At the critical juncture of Shakespeare’s Henry V, on St. Crispin’s Day, 1415, when the French forces looked ready to overwhelm the small band of knights that are with the English King at Agincourt, the young King Henry says:
This day is call’d the feast of Crispian
…And Crispin Crispian shall ne’er go by,
From this day to the ending of the world,
But we in it shall be remembered-
We few, we happy few, we band of brothers.
The names of the characters have changed and where there was Harry the King, Bedford and Exeter, Warwick and Talbot, Salisbury and Gloucester, we now have Zymergen, Arzeda and Gen9, Ginkgo and Amyris, Caribou and Edison AgroSciences, Calyx and Benson Hill.
The Democratization of R&D
The nature of collaboration across the advanced bioeconomy is shifting dramatically – and we are seeing the democratization of R&D. Like the Sasquatch or the Northwest Passage, hitherto it’s been more sought after than seen, discussed than discovered. But now becoming an unmistakable reality.
For some, the shift is prompted by economics — doing more with less by finding allies in a common pursuit.
But something more fundamental is underway and that is because of the Cloud, and the costs of gene sequencing, and the development of powerful algorithms whereby machines learn faster and become more accurately predictive.
The CRISPR revolution
As Dan Watkins of DFJ’s Mercury Fund put it, “There’s a total sea change in biology with CRISPR-CAS gene editing coming on less expensively, and we’ve got next-gen sequencing and the costs of the cloud coming down fast. It’s not that different from the IT world where now everyone has a supercomputer in their pocket.”
Today, we’ll look at the Cloud, and cloud biology. Benson Hill BioSystems is one of the companies at the heart of it. The CEO is Matt Crisp, who previously was chairman and co-founder at Edison AgroSciences and before that headed the agricultural biotech unit at Intrexon — and has absolutely no known relation to either Crispin Crispian or CRISPR. Well, now he does.
About the Cloud
For most, the Cloud is some kind of off-site server. You can save more files for less cost via the Cloud than a iPhone has memory, and you can stream one of your zillion songs or movies to “any of your devices, at any time”.
For others, the Cloud is a file-sharing environment. You can access common files across different locations, divisions or organizations — so that people can work on the same data and the same problem from a research lab in Emeryville, California or a mud hut with good bandwidth in the developing world.
The new Cloud, however, is more than that. It is a world where data is not only stored, or worked on by a defined team, but where algorithms will allow us to manipulate data faster, use advanced analytic or data-gathering tools, and even learn from one data set in order to draw conclusions faster from a second data set. Machine learning and application sharing.
We Band of Brothers
How did Benson Hill get going? In part, they are the product of the New Collaboration as much as a catalyst of it. Specifically, the technology was in an advanced state of readiness, developed at the Danforth Center, before Crisp and Benson Hill licensed it. The cost of capital would have been too high to establish a cloud biology infrastructure from scratch.
At the heart of Benson Hill is CropOS, a cognitive engine that integrates crop data and analytics with the biological expertise and experience of the scientists of Benson Hill and its partners.
CropOS is continuously advancing, always learning from our partners’ challenges and delivering insights that accelerate their R&D programs. It improves with every new dataset and milestone achieved, strengthening the system’s predictive power.
With CropOS, partners can:
• Assess the genetic resources of their populations with the power of CropOS, establishing a roadmap to accelerate crop improvement.
• Design CropOS solutions to provide actionable decision support and unlock the potential of a broad range of genetic resources.
• Deliver crop improvements using a spectrum of breeding and genetic modification approaches that our partners choose.
We have reported (such as here, with Finstere) on a new wave of companies coming forward, much more advanced and technically ready, out of the labs than before. Here’s one tangible fruit that reduces the need for high-risk, high-reward, high rate of return, fast-to-market capital.
We’ve had economies of scale. But now, Intelligence of Scale. Without the scale.
Benson Hill CEO Matt Crisp added, “Ag has been slow to adopt. The human health space is the best example, where tech gets adopted faster than industrial or ag. given the simplicity of the organisms that are used. Now, companies like Zymergen and Gingko are using a more empirically driven approach, with virtualization and automation, and they combined it into something revolutionary rather than evolutional.
We Happy Few: Consolidation and the falling of barriers
As many know, there’s a wave of mergers & acquisitions and the Big Six will become the Big 3 or 4. Monsanto and Bayer, Dow and DuPont, Syngenta and ChemChina. And more perhaps pending, a byproduct of the commodity down-cycle, where the companies are getting pressure from shareholders to get costs down and build market share, and become bigger.
But these have been the companies with the big R&D budgets and the usual suspects when it came to acquiring promising technologies from the venture space.
So, perhaps these technologies are coming forward at a great time. As Benson Hill’s Crisp puts it. “Technology is breaking down the barriers — data acquisition, genotyping, sequencing. These are happening at the same time as a dramatic uptick of high risk capital being deployed into ag.”
We would not die in that man’s company / That fears his fellowship to die with us: VCs, pure-plays and investment theses
A no-brainer to raise the capital to advance this technology class into agriculture? Hardly.
“It was really a struggle to raise capital,” Crisp recalls. “It’s more west coast money than pure ag plays, and companies like the ag vertical but stay close to home. So they go for precision ag and sensors and Big Data imagery and phenotyping. The mindshare of Precision Ag trumped everything, and there’s a recognition that with agriculture there’s still an inertia and you don’t get the rates of adoption. Because you’re convincing a farmer to add a line to the P&L at a time of low commodity prices. It’s a heck of a lot more difficult than telling farmers you have a better fertilizer or seed, something they are already investing in.
“We were fortunate to find Prelude Ventures, which was the only firm in San Francisco that has a thesis on carbon impact companies and a couple deals in Agriculture. But they are not pure play. On the pure-play side there are only a handful. Such as Cultivian, Finistere or Open Prairie. MLS is not pure play but knocking on the door in a big way and have raised a slightly larger fund. There’s interest from the broader VC community — but they want to rely on one of the stalwarts of the Ag VC space to help with due diligence and strategics.
Why now, why CropOS?
The genesis of crop os was work funded, with Todd Mockler and others at the Danforth Center in St. Louis. Primarily NSF funding, and a lot of what has become CropOS was developed through that pathway. In the case of Benson Hill, focus was the issue. The answer? To focus on photosynthetic trait development. Probably the most compelling topic in crop biology, and a lot of people failed working on it.
“We were not going to do 50 projects in 50 verticals and try to boil the ocean,” Crisp said. “If we could do this we would have a first mover advantage, building on what Danforth had given us.”
One of the problems?
Photosynthesis is a complex trait — you can’t simply sequence on the basis of photosynthesis genes. You have to look at hundreds of thousands of genes across systems. So, a strong computational technology would be needed.
But why something so biologically-driven and not just a “brute force” computational approach. For that, go see The Imitation Game, which detailed British efforts to crack the German naval code, Enigma, in the Second World War. Brute force computation failed. Using their own intelligence and experience, the code breakers were able to narrow the computation target down enough to crack the code.
A Confederacy of Machines and Projects
“This is an amazingly powerful tool,” Crisp noted. “There’s a lot of machine learning, this is a
cognitive engine that gets a lot smarter the more we put into it. There’s the data and the algorithms. We have data and there’s public data but we can utilize parter data to enhance all the data. So, you see this nonlinear community value of the platform. The new partner benefits all the partners. So, people want to join. They are most compelled not just by the analytics but by the community effect. They could never on their own pull together the amount of intelligence that a platform offers.”
The Secrecy Factor
At the same time, Crisp noted, “We’re not Big Brother, we never own the data, and our job is to build a front end that demystifies, but in a way that doesn’t open the kimono. It’s like 23 & Me, where it will tell you if you have a cousin on the platform after you’ve submitted your DNA, but you only know if you want to. We provide a potential for data exchange.”
The Democratization of Crops
It’s not only the empowerment of companies and researchers. Think about crops. About biodiversity itself. Corn and soybeans — huge R&D engines behind them,. But what about jute and hyacinth and pongamia and a host of crops that few have ever heard of. Crops that haven’t received or thought to deserve investment in R&D, can now have access to computation and genetics. It levels the playing field for the guy in rural India that he will have access to the same tools as they have in Des Moines, or anywhere.
Now, it’s not a universal panacea. This will work in certain parts of the developing world. Elsewhere,t eh infrastructure just doesn’t exist. Power, wireless, DNA prep tools.
More than that, though. The mudhutter might well have an advantage because the data is being analyzed and processed on a platform with thousands of other companies, working on thousands of other projects, from which machines are learning how to faster build metabolic pathways, re-structure genomes, analyze pathogens, advance complex traits, and construct defenses against predators.
The more researchers on the platform, the better. Not just because of economies of scale, but because of something new.
Broadly speaking, the platform that is being constructed is generally going to be known as Cloud Biology rather than Cloud Computing. The concepts are not all that new, in fact; they’re used every day in the world of the digital economy. Pharma has been using it.
More about Mercury
It’s based in Houston. We’ve covered the investment in GlycosBio overt the years. They’re on their third fund, with $200M under management. They’re a seed and Series A investor, mostly.
“It’s about democratization and accessibility,” said Mercury’s Watkins. “ There’s a reduced cost with the Cloud and we are seeing the same things with biology. We’re less interested in the precision ag and the frothy food deals. “
What about exits?
“Obviously if you are building a traditional ag offering” Watkins said, “the exit five years ago was to one of those players. And that’s a bit of a concern. But this market is ripe for disruption, and these giant companies that are going to result are not going to be moving as fast. When the tools become easier to use, then the barrier is around distribution, and we’ll see what happens.”
“Similar things are happening around pest control, with a number of companies working on delivery around biologicals instead of chemicals. Who buys those companies, we’ll have to see how that works out, but early disruptors will find a home.”
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