The traditional biomanufacturing industry using non-renewable fossil resources as raw materials is facing severe challenges of high energy consumption, high emissions and high pollution, and it is urgent to achieve sustainable development in the future through green transformation of raw materials and production processes (Cai et al., 2019). Biomanufacturing can utilize biological systems to convert renewable biomass such as natural materials and even waste and CO2 into value-added chemicals and biopolymers, thereby reducing the need for petrochemicals and pollutant emissions (Kim et al., 2015; Shi et al., 2023). In addition, there are other inherent advantages to using biomanufacturing technologies to produce chemicals, including mild reaction conditions, no expensive catalysts and few toxic by-products (Zhang et al., 2017b). Biomanufacturing is therefore a promising approach to building a green and sustainable economy and society (Clomburg et al., 2017).

One of the central aspect of biomanufacturing is the design and construction of efficient MCFs (Shi et al., 2023). Metabolic engineering utilizes recombinant DNA technology and other molecular biological techniques to purposefully modify and engineer the genetic circuits of microorganisms (Eggleston, 1991; Steinbüchel, 2001). The use of metabolic engineering can optimize existing biochemical reactions and metabolic pathways, introduce exogenous metabolic pathways, or even create metabolic pathways that do not exist in nature to reshape microbial metabolic pathways to increase cellular biomass or target metabolite production, thereby developing microbial cell factories with specific functions. Currently, different MCFs have been developed through metabolic engineering, and the products produced by them, such as cells, enzyme preparations, chemical monomers and polymers, have played an important role in promoting the economic development of various countries (Gong et al., 2017; Gustavsson and Lee, 2016; Zhao et al., 2022). In metabolic engineering, selected genes in metabolic pathways are intentionally amplified or deleted with consideration of metabolic network for strain improvement. However, the metabolism and regulation of microorganisms is a nonlinear complex network system, and cells rely on their exquisite gene circuit and strict regulatory mechanisms to maintain the stability of various metabolic activities. Due to the lack of overall understanding of the systematic biological correlation between microbial genotypes and phenotypes, this kind of local metabolic modification (only modifying a certain part of the metabolism) often fails to achieve the desired outcomes, while resulting in a waste of time, manpower, and economy, which is no longer in line with the requirements of the current development of industrial biotechnology (Park et al., 2009). Therefore, a comprehensive and detailed understanding of metabolic networks and determining their flux distribution is essential for guiding and developing metabolic engineering.

Genome-scale metabolic network model (GSM) is mathematical model reconstructed by extensively collecting and organizing biological information on gene annotation and functions, metabolites, metabolic reactions, enzymes and their interactions within a given organism. It describes a complete set of stoichiometry-based, mass-balanced metabolic reactions in an organism base on gene-protein-reaction (GPR) (Ye et al., 2022). Importantly, GSM is also a powerful platform for integrating multiple biological constraints, such as transcriptomic, proteomic, and thermodynamic as well as various algorithms, to systematically and widely simulate cellular metabolism and perturbation. It can reflect our understanding of microbial metabolic signatures in a comprehensive and standardized manner and has become an important tool for systematically understanding and modifying microorganisms (Park et al., 2009). As the continuous development of genome sequencing and relevant omics analyses technologies, the reconstruction quality and application of GSMs have also expanded accordingly, which helps us to better understand the metabolism of various organisms. As a result, since the construction of the first GSM in 2000, more than 100 microbial GSMs have been reconstructed, many of which are industrial microbial GSMs such as E. coli, S. cerevisiae and C. glutamicum (Lopes and Rocha, 2017). GSMs of these industrial microorganisms have shown great promise in guiding metabolic engineering for producting industrial products such as ethanol (Liu et al., 2012), L-proline (Zhang et al., 2017a) and α-aminoadipate (Zhang et al., 2023).

In this review, we summarized latest developments and current status of important industrial microorganisms GSMs and highlighted some of the new technologies and approaches that drive the construction of high-quality GSMs. In addition, we also summarized the major and recent applications of GSMs in guiding metabolic engineering, and provided an outlook on the future development of GSMs.