Synthetic biology industry: data-driven design is creating new opportunities in biotechnology

Abstract

Synthetic biology is a rapidly emerging interdisciplinary research field that is primarily built upon foundational advances in molecular biology combined with engineering design. The field considers living systems as programmable at the genetic level and has been defined by the development of new platform technologies. This has spurned a rapid growth in start-up companies and the new synthetic biology industry is growing rapidly, with start-up companies receiving ∼$6.1B investment since 2015 and a global synthetic biology market value estimated to be $14B by 2026. Many of the new start-ups can be grouped within a multi-layer 'technology stack'. The 'stack' comprises a number of technology layers which together can be applied to a diversity of new biotechnology applications like consumer biotechnology products and living therapies. The 'stack' also enables new commercial opportunities and value chains similar to the software design and manufacturing revolution of the 20th century. However, the synthetic biology industry is at a crucial point, as it now requires recognisable commercial successes in order for the industry to expand and scale, in terms of investment and companies. However, such expansion may directly challenge the ethos of synthetic biology, in terms of open technology sharing and democratisation, which could unintentionally lead to multi-national corporations and technology monopolies similar to the existing biotechnology/biopharma industry.

Keywords: biotechnology; industry; synthetic biology.

Figure 1.. Typical design–build–test–learn cycle diagram from [4].

The cycle begins with Design (D), which involves the computational design of genetic parts, circuits, regulatory and metabolic pathways to whole-genomes; Build (B) involves the physical assembly of those designed genetic components; Test (T) involves the prototyping and testing of the assembled genetic designs; Learn (L) is the application of modelling and computational learning tools, which uses the data obtained in T to inform the design process. Iterations of the DBTL cycle results in genetic designs that aim to fulfil the design specifications established in D. Reproduced from [4] with permission from Dr Chris Petzold.

Figure 2.. The synthetic biology technology stack.

Reproduced from [21] with permission from Will Canine CEO Opentrons. The stack is divided into four layers with the application layer being enabled by the BioCAD design layer that utilises the execution layer to deliver the design/application using biological reagents from the fourth layer. The technology stack is based in part around the DBTL cycle shown in Figure 1.