is part of this program and is focused on developing AI technologies that can reason over rich models of complex systems.

“Over the past 50 years, DARPA has been a world leader in spurring innovation across the field of AI, including statistical-learning and rule-based approaches. We are proud to work with DARPA to advance the state-of-the-art in AI-assisted genetic engineering.”

Alec Nielsen, PhD, Asimov CEO
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