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Automated ALE has already proven a powerful player in the toolkit available for strain improvement at Ginkgo. Here, Simon Trancart, head of ALE at Ginkgo, discusses how partners have worked with Ginkgo in the past, as well as ongoing work that is aimed at making Automated ALE at Ginkgo accessible to new industries.
Humans of Ginkgo is an interview series featuring Sudeep Agarwala interviewing some of the brilliant folks at Ginkgo to learn more about the technology that makes our work possible.
— This is the final part of a three-part interview. Read Part 1 Why ALE? here or Part 2 Inside ALE here. —
Sudeep Agarwala: In thinking about how different groups could interface with ALE at Ginkgo, it sounds like there are a few different scenarios: a first case in which ALE is part of a larger engineering program at Ginkgo. There’s another case in which a customer’s done a lot of work beforehand on their strain or maybe has been using that strain for years at commercial scale and just wants the output of ALE without a lot of characterization. Then maybe there’s this other hybrid case where the customer wants the strain and there’s characterization about what mutations have come into the strain, how it’s performing in high detail, etc.
Simon Trancart: When a customer comes with the sole goal of improving the commercial strength of their strain, most of the time, they don’t want to pay an additional bolus of money and time that would be necessary to understand what the mutations are. So of course we can offer a limited scope of work in these situations. And that’s fair: in many instances there’s no need to do extra work to understand the mutations; performance and time to market is what matters here.
But I would say that for earlier stage programs where ALE is part of the R&D process or programs of course we’ll look for mutations. If we think it’s relevant, then we can learn from it too. And if we demonstrate by retro-engineering the parenteral strain with what we believe are the causative mutations that they impact the phenotype, that’s a very powerful way to validate.
So I think of ALE as an evolutionary engine to generate mutations that can be added to our understanding of biology. That’s where I think there is a very important value as well.
SA: Ginkgo has a huge number of resources in its Foundry. If all I wanted was the ALE service, is that something that Ginkgo would offer?
ST: Absolutely! And I believe we are the best partner for it. ALE has become trendy and we recently have seen startups and spinoffs from academic labs that propose competing services. They’re probably cheaper as they need to penetrate the market. But from our perspective, the automated ALE that we’re working with has been validated for a wide range of organisms and applications. It includes certain selection modes that we believe are unique and have a lot less inherent risk than the other competing systems out there; we’ve worked hard to ensure that we have a superior technology. And, in thinking about how we partner with customers, we’re trying to be creative with our pricing, so that what we’ve built can be accessible to startups that need to achieve milestones quickly or industrial players that need to get a return on investment faster.
Yes, we do projects that are mostly based on the use of automated ALE for customers that are looking to get started with strain improvement, others looking for cost reduction through adaptation to new conditions such as new feedstocks or higher temperature, or other applications accessible by ALE. Our experience means you have a better chance of success. Having said that, Ginkgo has great power as a one-stop shop where you can have a full external program. That way you don’t have to coordinate development between separate teams. I think Ginkgo creates even more value to customers in this type of projects: the way that we reduce costs is actually to improve the efficiency of an R&D workstream.
SA: What types of things are being developed for automated ALE at Ginkgo?
ST: We had a successful, I would say “proof of concept” experiment with filamentous fungi that produced very large filaments. We were positively surprised by the results because we could perform all the basic fluidic operations from transferring from one chamber to another, taking samples, diluting, et cetera, without too many issues.
The only issue is that the optical density measurement was very noisy. But I would say that we are pretty confident that it should work with low-viscosity Aspergillus strains that are at Ginkgo because they behave almost like yeast. Right now, we are working on a proof of concept with two of those low-viscosity strains, to evaluate the suitability of our automated ALE system with that type of organism.
We have also worked with acute myeloid leukemia cells. Even though it was a very short run, it was promising. I think that there is potential for other non-adherent mammalian cell lines as well. But we will need to investigate this further. We are evaluating how we can engineer our system for a wide range of cell lines.
SA: You’ve talked about how ALE can be used in conjunction with genetic engineering techniques or alone in unbiased strain construction. What are some of the more creative uses of ALE you’ve seen?
ST: I do also believe that our capability to continuously cultivate organisms for a very long time can be interesting for other applications than improvements through evolution. We have one customer who has been using our technology for many years. And in the last few months, they have been using it to benchmark different strains against the genetic stability criterion to choose the very one strain that they were going to inoculate in their first commercial fermentor. And they were concerned that there would be genetic drift because it’s a continuous process and their scheduled maintenance is every three months.
They wanted to have a very stable strain and they thought that they had no other technique that could reproducibly expose each of the different candidates to stresses similar to those they’ll see during the long fermentation. We developed a system that can expose strains to reproducible conditions for long durations and that could get close to those stresses of their particular process. But of course, it’s important to note that since automated ALE is at lab scale, we could not really mimic industrial conditions.
We’ve also been talking about the strain as the output of automated ALE, but the evolution can also tell us about certain products’ efficacy as well. For example this system can also be used, for instance, as a pre screening tool for antibiotic molecules or prebiotic/probiotic strains and compounds, where we would inoculate our system with a microbiome model or organisms, and monitor how these molecules or strains modulate the population in the continuous cultivation over time–how the residence time of the product or what is the impact on the population, etc.
So the capability to be able to cultivate cells for a very long period is powerful. And being able to maintain sterility and prevent biofilm formation while monitoring the genotypic and phenotypic in that population presents a versatile tool that has applications in a wide range of fields.
Posted by Sudeep Agarwala
Simon Trancart joined Ginkgo through the acquisition of Altar, a French biotech company he co-founded and led as CEO. Altar specialized in automated adaptive laboratory evolution (ALE), a niche that Simon navigated with his background in engineering and civil engineering.
At Ginkgo, Simon leads the Adaptive Laboratory Evolution, based in Évry-Courcouronnes, France. Simon’s work focuses on the automated ALE process, which the performance of ALE campaigns. He has been instrumental in integrating the ALE team’s work with Ginkgo’s foundry services, enabling better execution and insight into ALE. Simon’s expertise extends to the application of ALE in various organisms and its coupling with rational design.
