Metabolism Meets Machine Learning: Computational Metabolomics

Explore the intersection of metabolism and machine learning in this inaugural lecture on computational metabolomics. Delve into the world of metabolites, their measurement techniques, and the role they play in biological systems. Discover how Professor Tim Ebbels applies data science to metabolomics, including projects involving the Internet, NMR spectra analysis, and differential correlation networks. Learn about future directions in the field, such as pathway analysis, interpretation methods, and integration with other omics data. Gain insights into the challenges and limitations of metabolomics, as well as its potential applications. The lecture also touches on teaching, work-life balance, and the importance of family in academic pursuits. Engage with thought-provoking questions and discussions on nonlinear correlations and multi-omics pathway integration.

Introduction
Welcome
What keeps you alive
What are metabolites
Is it all about genes
Measuring metabolites
How did Tim get into this
The project
The Internet project
NMR spectra
Differential correlation networks
Future directions
Pathways
Interpretation
Example
Integration
Summary
Perspectives
Teaching
Family
Worklife balance
Questions
Limitations
Limitations of metabolomics
Multiomics pathway integration
Applications
Nonlinear correlations