Héctor García Martin

Lawrence Berkeley National Laboratory

Research Focus

Hector Garcia Martin is a Berkeley Lab scientist working in the intersection of synthetic biologymachine learningautomation. He received his Ph. D. in theoretical physics from the University of Illinois at Urbana-Champaign under the direction of Nigel Goldenfeld. At Berkeley lab, he leads the quantitative metabolic modeling group and develops methods to make bioengineering a predictable discipline. He has developed machine learning algorithms as well as mechanistic models to systematically guide the bioengineering process. 

Projects in the Agile BioFoundry

Featured publications

Radivojević, Tijana, et al. “A machine learning Automated Recommendation Tool for synthetic biology.” Nature Communications 11.1 (2020): 1-14.

Zhang, Jie, et al. “Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.” Nature Communications 11.1 (2020): 1-13.

Costello, Zak, and Hector Garcia Martin. “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.” NPJ Systems Biology and Applications 4.1 (2018): 1-14.

Lawson, Chris, et al. “Machine learning for metabolic engineering: A review.” Metabolic Engineering (2020).

Warnecke, Falk, et al. “Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite.” Nature 450.7169 (2007): 560-565.

Martín, Héctor García, et al. “Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities.” Nature Biotechnology 24.10 (2006): 1263-1269.

Martín, Héctor García, and Nigel Goldenfeld. “On the origin and robustness of power-law species–area relationships in ecology.” Proceedings of the National Academy of Sciences 103.27 (2006): 10310-10315.