The molpigs Podcast

Join us for the first of our 'Meet the Molecular Programmer' series, in which we talk with seasoned academics in our field about their journey and life experiences. In this episode, we chatted with Prof. Rebecca Schulman.

We briefly talked with Rebecca about her work from her early PhD journey to more recent work coming from her lab. We heard about her stories, the challenges, fun and exciting memories she had and lessons she learned during graduate school, and her wisdom about running her own lab. Rebecca provided valuable advice to students surviving and thriving graduate school and suggestions regarding working in a lab. We also had wonderful discussion about the interdisciplinary property of our field. Hope you enjoy the conversation! We certainly had a lot of fun!

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Find more information at the episode page here:
https://podcast.molpi.gs/media/schulman-r-7ffb8c72c09d6d9b/

For our first event, Brenda Rubenstein has presented a tutorial on her lab's approach to storage and computation, making use of the chemical properties of a variety of types of small molecules. This was a real tour-de-force, and is worth a watch. Be sure to listen to our subsequent Q&A session in a couple episodes time!

Abstract: As transistors near the size of molecules, computer engineers are increasingly finding themselves asking a once idle question: how can we store information in and compute using chemistry? While molecular storage and computation have traditionally leveraged the sequence diversity of polymers such as DNA, our team has recently demonstrated that vast amounts of information can also be stored in unordered mixtures of small molecules. In this tutorial, I will begin by explaining this new, more general molecular storage paradigm and how polymers fit into it. I will then describe how our team has married combinatorial chemical synthesis with high resolution spectrometry to experimentally realize this paradigm and store GBs of information in small molecules and metabolites. Lastly, I will end with a discussion of how these storage principles can be combined with machine learning techniques to realize fully molecular neural networks for pattern recognition and image processing. The new paradigm discussed in this tutorial will lend itself to new means of increasing molecular storage capacity and interpreting the many small molecule chemistries that underlie "computing" within the body.

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Find more information at the episode page here:
https://podcast.molpi.gs/media/rubenstein-b-2b86de754345/