Héctor García Martin

Staff Scientist

Process Engineering & Analytics

Secondary Affiliation: Environmental Genomics and Systems Biology

Biosystems Data Science

Building: 978, Room 4244
Mail Stop: 978-4121
Phone: (510) 495-2505
Fax: (510) 486-4252
hgmartin@lbl.gov
https://hectorgarciamartin.com/
https://qmm.lbl.gov/

Curriculum Vitae

Research Interests

My research interests focus on mathematical modeling of biological systems: from synthetic biology, machine learning, systems biology, metabolic flux analysis, data visualization, scientific software development, ecology, to automation and complexity

My research is divided among machine learning, metabolic flux analysis, retrobiosynthesis, automation and software development for visualization and acquisition of data. I am also using mathematical approaches to try and develop quantitative predictive models for microbial communities. You can find more detailed information on my research interests here and the full story of how a physicist ended up working in biology here.

Selected Publications

LinkedIn Profile
ResearchGate Profile

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Machine Learning Takes on Synthetic Biology: Algorithms Can Bioengineer Cells for You

September 25, 2020

Engineering biological systems to specification–for example, designing a microbe to produce a cancer-fighting agent–requires a detailed mechanistic understanding of how all the parts of a cell work. Typically, this knowledge is acquired through years of painstaking work and a fair amount of trial and error. But Berkeley Lab scientists have created an Automated Recommendation Tool (ART) that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. With a limited set of training data, the algorithms are able to predict how changes in a cell’s DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal. The work was led by Hector Garcia Martin, a researcher in Berkeley Lab’s Biological Systems and Engineering (BSE) Division and Tijana Radivojevic, a BSE data scientist. In a pair of papers recently published in the journal Nature Communications, they presented the algorithm and demonstrated its capabilities.

Read more in the Berkeley Lab News Center.

More Investment Needed for Machine Learning for Bioengineering

July 22, 2019

In an opinion piece published July 19 in ACS Synthetic Biology, Hector Garcia Martin and Tijana Radivojevic of the Biosciences Area’s Biological Systems & Engineering Division collaborated with Pablo Carbonell of the Manchester Institute of Biotechnology’s SynBioChem Centre, to highlight the opportunities in a radical new approach to bioengineering that leverages the latest disruptive advances in machine learning.

New Computational Biosciences Group Formed

March 12, 2019

Researchers from Biosciences and the Computational Research Division (CRD) have formed a new integrated Computational Biosciences Group to develop tools for addressing a range of scientific problems that cross organizational lines. Members of the group include (pictured, from left): Héctor García Martin of the Biological Systems and Engineering and the Environmental Genomics and Systems Biology Divisions (BSE/EGSB), acting group lead Kris Bouchard (BSE), Chris Mungall (EGSB), Andrew Tritt of the Computational Research Division(CRD), Oliver Rübel (CRD), and Ben Brown (EGSB). Additional members not pictured are: Aydın Buluç (CRD), Silvia Crivelli (CRD), Hans Johansen (CRD), Talita Perciano (CRD), and Peter Zwart of the Molecular Biophysics and Integrated Bioimaging Division (MBIB).

Read more on the Computing Sciences website.