In today’s episode I’m joined by Zak Costello, post-doctoral fellow at the Joint BioEnergy Institute.
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Zak joins me to discuss his recent paper, “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.” In our conversation, we start with an overview of synthetic biology, and from there dig into Zak’s particular application, which is the use of ML techniques to optimize metabolic reactions for engineering biofuels at scale. We get into an interesting chat about BioHacking at the end of our interview; it turns out that it’s pretty easy for hobbyists to get started on this type of work, which is pretty wild.
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Mentioned in the Interview
- Joint BioEnergy Group
- Hector Garcia Martin, JBEI
- Central Dogma of Biology
- Limonene Fuel
- Biocurious Biohacking Space
- TWIML Presents: Series page
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0