In the early days of the semiconductor industry, integrated circuits were designed by one or two engineers with slide-rules, hand-drawn on paper, and then given to a lithographer to print onto silicon wafers. As circuits became more complex, blueprints gave way to software. These digitally represented designs were much more than a reproduction of a pencil sketch: productivity, design quality, and communication all improved rapidly thanks to software’s ability to codify desired behaviors into actionable layouts, while also allowing for easy, iterative design improvements.
Today, large teams of engineers design circuits using high-level languages that automate the process, and chip layouts more detailed than a street map of the entire U.S. can be generated automatically. The result has been a revolution in engineering and design, manifesting itself as Moore’s Law and the Information Age itself.
Today, a similar revolution is happening in biology, most notably in the field of synthetic biology. And comparisons between computer-aided design (CAD) and computer-aided biology (CAB) are hardly accidental.
Biology, like integrated circuit design, is complicated.
In recent years, automation has revolutionized how we “do” biology: driving down the cost of sequencing, facilitating open-source science, and pushing screening and many other processes towards higher throughput. In parallel, this trend has pushed biological experimentation into the realm of “big data,” where the inherent complexity of biology is finally beginning to be codified in the form of large datasets from increasingly optimized experimentation.
However, the engineering and synthetic biology world has not quite been able to harness and systematize these developments into a sustainable positive feedback loop. Single-factor experiments, such as the one described above, remain the norm because of how this automation has scaled — in the form of liquid handling robots or electronic “lab notebook” technology, for example, but not at the foundational level of expanding and enhancing experiments to enable effective data integration and iterative design which effectively captures the multivariate complexity of biology.
“It takes weeks and weeks to program robots, and it’s not good for a different combination of factors with a completely different experiment the next day,” says Tim Fell, CEO of Synthace and member of the United Kingdom’s Synthetic Biology Leadership Council. “It’s important to look at manufacturing holistically so we simultaneously can bring wonderfully flexible automation to inflexible hardware and enter this new frontier of computer-aided biology.”
Synthace is a bioprocess-turned-software company founded in 2011, and it wants to accelerate the inevitable equivalent shift in biology by making high-throughput experiments easy and ubiquitous. For Fell, automating biology and silicon chip production, at their highest levels, are essentially the same thing.
“These shifts are both about making the physical digital and the manual automated,” he says. “They’re both digital tooling to help these processes along. And it’s digital tooling we need to leverage tools such as machine learning to unravel biological complexity faster, and with that better insight, to close loops of iterative design,” he posits.
The cornerstone of Synthace’s engineering biology endeavors is Antha, a cloud-based software platform for automating and improving the success rate, efficiency, and scalability of biological processes by connecting together all the hardware in a lab. Unlike most other platforms that digitize biology, Synthace still has a lab — a key differentiating factor that facilitates essential validation of new workflows.
To Fell, this is the key to ensuring everything from easily tweakable out-of-the-box protocols for processes as simple as PCRs (the same technique used for COVID-19 testing) and automated data aggregation creating structured datasets for high-dimensional statistical learning to iterative multifactorial experiments and optimization of protein and gene-based bioprocessing. “Our customers don’t want software,” he explains. “They want a biological outcome that produces reliable biological outputs.”
This technology is the framework of the company’s two white papers: Computer-Aided Biology: The Metadata Responsibility, which highlights the crucial responsibility to define, capture, and combine metadata at the point of creation to facilitate deeper analysis downstream, and Computer-Aided Biology: Delivering biotechnology in the 21st century. This philosophy has not gone unnoticed: Synthace’s major partners and customers include Merck, Oxford Biomedica, Dow, Microsoft Station B, Tecan, and Syngenta, with work ranging from vectors for CAR-T cancer therapies to optimization of liquid handling robots.
The dawn of digital biology
The value of such a mindset becomes apparent when considering the myriad intertwined pathways that accompany most any biological phenomena — there is seldom just one protein involved. Multiple proteins and pathways need to be screened and understood against the backdrop of innumerable other cellular components. That kind of experimental design requires intense scalability and organization.
“We can only do this if we codify biological experiments in an unambiguous way, akin to standards of CAD,” Fell explains. “To do that, you have your examples of what needs to be defined, then you build experimental blocks, and then you pass your parameter set. That gives you the structure and context to be able to use your data downstream.”
Synthace’s approach has gained notable traction within a variety of communities, drawing on its own team with broadly interdisciplinary skillsets to push the technology forward. “Multifactoring [screening beyond just one variable] turns cynics into evangelists in one experiment,” Fell remarks. “There’s no going back. You need these higher-order interactions to extra true insights.”
Judging by the investing team behind Synthace, many agree. Chairman of the Board Bob Widerhold, integrated circuit veteran from Bell Labs and later Cadence, is incredibly optimistic about the future of computer-aided biology. “Cadence turned out to be the leader in the [integrated circuit] space and a multi-billion dollar company. I see the exact same scenario playing out 40 years later in biology with the formation of a computer-aided biology industry, and I hope to help Synthace be the Cadence of the Biology industry,” he says.
Widerhold sees this change as not only inevitable but also crucial. “The same shift that happened in the early ’80s in the semiconductor industry, designing every aspect of a microprocessor on a computer to handle increasing complexity, needs to happen in the biology industry,” he asserts. “I believe this shift will usher in a period of incredible exponential progress in biology and enable biology to reach its full potential to change the world in a very positive way.”
Hermann Hauser, co-founder of Amadeus Capital Partners with successes including acquisitions by Microsoft, Illumina, and Nvidia, emphatically agrees on biology’s potential and the progress needed to make a promising future reality. “Synthetic biology is the future of biology,” Hauser suggests, “but biology needs standardized lab procedures to produce replicable results the way microprocessors standardized instruction sets for programming. This will make it possible to program biology.”
These are grand visions of where the engineering biology industry is headed next, but many moving parts will need to mature to realize these visions. “Everyone has a part to play in this ecosystem,” Fell declares, “and ours is in experiment execution.” While Synthace hopes to find enthusiastic advocates in other companies to evangelize their overarching missions, the company will continue to drive the field forward and its most basic, foundational level: automating and abstracting biology to scale experimentation into the next frontier.
Subscribe to my weekly synthetic biology newsletter. Thank you to Aishani Aatresh for additional research and reporting in this article. I’m the founder of SynBioBeta, and some of the companies that I write about are sponsors of the SynBioBeta 2020 Global Synthetic Biology Summit.