Biologics accounted for more new drug approvals than did small molecules for the first time in 2022, marking a significant shift in the pharmaceutical industry (1). Large molecule pipelines are also moving from standard monoclonal antibodies (MAbs) to more complex and difficult-to-express molecules, which intensifies pressure on the industry to meet biomanufacturing demands. There is a pressing need for innovative Chinese hamster ovary (CHO)–based bioproduction systems to keep pace with this evolving landscape.

While multiple areas of cell-line development (CLD) have improved over the years, advances in expression vector design have lagged behind other technologies. Most expression vectors still rely on a “one-size-fits-all” approach across molecules in which the coding sequences (CDSs) of different therapeutic proteins are inserted into fixed plasmids made up of legacy genetic parts. That method often results in suboptimal expression, increased manufacturing costs, and delays in clinical development for both MAbs and other, more complex modalities.

Asimov’s CHO Edge system builds on the current state of the art for CLD by integrating expanded genetic tools with data-driven models. The system includes a glutamine synthetase (GS) knock-out CHO host — and a Fut8/GS double knock-out also is available for afucosylated antibody production — with a proprietary hyperactive transposase for genomic integration, a library of more than 2,500 characterized genetic elements, and Kernel computer-aided design software for vector design and simulation.

The integrated system routinely achieves titers of 5–10 g/L across modalities in a four-month CLD timeline (Figure 1). The entire system can be licensed and also is offered as a CLD service with a disruptive commercial structure. The cost of a campaign is linked directly to research cell bank (RCB) performance: If a generated RCB expresses a MAb at <4 g/L, then the CLD campaign and all commercial-use rights will be free of charge.


  • 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.


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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