Zymergen approaches biology with an engineering and data-driven mindset. Its platform integrates robotics, software, and biology to deliver predictability and reliability during strain design and development. Zymergen’s solutions engineers develop software tools to help its scientists plan experiments, update sample management and measurement data, and visualize results in meaningful ways and leverage Jupyter notebooks to collaboratively prototype solutions alongside scientists and factory automation. The Jupyter notebooks are run on an external server in virtual environments, solving dependency conflict problems.
Through Jupyter notebooks, Zymergen has been able to continuously deploy software solutions to improve its scientists’ workflows. Examples range from ensuring that a robot executes a given protocol to performing quality control analysis on a given DNA sequence. In addition, scientists are able to use hosted Jupyter notebooks to test and develop their own Python scripts without the need for local tooling.
Danielle Chou shares examples of some of the deployed scientific tools, using ipywidgets and magic commands to isolate execution from users and provide practical and scalable tools. Danielle also highlights how Python packages developed in-house are distributed and made available for scientists to use within their own Jupyter notebooks.
Danielle Chou is a solutions engineer at Zymergen, where she works on custom software tools for scientists. Previously, she worked on failure detection software for an ingestible sensor company and studied bioengineering at UC Berkeley and UCSF.
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