This article presents a framework for engineering natural microbiomes to enhance bioremediation through a combination of top-down and bottom-up strategies. The study demonstrates that applying herbicides and inoculating with herbicide-degrading bacteria can drive the convergence of natural microbiomes toward functional microbiomes with enhanced bioremediation capabilities. A new microbiome modeling framework called SuperCC is developed to simulate the performance of different microbiomes and identify key metabolic interactions. Using SuperCC, the researchers constructed synthetic microbiomes based on 18 keystone species identified from natural microbiomes. The results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes. Microbiomes are ubiquitous in nature and play important roles in biogeochemical cycles, agriculture, food fermentation, element cycling, biofuels, and pollutant degradation. Synthetic microbiomes, which are based on interactions within microbiomes, can perform more complex tasks with higher efficiency than single strains or natural microbiomes. These synthetic microbiomes offer a new strategy to restore complex metabolic functions by combining the metabolic capacities of multiple strains, which can help overcome the limitations of single strains. Additionally, synthetic microbiomes provide a viable option for sharing unwanted metabolic burdens among strains in a community. The study explores the dynamic process of microbiome reassembly driven by herbicide and inoculum applications. Metagenomic analyses reveal functional differences among treated microbiomes, with the treated microbiomes clustering together and showing increased abundance of genes involved in pollutant degradation. The identification of keystone species, which are affected by treatments, provides an easy way to construct simplified functional microbiomes. The researchers identified 18 key species that can be used to construct synthetic microbiomes. The study also explores interspecies interactions in inoculated synergistic and competitive consortia. The results show that metabolic interactions between strains can enhance community growth and pollutant degradation. The researchers used computational modeling to predict and validate these interactions, demonstrating the potential of metabolic modeling in exploring mutualism in natural environments. The study highlights the importance of metabolic interactions in shaping microbiome functions and provides practical guidance for engineering natural microbiomes. The results suggest that metabolic modeling can be used to explore microbial mutualism in natural environments and improve bioremediation capabilities.This article presents a framework for engineering natural microbiomes to enhance bioremediation through a combination of top-down and bottom-up strategies. The study demonstrates that applying herbicides and inoculating with herbicide-degrading bacteria can drive the convergence of natural microbiomes toward functional microbiomes with enhanced bioremediation capabilities. A new microbiome modeling framework called SuperCC is developed to simulate the performance of different microbiomes and identify key metabolic interactions. Using SuperCC, the researchers constructed synthetic microbiomes based on 18 keystone species identified from natural microbiomes. The results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes. Microbiomes are ubiquitous in nature and play important roles in biogeochemical cycles, agriculture, food fermentation, element cycling, biofuels, and pollutant degradation. Synthetic microbiomes, which are based on interactions within microbiomes, can perform more complex tasks with higher efficiency than single strains or natural microbiomes. These synthetic microbiomes offer a new strategy to restore complex metabolic functions by combining the metabolic capacities of multiple strains, which can help overcome the limitations of single strains. Additionally, synthetic microbiomes provide a viable option for sharing unwanted metabolic burdens among strains in a community. The study explores the dynamic process of microbiome reassembly driven by herbicide and inoculum applications. Metagenomic analyses reveal functional differences among treated microbiomes, with the treated microbiomes clustering together and showing increased abundance of genes involved in pollutant degradation. The identification of keystone species, which are affected by treatments, provides an easy way to construct simplified functional microbiomes. The researchers identified 18 key species that can be used to construct synthetic microbiomes. The study also explores interspecies interactions in inoculated synergistic and competitive consortia. The results show that metabolic interactions between strains can enhance community growth and pollutant degradation. The researchers used computational modeling to predict and validate these interactions, demonstrating the potential of metabolic modeling in exploring mutualism in natural environments. The study highlights the importance of metabolic interactions in shaping microbiome functions and provides practical guidance for engineering natural microbiomes. The results suggest that metabolic modeling can be used to explore microbial mutualism in natural environments and improve bioremediation capabilities.