Across industries as diverse as electronics, pharmaceuticals, food, and materials, industrial synthetic biology has begun to produce the goods we need more efficiently and sustainably. Using the synthetic biology approach—or “synbio” for those familiar with the field—start-ups and Fortune 500 corporations alike are genetically-engineering microbes to manufacture a massive range of goods from the touchscreens in your phone, to the cotton in your t-shirt, to the “meat” in your plant-based burger.

Traditionally, this synthetic biology process has been a slow and hypothesis-driven endeavor, dependent on researcher genius—and a healthy dose of luck—to design these microbial “mini-factories”. But not anymore.

Two companies have embraced the power of computational biology to take the guesswork out of synthetic biology and drive this young industry into the mainstream. Zymergen and Ginkgo Bioworks both use vast metagenomic databases, machine learning, and robust automated laboratories to design microbes custom-suited to manufacture a desired good. Both organizations have raised significant capital to build out their platforms, with Zymergen having raised a $400-million Series C in 2018 and Ginkgo Bioworks raising almost double that figure in multiple rounds over its lifetime.

Despite their shared mission to turn the art of engineering biology into a science, the two synbio pioneers are strongly differentiated by their respective technology stacks and business models. Analyzing the key differences between the two can help us understand how the synthetic biology industry as a whole may evolve and where the value levers are hidden in the synbio ecosystem.

The synbio value chain

At its core, industrial synthetic biology offers a path to both producing humanity’s typical array of materials—such as plastics, textiles, food, and medicines—more efficiently, as well as creating completely new substances. To produce these goods with biology relies on a three-step process of:

1) Molecule Identification: Identifying the molecule you want to create;

2) Biology Design: Designing a microbe to manufacture the good; and

3) End-Product Manufacturing: Scaling the manufacturing.

Overlaying this simplified three-step value chain against each of these organization’s core competencies illustrates where the two companies hope to compete in the industrial synbio field and how they will likely co-exist (or not) with other players in the space.

A Shared Strength: Biology Design

Both Zymergen and Ginkgo have established wide competitive moats around the Biology Design process. By constructing massive and growing metagenomic databases—mapping genes, proteins, and metabolic pathways—and investing capital into automated labs capable of running high-throughput experimentation, the two companies have achieved a level of take-off velocity insulating them from would-be challengers seeking to enter the field.

Both companies have embraced an R&D process that creates a positive feedback loop, perpetually self-improving their ability to design microbes and putting greater distance between them and any potential competitors. Both companies leverage their massive datasets to train machine-learning algorithms that help design potential microbes for a given purpose. They then turn to their automated labs to test thousands of strains for performance, creating new data-points to further improve their machine-learning for the next batch of microbes.

While important differences exist between the two companies’ Biology Design stacks regarding the size of their datasets, the robustness of their software, and the automation of their hardware, both can claim strong proficiency in the Biology Design portion of the synbio value chain.

Integrating the rest of the Synbio value chain

Beyond Biology Design, Ginkgo and Zymergen have taken divergent approaches to both Molecule Identification and End-Product Manufacturing. Regarding the former, Zymergen has developed chemistry and materials datasets and supporting lab infrastructure to rigorously scan their databases for promising molecules, predict their fit for a given purpose, and test those predictions empirically. To its credit, Zymergen has announced three products so far—a biofilm for electronics, an insect repellent, and a crop pest control agent—that are all reported to be completely novel, never-used-before molecules.

Zymergen has also endeavored to internalize End-Product Manufacturing capabilities. After Zymergen designs a product, they turn to a network of close bio-manufacturing partners to produce the end-product “in-house” for downstream customers and partners.

While exceptions exist, Ginkgo has concentrated its focus almost solely on the Biology Design piece—perhaps with good reason. The ability to predict how a new molecule will behave varies across applications and necessitates a diverse toolkit: the analysis of a molecule to be used in electronic consumables varies greatly from that of one to be used in food or nutrition, diluting the scalability of a Molecule Identification skillset. For this reason, Ginkgo has focused their platform less on the discovery of totally novel substances, but more on finding a better means of manufacturing those we already know.

In regard to End-Product Manufacturing, commercial bio-manufacturing represents a fairly old technology—it relies primarily on the same fermentation process used to make beer.

Accordingly, Ginkgo believes that the End-Product Manufacturing element of the synbio process is relatively commoditized, and that their platform does not provide much marginal advantage here. In Ginkgo’s worldview, Molecule Identification appears to be too specialized beyond their core competency, while End-Product Manufacturing is perhaps too commoditized to offer much additional value.

In a nutshell, Zymergen has positioned itself as a “products” company with the infrastructure to cover the full product development process, while Ginkgo acts as a “platform” providing a modular solution at one valuable link in the synbio value chain. Under this strategy, Zymergen can produce differentiated products that cannot be found anywhere else—and Zymergen will be able to charge for that unique value.

Lacking an internal “products” engine, Ginkgo has cast a wide net of partnerships with industry innovators and internal spin-outs, in effect creating a decentralized product discovery network that can leverage its biology design platform to bring products to market.

Predicting the synbio ecosystem of the future

While the two companies’ early strategies may not be indicative of their future evolution, their current directions do reflect their intended role within the larger synthetic biology ecosystem. On the one hand, Zymergen seems to be driving for a world where they can act as the one-stop-shop for anyone’s manufacturing or materials needs—they can ID the best substance, engineer the best organisms to produce it, and manufacture the product for sale to customers.

Alternatively, Ginkgo’s platform allows for a more distributed ecosystem—where they can leverage their unique expertise in organism design to provide downstream partners with the crucial key to implementing their own bio-manufacturing processes.

If either approach can succeed in pulling synthetic biology into the mainstream, we could all benefit from a cleaner, healthier, and more abundant world.


Matthew Kirshner works in life sciences consulting at Putnam Associates, focused on pharma and biotech organizations commercializing novel therapeutics and diagnostics. He has no professional affiliation in his work with either company referenced in this article.

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