Explorations in Biology: DNA as Data Storage & High Throughput Screening

For the month of July, we will feature two main talks on cutting-edge advancements in biology and data. Karin Strauss of Microsoft Research discusses the ongoing project of storing data into DNA, and Erin Shellman of Zymergen discusses robust statistics in screening candidates in drug discovery and synthetic biology.

The event will be streamed and recorded to watch later. You will be able to find the livestream here (https://www.youtube.com/watch?v=XJvFUxpwplU).

Talk 1: Karin Strauss - “A DNA-based Archival Storage System”

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Demand for data storage is growing faster than the capacity of existing storage media. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 exabyte/mm3 (10^9 GB/mm3), and long-lasting, with observed half-life of over 500 years.

This talk will cover our ongoing work on designing a storage system to archive digital data in DNA. It will cover the entire process of storage, from bits to DNA molecules back to bits, and touch on some of the new features in our approach. It will also discuss wet lab experiments we’ve performed to demonstrate feasibility, random access and robustness of our proposed encoding. Finally, this talk will highlight trends in biotechnology that point to the practicality of large scale DNA-based archival.

Karin Strauss is a Researcher in Computer Architecture at Microsoft Research. Her research interests are in emerging memory and storage technologies, as well as hardware acceleration of compute- and memory-intensive workloads such as machine learning algorithms. She has worked on multicore design, hardware support for debugging on multi-threaded shared-memory software, and reliability of systems built out of main memories that wear out. Most recently, she has been working on DNA digital data storage.

Talk 2: Erin Shellman - Catching the Most with High-Throughput Screening

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High-throughput screening (HTS) is a technique applied in drug discovery and synthetic biology to screen thousands of 'candidates' (e.g. molecules, cells, strains) for expression of interesting traits. In exchange for throughput, we often pay a penalty in terms of sample size and variance. Small sample sizes and highly variable measurements can lower our ability to detect improvements . In this talk, I’ll describe a simulation environment for conducting power analysis and describe special considerations for maintaining high power with a moving target.

Erin Shellman a statistician, programmer, and senior data scientist at Zymergen. She has done research and data science in a broad range of industries including retail, cloud computing, systems biology, and biotechnology. Along the way, she built product recommendations, web scrapers, interactive visualizations, and analyzed terabytes of data. She is passionate about technology education and teach data mining at the University of Washington’s school for Professional and Continuing Education and Python programming in an after-school Girls Who Code club. I also co-organize the Seattle chapter of PyLadies, an international mentorship group for women who program in Python.