[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.