Quantitative

Metabolic Modeling

Our goal

We create the tools necessary to predict biological behavior, in order to produce renewable biofuels and bioproducts, and unlock the full potential of bioengineering. To this end, we combine machine learning, synthetic biology and automation with mathematical modeling.

You can find more detailed information on our research here (or click on the figures below for examples).

Machine Learning


Synthetic Biology


Automation


News

Hector Garcia Martin featured in National Public Radio (NPR) interview talking about synthetic biology, climate change and machine learning.

(at the Basque Center for Applied Math)

You will develop a predictive model of microbiome and human metabolism by leveraging high performance computing to efficiently sample the full metabolic phase space.

The postdoctoral candidate will work under the supervision of Ikerbasque Research Professor Elena Akhmatskaya (MSLMS group, BCAM) and Hector Garcia Martin (LBNL, Berkeley)

(At Berkeley National lab)

The candidate will combine machine learning, synthetic biology, and automation to make bioengineering a predictable endeavor, and enable the production of commercially viable bioproducts for the benefit of society. This position will be part of the Quantitative Modeling Group led by Hector Garcia Martin.


New paper about the opportunities in the intersection of synthetic biology, machine learning and automation

New paper measuring metabolic fluxes in R. opacus

New paper leveraging machine learning to facilitate metabolic engineering.

Last updated August 23rd 2019

Programs/grants: