The article "Deciphering and Designing Microbial Communities by Genome-Scale Metabolic Modelling" by Shengbo Wu et al. provides a comprehensive review of the advancements in understanding and designing microbial communities using genome-scale metabolic models (GEMs). The authors highlight the complexity and ecological properties of microbial communities, emphasizing the need for GEMs to capture intricate interactions among organisms and the environment. They outline a framework for constructing GEMs of microbial communities, focusing on the role of external environmental and intracellular resources in shaping community assembly. The review also discusses the strengths and weaknesses of different GEM reconstruction methodologies, including static and dynamic models. Additionally, the article explores the integration of multi-omics data and machine learning algorithms to enhance the accuracy and interpretability of GEMs. Finally, the authors discuss key challenges and future directions, such as the integration of quorum sensing mechanisms and the development of automatic modeling tools, to advance the field of synthetic biology and improve the design of microbial consortia for various applications.The article "Deciphering and Designing Microbial Communities by Genome-Scale Metabolic Modelling" by Shengbo Wu et al. provides a comprehensive review of the advancements in understanding and designing microbial communities using genome-scale metabolic models (GEMs). The authors highlight the complexity and ecological properties of microbial communities, emphasizing the need for GEMs to capture intricate interactions among organisms and the environment. They outline a framework for constructing GEMs of microbial communities, focusing on the role of external environmental and intracellular resources in shaping community assembly. The review also discusses the strengths and weaknesses of different GEM reconstruction methodologies, including static and dynamic models. Additionally, the article explores the integration of multi-omics data and machine learning algorithms to enhance the accuracy and interpretability of GEMs. Finally, the authors discuss key challenges and future directions, such as the integration of quorum sensing mechanisms and the development of automatic modeling tools, to advance the field of synthetic biology and improve the design of microbial consortia for various applications.