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The Synthetic biology infrastructure will drive development of cloud based synthetic biology “apps”, and biology-as-a-service

Synthetic biology is the application of science, technology and engineering to facilitate and accelerate the design, manufacture and/or modification of genetic materials in living organisms, as defined by the European Commission. It envisions the redesign of natural biological systems for greater efficiency, as well as create new organisms as well as molecules with desired bio-attributes. Among the potential applications of this new field is the creation of bioengineered microorganisms (and possibly other life forms) that can produce pharmaceuticals, detect toxic chemicals, break down pollutants, repair defective genes, destroy cancer cells, and generate hydrogen for the post petroleum economy.


“Biotechnologies, including synthetic biology, are going to be foundational to the 21st century economy and they’re also going to be a critical arena for global competition in the geopolitical realm,” said Tara O’Toole, former undersecretary of Homeland Security for science and technology and current executive vice president and senior fellow at In-Q-Tel, an Arlington, Virginia-based investment firm that works with defense and intelligence organizations. A 2020 report released by McKinsey Global Institute projected bioengineered products could have a direct economic impact of as much as $4 trillion over the next 10 to 20 years.


This type of revolution is founded on several core biological technologies, “but it is all about being able to read, write and edit the code of life,” O’Toole said. One core technology is DNA sequencing, or the ability to read DNA. Another is DNA synthesis, or the ability to write code for DNA, she noted. “Our ability to write it, to synthesize DNA … is less advanced,” she said. “It’s slower, it’s more expensive, but again we are getting better and better.” Gene editing is another core biotechnology. It allows scientists to alter a DNA sequence by adding, swapping or removing genes. Synthetic biology uses the aforementioned technologies to manipulate multi-cell systems in organisms in a way that can construct new biological parts.


Applications of engineering biology have grown beyond chemical production to include the generation of biosensor organisms for the lab, animal, and field, modification of agricultural organisms for nutrition and pest/environmental resilience, production of organisms for bioremediation, and live cell and gene/viral therapies. The rapid expansion of the field has resulted in new tools and new approaches; however, we are still challenged by the need for novel and more robust computational tools and models for engineering biology. For example, improved models of synthetic systems and of their interaction with their host organisms will facilitate more successful engineering and broader application.


The design of a microbe for any given purpose — to produce a medicine or to sequester CO2 in the air — is a complex process incorporating the interactions of hundreds or thousands of genes, proteins, and metabolic pathways. For this reason, human-led engineering may provide a starting-point, but only recent advances in machine learning can truly optimize this process.


Artificial intelligence (AI) is the science and engineering of making intelligent machines and programming them with reasoning, learning and decision making behaviours. Biology, in particular, is one of the most promising beneficiaries of artificial intelligence. From investigating genetic mutations that contribute to obesity to examining pathology samples for cancerous cells, biology produces an inordinate amount of complex, convoluted data. But the information contained within these datasets often offers valuable insights that could be used to improve our health.


Early leaders in the synthetic biology space, particularly Zymergen, have developed massive chemical, genomic, proteomic, and metabolomic datasets, as well as near-fully automated laboratories to conduct high-throughput experiments that generate even more data every day. These datasets are then fed into machine-learning algorithms that predict the best molecule for a given purpose and the best microbe to produce that molecule. As the datasets grow, the machine-learning algorithms are perpetually trained to offer stronger and more optimal predictions.


This self-reinforcing feedback loop of experimentally-derived data and machine learning optimization has resulted in a moat that competitors will find tough to contend with. With this platform, Zymergen expects to be able to discover the best materials for a given use case, and the most efficient microbe for producing that material. And they expect to be able to do this more quickly and more cheaply than any competitor without similar data and algorithms to leverage.


The foundation of a viable design and manufacturing process for, or using, engineering biology is automation, which requires a complete description of a biological system’s components, data to describe the system’s function and interconnections, and computational models to predict the impact of environmental parameters on the system’s behavior. Data Integration, Modeling, and Automation focuses on robust, systematic use of the design, build, test, learn methodology to create complex systems. Progress requires a purpose-built computational infrastructure that supports DBTL biological processes, the ability to predict design outcomes, and optimize manufacturing processes at scale.


“Synthetic biology is, in essence, a way to write programs—except in this case the computers are cells, and the programs are the genetic materials we introduce into them,” says Duke University researcher Lingchong You, PhD. But achieving these goals will require a concerted effort from engineers, biologists, chemists, computer scientists, and a host of other experts.


Synthetic Biology ‘Apps’

Synthetic biology “apps” come in two forms: a product produced by a microbe (such as silk or food protein), or the microbe itself (e.g., a bacterium that can substitute for traditional fertilizer). In both cases, there are typically three steps in the product development: First, identify the use case. Second, design the microbe. Third, manufacture the end-product.


Organizations such as Ginkgo Bioworks will cover your microbial design needs, Culture Biosciences can optimize your bio-manufacturing process, and a slew of biomanufacturing organizations can deliver on the end-product manufacturing. As this enabling infrastructure develops, synthetic biology product development could be so abstracted away from the core biology skill-set to enable even those without any specialized training to pursue cutting-edge synthetic biology “apps” — at least, in theory.


A second approach relies on developing expertise around a field which the machine-learning engines are not built to optimize. For example, companies discovering and designing new food items, such as Nature’s Fynd, are playing in a niche that has not yet been made vulnerable to Zymergen’s brand of machine-learning enabled disruption. In a similar vein, companies inventing new ways to compete with Zymergen could find a competitive advantage in certain synthetic biology verticals. To this end, companies digitizing new types of data, including the next-generation proteomics championed by Nautilus Biotechnology, could begin to accumulate their own datasets that are advantageous within a given use case.


Ginkgo Bioworks wants to be the AWS of synthetic biology

The Boston-based synthetic biology leader Ginkgo Bioworks which was founded by Kelly and a team of fellow MIT synthetic biology experts in 2008 — builds made-to-order microbes for companies in a range of industries, including fragrances and food ingredients. It takes advantage of the growing ability of scientists to design and print DNA on demand — the field now known as synthetic biology.


Ginkgo — the first synbio unicorn — was most recently valued privately at $4.86 billion. The company has pivoted to the broader pharma industry, receiving a $1.1 billion loan in November from the U.S. International Development Finance Corporation to optimize COVID-19 vaccine manufacturing and expand testing efforts.


Gingko increasingly sees itself as a “platform,” Kelly tells Axios, charging customers for the use of its biological foundry “like AWS does for data center cycles.”

  • Ginkgo also takes royalties or equity in the biological apps developed on its platform — “like the Apple App Store,” says Kelly. With the money generated by the SPAC deal, “I want to create an ecosystem of services that sits around the much more technical platform.”
  • Those include more lucrative areas like drug research and manufacture — Kelly estimates companies spend some $40 billion on biotech R&D work that could be supported and accelerated on Ginkgo’s increasingly automated platform.
  • “It’s like a software company migrating from individually-run servers to the cloud,” says Kelly. “Now everyone just pays a bill to Amazon, and we think there’s a similar big potential market for us” in biotech.


The creation of synthetic biology infrastructure, including low-cost genetic sequencing, automated cloud-accessed laboratories, and biology-as-a-service providers, could enable a democratized ecosystem similar to that seen in mobile app development as bio-preneurs identify profitable use cases for synthetic biology technology. Similar enabling infrastructure is taking root in the field of synthetic biology, a scientific discipline that uses genetic tools to engineer microbes for a wide range of downstream use cases, from manufacturing the screens in our smartphones to producing the food we eat.


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