Hector Garcia Martin is a Berkeley Lab scientist working in the intersection of synthetic biology, machine learning, automation. 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
- Data capture, storage and visualization.
- Machine learning modeling of multiomics data.
- Mechanistic modeling of metabolism.
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.