Molecular Microbiology Research 2024, Vol.14, No.1, 39-48 http://microbescipublisher.com/index.php/mmr 42 Metabolic Pathway Engineering: By modularizing and optimizing metabolic pathways, synthetic biology enables the creation of microbial consortia where each member specializes in a specific part of a metabolic process. This division of labor can lead to more efficient resource utilization and higher overall productivity. For example, the co-fermentation of different substrates by engineered microbes can significantly boost biohydrogen production (Hu et al., 2020) Cross-Feeding Interactions: In some synthetic communities, non-metabolizing members can support metabolizing ones through cross-feeding interactions. This can involve the exchange of metabolic intermediates that enhance the overall degradation or production capabilities of the community. An example is the synergistic biodegradation of n-alkanes by a consortium of Dietzia sp. and Pseudomonas stutzeri, where metabolic intermediates are exchanged to improve degradation efficiency (Hu et al., 2020). Ecological Interaction Modulation: Synthetic biology allows for the precise tuning of ecological interactions such as competition, exploitation, and mutualism. By engineering these interactions, researchers can create consortia with desired dynamic behaviors and enhanced functional outcomes. For instance, engineered E. coli strains can be programmed to exhibit different interaction modes, such as synergy or competition, depending on the initial conditions and engineered modules (Li et al., 2022). 3.3 Examples of enhanced functional outcomes Increased Metabolite Production: Synthetic microbial communities have been designed to enhance the production of valuable metabolites. For example, the modularization of metabolic pathways combined with growth-coupled selection schemes has been shown to significantly improve the bioproduction capabilities of cell factories (Figure 1) (Orsi et al., 2021). Improved Degradation of Pollutants: Engineered microbial consortia can achieve more efficient degradation of environmental pollutants. The synergistic interaction between Dietzia sp. and Pseudomonas stutzeri in n-alkane biodegradation is a prime example, where the presence of a non-degrading member enhances the overall degradation efficiency through metabolic cross-feeding (Hu et al., 2020). Figure 1 compares the classical "design-build-test-learn" (DBTL) pipeline (top) with the "growth selection-based" DBTL cycle proposed in this study (bottom) (Figure 2). Both cycles are supported by the same technologies in the "design" and "build" phases. However, the novel pipeline changes in the "test" and "learn" phases, making them faster due to the absence of -omics analyses (shorter red and purple arrows). If necessary, the new cycle can incorporate adaptive laboratory evolution within its "test" phase (longer red arrow) (Orsi et al., 2021). This improved DBTL cycle significantly accelerates the "test" and "learn" phases by reducing the reliance on -omics analyses. It allows for more flexible and adaptive experimental designs, enhancing efficiency and shortening cycle times. This new approach provides an effective pathway for rapid iteration and optimization in microbial engineering, improving the adaptability of laboratory evolution. Enhanced Biohydrogen Production: The co-fermentation of antibiotic fermentation residue and fallen leaves by a synthetic microbial community has been shown to significantly increase biohydrogen production. This enhancement is attributed to the synergistic effects of microbial activity, enriched hydrogen-producing bacteria, and the expression of key functional genes (Yang and Wang, 2021). By leveraging these mechanisms and examples, synthetic microbial communities can be strategically designed to achieve enhanced functional synergy, leading to more efficient and robust biotechnological applications. 4 Applications and Implications 4.1 Industrial biotechnology Synthetic microbial communities have shown significant potential in various industrial biotechnology applications, including biofuel production and bioremediation. The design of these communities often involves selecting appropriate microbial partners to optimize metabolic interactions and enhance overall productivity. For instance,
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