Metabolic flux simulation of microbial systems based on optimal planning algorithms

The genomic scale metabolic networks of the microorganisms can be constructed based on their genome sequences, functional annotations, and biochemical reactions, reflecting almost all of the metabolic functions. Mathematical simulations of metabolic fluxes could make these functions be visualized, thereby providing guidance for rational engineering design and experimental operations. This review summarized recently developed flux simulation algorithms of microbial systems. For the single microbial systems, the optimal planning algorithm has low complexity because there is no interaction between microorganisms, and it can quickly simulate the stable metabolic states through the pseudo-steady hypothesis. Besides, the experimental conditions of single microbial systems are easier to reach or close to the optimal states of simulation, compared with polymicrobial systems. The polymicrobial culture systems could outcompete the single microbial systems as they could relieve metabolic pressure through metabolic division, resource exchange, and complex substrate co-utilization. Besides, they provide varieties of intracellular production environments, which render them the potential to achieve efficient bioproduct synthesis. However, due to the quasi-steady hypothesis that restricts the simulation of the dynamic processes of microbial interactions and the algorithm complexity, there are few researches on simulation algorithms of polymicrobial metabolic fluxes. Therefore, this review also analyzed and combed the microbial interactions based on the commonly used hypothesis of maximizing growth rates, and studied the strategies of coupling interactions with optimal planning simulations to metabolism. Finally, this review provided new insights for the genomic scale metabolic flux simulations of polymicrobial systems. • Summarized the flux simulation algorithms of single microbial and polymicrobial systems. • Analyzed and combed the microbial interactions based on the hypothesis of maximizing growth rates. • Studied the strategies of coupling microbial interactions with optimal planning simulations to metabolism. • Provided new insights for the genomic scale metabolic flux simulations of polymicrobial systems.