Quantitative
Metabolic Modeling
Our goal
We create the tools necessary to predict biological behavior, so as to unlock the full potential of bioengineering. To this end, we combine machine learning, synthetic biology and automation with mathematical modeling. We use these tools to enable the production of renewable biofuels and bioproducts, and combat climate change.
You can find more detailed information on our research here (or click on the figures below for published examples).
Machine Learning
Synthetic Biology
Automation
News
“It is cutting-edge work,” says Héctor García Martín, a physicist and synthetic biologist at Lawrence Berkeley National Laboratory in Berkeley, California. “They are fully automating the whole process of protein engineering.”
Increasing the sophistication of self-driving biology labs might require a new generation of hardware, because existing automated lab equipment tends to be made with a human overseer in mind, says García Martín. A more fundamental challenge is to create self-driving labs able to generate knowledge that can be interpreted by machines, as well as humans.
New paper on self-driving labs!!
The group in the media
Machine Learning Takes on Synthetic Biology: Algorithms Can Bioengineer Cells for You
Berkeley Lab scientists develop a tool that could drastically speed up the ability to design new biological systems (Berkeley Lab News).
See more press releases and ART information on the ART website.
Hector's Public Lecture about Synthetic Biology and Artificial Intelligence
Nerd Nite East Bay public lecture provides a non-technical introduction to our work.
