Optimizing the Design-Build-Test-Learn (DBTL) cycle to scale engineering biology

The recent boom in biological engineering technologies alongside growing capabilities in automation, artificial intelligence, and machine learning have expanded the possibilities for engineering biology. This innovation is essential to enable scientists to create novel, sustainable solutions to evolving issues that affect human health, agriculture and the environment. It is also predicted to significantly change the global economic landscape as part of a shift towards sustainable solutions and personalized healthcare.

Engineering biology builds upon an iterative Design-Build-Test-Learn cycle to achieve desired functions for novel biological systems. However, limitations in the Build phase of this cycle hinder developments made elsewhere. In particular, DNA synthesis methods are currently unable to meet the rising demand for high-quality, gene-length sequences.

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Analysis of scientifically and commercially viable engineering biology applications has revealed the potential for $2-4 trillion in global economic impact annually over the next 10−20 years.³, It is further estimated that up to 60% of physical products in the global economy, including wood, meat, plastics, and fuels, could theoretically be produced through biological innovations.³ In addition, engineering biology is already addressing needs in healthcare, from mRNA vaccine technology to monoclonal antibody therapy and personalized healthcare. The standardization and scale-up of innovative tools and technologies, however, is critical to achieving maximum benefit in each economic sector.⁴

The workflow for engineering biology follows an iterative Design-Build-Test-Learn (DBTL) methodology. This approach applies systems data analysis and mathematical modeling to inform the design of the next cycle and address a particular biological application.² Given the intricate and dynamic environment of a cell or organism, iterative design of biological components is crucial for characterizing and regulating host responses.

While this systematic approach is necessary due to the complexity of biological systems, the overall cycle is slow and laborious. This can result in extended development and production timelines driven by delays in the Build phase of the cycle owing to limitations in gene synthesis technology. As researchers seek to generate novel biological systems with unique DNA sequences, the need for engineered DNA will continue to grow.

A shift away from centralized service providers will allow laboratories to gain more control over DNA synthesis, maintain confidentiality of proprietary sequences, and judge project timelines more accurately. The development of benchtop DNA printers represents a new breakthrough in DNA synthesis technologies and a growing need for this process to become more affordable, flexible, and scalable.