Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data.

Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations.

IdealKnock: A framework for efficiently identifying knockout strategies leading to targeted overproduction.

In recent years, computer aided redesigning methods based on genome-scale metabolic network models (GEMs) have played important roles in metabolic engineering studies; however, most of these methods are hindered by intractable computing times. In particular, methods that predict knockout strategies leading to overproduction of desired biochemical are generally unable to do high level prediction because the computational time will increase exponentially. In this study, we propose a new framework named IdealKnock, which is able to efficiently evaluate potentials of the production for different biochemical in a system by merely knocking out pathways.

Metabolome analysis and pathway abundance profiling of Yarrowia lipolytica cultivated on different carbon sources.

Yarrowia lipolytica, a model microorganism of oleaginous yeasts with developed sophisticated genetic tools, is able to metabolize a wide range of substrates and accumulate large amounts of lipids. However, there is a lack of literature reporting the metabolic characteristics of Y. lipolytica metabolizing these substrates in a systematic view.