[hongloumeng]

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.

[overclocked]

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.

[squarecog]

Hi, I manage the data science team at Zymergen, and I can tell you we are working on these topics (and hiring). Shoot me an email if you'd like to chat -- dmitriy at zymergen.com