Predicting Metabolic Pathway Dynamics w/ Machine Learning with Zak Costello - TWiML Talk #163
Analysis
This article summarizes a podcast episode featuring Zak Costello, a post-doctoral fellow, discussing his research on using machine learning to predict metabolic pathway dynamics. The focus is on applying ML to optimize metabolic reactions for biofuel engineering within the context of synthetic biology. The article highlights the use of time-series multiomics data and the potential for scaling up biofuel production. The brevity of the article suggests it serves as a brief introduction or announcement of the podcast episode, directing readers to the show notes for more detailed information.
Key Takeaways
- •The article discusses the application of machine learning in synthetic biology.
- •The research focuses on predicting metabolic pathway dynamics using time-series multiomics data.
- •The goal is to optimize metabolic reactions for biofuel engineering.
Reference
“Zak gives us an overview of synthetic biology and the use of ML techniques to optimize metabolic reactions for engineering biofuels at scale.”