Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
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Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#2I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#3I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
What's the current cost per base pair? Is a significant portion of the funding really going into synthesis?
Do you foresee synthesis costs falling quickly? It seems like its a major factor holding back commercial applications of synthetic biology.
Congrats on the funding and best of luck as you continue to scale!
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#4I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
This is very cool stuff. I was just wondering what do you see is the biggest technical problem with this technology? I understand that the hardest part currently is transferring the modified genome into all cells, is this still correct?
If I'm interested in this technology how do you recommend learning the required techniques. As a machine learning engineer I know maths more then biology, but want to contribute to the open source movement. Where do you view the biggest impact of software/machine learning engineers can make to the open sourced biology movement?
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#5I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
What's the current cost per base pair? Is a significant portion of the funding really going into synthesis? Do you foresee synthesis costs falling quickly? It seems like its a major factor holding back commercial applications of synthetic biology. Congrats on the funding and best of luck as you continue to scale!
This is Patrick, I lead our Design team here. Rather than commercial applications being held back by synthesis prices, it's the large scale demand from foundries ours that helps synthesis companies scale up their processes. If you assume that synthetic biology is going to be done by-hand for the foreseeable future, demand doesn't scale as quickly and prices stay high.
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#6I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
This is very cool stuff. I was just wondering what do you see is the biggest technical problem with this technology? I understand that the hardest part currently is transferring the modified genome into all cells, is this still correct? If I'm interested in this technology how do you recommend learning the required techniques. As a machine learning engineer I know maths more then biology, but want to contribute to th…
I'd love to hear a reply to this.
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#7I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
I'm a machine learning and have done work with Bayesian methods for modeling dynamic systems. I've been considering jumping into a synthetic biology company in industry. I've been searching for statistics and machine learning problems that need solving in synthetic biology. I suspect that there are two problems where a statistician or machine learning expert could contribute. The first is building data-driven models of metabolic pathways. The second is implementing an active learning approach to organism design -- basically building a robot that iteratively conducts experiments that maximize information while minimizing cost. But I also suspect that synthetic biology companies like yourself and Zymergen are more concerned with scaling up your business in the short term, and that implementing machine learning or computational biology-types of processes is a long term "optimization" task, not important to the core business in the near term. I'm afraid of making the jump if this type of work isn't important to the organization. Can you please comment? Cheers.
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#8I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
This is very cool stuff. I was just wondering what do you see is the biggest technical problem with this technology? I understand that the hardest part currently is transferring the modified genome into all cells, is this still correct? If I'm interested in this technology how do you recommend learning the required techniques. As a machine learning engineer I know maths more then biology, but want to contribute to th…
The biggest technical problem in general for synthetic biology is predictive design. Most of the technology we are building is to enable massive numbers of designs to be tried in parallel b/c the complexity of the cell rebuffs predictive CAD models. This will change as we get more data which is what foundries like ours generate - lots of data.
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#9> works with DARPA to produce probiotics that will help U.S. soldiers stave off stomach bugs
Will there be synthetic probiotics in the future? (I understand that some probiotics are already heavily engineered.)
Re: Ginkgo Bioworks (YC S14) raises $100M to buy a lot of synthetic DNA
#10I'm Jason Kelly, one of the Ginkgo co-founders -- happy to answer questions!
I'm a machine learning and have done work with Bayesian methods for modeling dynamic systems. I've been considering jumping into a synthetic biology company in industry. I've been searching for statistics and machine learning problems that need solving in synthetic biology. I suspect that there are two problems where a statistician or machine learning expert could contribute. The first is building data-driven models…
You mentioned two good examples of how a data scientist can contribute to a synthetic biology company. Models are useful in many ways but only if they are realistic and backed by data. Today, many models are limited in their usefulness because they make assumptions to reduce complexity, assumptions that are not always true in nature. We'd love to design iterative experiments and gather more data, so we can improve and expand these models. However, to end up with a useful outcome, we would need to a) test many designs and b) capture as many experimental parameters as possible. We are working on (a) -- through increasing ability to synthesize DNA, and by improving our foundry capability and scalability (so we can process and assay synthesized samples and analyze results). (b) is extremely challenging due to the complexity of biology. At Ginkgo, all data are analyzed by scientists and engineers with high degree of biological intuition, so they can fill in gaps not captured in data. For these reasons, we have focused our software and computation efforts on building up wetware and automation infrastructure, so we can run more and better experiments.
We are always looking for passionate engineers to join us to tackle tough challenges. Just because something isn't doable today doesn't mean we can't shoot for the moon! There's no better place to change how biology is engineered than here. Ping us if you are interested in joining our efforts.