Biotechnologists have been working hard to address the climate change and limited fossil resource issues through the development of sustainable processes for the production of chemicals, fuels and materials from renewable non-food biomass. One promising sustainable technology is the use of microbial cell factories for the efficient production of desired chemicals and materials. When microorganisms are isolated from nature, the performance in producing our desired product is rather poor. Metabolic engineering is performed to improve the metabolic and cellular characteristics to achieve enhanced production of desired product at high yield and productivity. Since the performance of microbial cell factory is very important in lowering the overall production cost of the bioprocess, many different strategies and tools have been developed for the metabolic engineering of microorganisms.
One of the big challenges in metabolic engineering is to find the best platform organism and to find those genes to be engineered so as to maximize the production efficiency of the desired chemical. Even Escherichia coli, the most widely utilized simple microorganism, has thousands of genes, the expression of which is highly regulated and interconnected to finely control cellular and metabolic activities. Thus, the complexity of cellular genetic interactions is beyond our intuition and thus it is very difficult to find effective target genes to engineer. Together with gene amplification strategy, gene knockout strategy has been an essential tool in metabolic engineering to redirect the pathway fluxes toward our desired product formation. However, experiment to engineer many genes can be rather difficult due to the time and effort required; for example, gene deletion experiment can take a few weeks depending on the microorganisms. Furthermore, as certain genes are essential or play important roles for the survival of a microorganism, gene knockout experiments cannot be p
|Contact: Lan Yoon|
The Korea Advanced Institute of Science and Technology (KAIST)