Pengcheng (Patrick) Fu
College of Life Science and Biotechnology, Beijing University of Chemical Technology, China
Cyanobacteria have played an important role in the global carbon cycle and have long been studied as model organisms for photosynthesis and CO2 fixation. Recently, there is an increased interest in the use of photosynthetic microorganisms for the production of biofuels, protein and chemical products. Particular attention is given to the engineering of cyanobacteria for biofuel production, including both hydrocarbon and hydrogen fuels. In order to overcome technical challenges in this field, genetic tools and strategies for manipulation of cyanobacteria need to be optimized for industrial scale production.
Genetic engineering efforts on cyanobacteria usually involve antibiotic resistance as the selectable markers to screen for successful transformants. This has limited biotechnology applications of engineered cyanobacteria. For instance, since cyanobacteria require light for growth, many light-sensitive antibiotics are not desirable. On the other hand, antibiotic selection is dependent upon the sensitivity of the antibiotics to the host organism. It also relies on the host’s ability to produce the functional protein product of the antibiotic resistance cassette.
We have used the cyanobacterium Synechocystis PCC 6803 as the model system to create a new function to this photosynthetic organism in addition to the pre-existing functions of photosynthesis and CO2 fixation. It involved the reconstruction of genome-scale metabolic network for the systems biology prediction of cellular metabolism under different genotypes and growth conditions. For synthetic biology, heterologous “biological parts” were assembled into the Synechocystis “chassis” to create a novel ethanol producing pathway which allows the carbon flow from the CO2 to the end product, ethanol as predicted by a computer model. The integration of systems biology and synthetic biology for Synechocystis has proven to be precise, cost effective, and predictable.