Guest blog | Synthace | Engineering Science with Computer Aided Biology

Computer Aided Biology (or CAB) is a conceptual framework and an emerging ecosystem of digitally powered research tools that augment human capabilities in biological research. These tools encompass both the digital (e.g. machine learning algorithms) and the physical (lab automation) which together empower biologists to address increasingly complex biological challenges.

By adopting CAB, researchers can be empowered to explore dynamic biological space in a non-reductionist, and iterative manner; moving biotechnology gradually towards an ‘Engineering’ approach to experimentation. Engineering Biology combines engineering, biology and programming to design new processes and products, and is a thematic area that the UK has leadership in as exemplified by companies such as Prokarium, Synpromics and Evonetix (amongst many others). It is also an area the BIA actively supports through its Engineering Biology Advisory Council.

Our inspiration at Synthace for CAB came from comparing the Engineering of Biology, with the development of software tools for the integrated circuit industry (where our Chairman was an early leader in Silicon Valley). Within the integrated circuit industry as processors became increasingly complex, software products began to incorporate simulated error checking and abstracted design. As such, CAB includes the presence of simulation of both biological designs (e.g. a DNA sequence) and experimental protocols (e.g. add reagent 1, mix, repeat) These simulation layers enable error checking at multiple hierarchical levels of design, a critical factor when running complex experiments or designing complex networks within living cells. As in electronics, we also advocate for an increased use of abstraction (in computing - abstraction is a way to represent essential features of a system without including background details) in both biological design and experimental planning. This is not to say we fully buy into the simplistic and reductionist building blocks approach to biology, but instead so that researchers without developed coding abilities can also leverage the benefits of this approach. This all means that CAB enables the efficient bridging on digital in silico tools and physical wet lab experiments, the missing link between the biology of today and the plethora of computational tools being created in industry and academia. The CAB cycle is illustrated in Exhibit 1.


Exhibit 1. The CAB landscape connects the digital and physical worlds, powered by AI and automation. For a more complete overview, including a discussion of the relevant hardware, please refer to the full ‘Computer Aided Biology: Delivering 21st Century Biotechnology’ whitepaper.

Synthace is predominately focussed upon the translation of biological ideas into experimentally validated datasets: Experimental Design, Simulation, Execution and Data Management. There exists a whole ecosystem of software and hardware companies working on other parts of the CAB cycle. This broad ecosystem of companies can be found within a swathe of overlapping nomenclature; lab informatics, synthetic biology, lab of the future or the ‘closed loop’ to name but a few. The vast majority of these companies being headquartered in Massachusetts or Silicon Valley. 

CAB as an approach is also agnostic to its position in the biopharmaceutical value chain, with several of the companies in our ecosystem (including Synthace) focussed specifically on the less ‘news-worthy’ but still very challenging aspects of the biopharmaceutical value chain such as bioprocessing or manufacturing. There are also companies leveraging the synergistic nature of lab automation with machine learning to accelerate small molecule drug discovery in a non-reductionist way (phenotypic screening). In all these cases, CAB doesn’t seek to replace the scientists or existing systems, but instead seeks to empower them and allow them to work with increased complexity and throughpput.

Walk around any lab and you will see researchers toiling at benches – often with pipettes, excel spreadsheets or even still paper lab books. It is clear that despite the rapid advances in scientific knowledge, a gap has slowly emerged between the complex questions we are asking, and the approach we use to answer these. CAB is a new way of working – digitally augmented scientists unchained from the repetitive and error prone tedium of lab research and instead allowed to focus on innovation. This new way of working will be empowered by the emerging tools of the CAB ecosystem, new digitally enabled hardware and the development of interoperable industry wide standards. By using the tools in the CAB ecosystem researchers will be able to conduct complex experiments faster, leading to better research outcomes. To find out more visit our October 2018 Whitepaper.

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