Bisabolane; Systems Biology; Terpene Synthesis; Metabolic Engineering; Biofuel Biosynthesis; Biosynthetic Pathways; Monte Carlo Sampling; Microbial Communities; Synthetic Biology; Flux Balance Analysis
Latif H., Szubin R., Tan J., Brunk E., Lechner A., Zengler K., Palsson B.O., A streamlined ribosome profiling protocol for the characterization of organisms, in Biotechniques
Bozkurt E, Ashari N, Browning N, Brunk E, Campomanes P, Perez MAS, Rothlisberger U., Lessons from Nature: Computational Design of Biomimetic Compounds and Processes, in Chimia
, 68, 642.
Brunk E., Rothlisberger U., Mixed Quantum Mechanical/Molecular Mechanical Molecular Dynamics Simulations of Biological Systems in Ground and Electronically Excited States, in Chemical Reviews
Guzman G, Utrilla J, Nurk S, Brunk E, Monk JM, Ebrahim A, Palsson B, Feist AM., Model-driven discovery of underground metabolic functions in Escherichia coli, in Proceedings of National Academy of Sciences
, 112(3), 929-934.
Recent developments in the genomic era has led to the development of metabolic engineering, which has a great potential to become an enabling strategy for the microbial production of biofuels. Microbe-mediated production platforms are based on natural processes to facilitate energy solutions on the molecular level that are elegantly safer, more economical and highly ef?cacious when compared to conventional methods. The ambition of this project is to use the unique richness of the Joint BioEnergy Institute (JBEI) cluster to combine two synergistic approaches, metabolic engineering and synthetic biology, namely to explore the logic that connects the emergence of biosynthetic pathways in engineered microorganisms to produce desired chemicals. We will use metabolic engineering to optimize a recently reported microbial-based biofuel production platform for the over-production of bisabolene, the immediate precursor to a novel biosynthetic alternative to D2 diesel fuel, bisabolane. We will use the methods developed in this project to gauge the effect of synthetic biology techniques on the metabolism of the hosts and predict changes to improve production of large quantities of biosynthetic bisabolene for mass production. On a fundamental level, this project aims to leverage computational metabolic engineering methods with a Monte Carlo sampling approach to predict large-scale systemic responses in engineered single-genome scale models as well as in multi-genome scale models of microbial communities. We will use multi-scale modeling approach to expand the existing metabolic network models to study different parts of metabolism at different levels of resolution, or scales, and allow ?uxes to be constrained for genome scale modeling using 13C labeling experiments data.