Predicting Metabolic Pathway Dynamics with Machine Learning w/ Zak Costello

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

In today’s episode I’m joined by Zak Costello, post-doctoral fellow at the Joint BioEnergy Institute.

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.


On July 17th at 5pm PT, Nic Teague will lead a discussion on the paper Quantum Machine Learning by Jacob Biamonte et al, which explores how to devise and implement concrete quantum software for accomplishing machine learning tasks. If you haven’t joined our meetup yet, visit to do so.

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About Zak

Mentioned in the Interview

“More On That Later” by Lee Rosevere licensed under CC By 4.0

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