Applications of genome-scale metabolic network model in metabolic engineering

Abstract

Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes

“…The existing body of work evaluating different E. coli strains in metabolic engineering and synthetic biology (Archer et al, 2011, Arifin et al, 2014, Yoon et al, 2012, Vijayendran et al, 2007, Marisch et al, 2013, Chae et al, 2010) demonstrated a need for the comprehensive analysis of strain-specific differences. Despite significant success in engineering E. coli for industrial production of chemicals and proteins (Lee et al, 2012b, Kim et al, 2015), there is no unified fundamental basis for selection of one strain over another for a given metabolic engineering project or expression of a given construct. Previous studies have shown that the choice of host strain for production of a given compound has a significant impact on results (Na et al, 2013, Kim et al, 2014) and up until now represented a major brute force screening effort.…”

IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models

“…It can help to identify targets that couple cell growth with product formation, e.g., targets for gene upregulation, downregulation, and gene deletion 34, 35 . In IMGMD, a library that combines in vivo and in silico results to guide metabolic engineering was created.…”

Thermodynamics-based design of microbial cell factories for anaerobic product formation

“…Recently, some stoichiometric models covering the full genome have been used to design microbial cell factories [53,54]. In this case thermodynamic constraints have played an important role narrowing the number of feasible pathways [55][56][57][58].…”