misosoup: a metabolic modeling tool for identifying minimal microbial communities, facilitates the exploration of microbial ecology and biotechnological applications
ABSTRACT Microbial survival and function often depend on metabolic interactions within communities. Therefore, a central question in disentangling microbial organization is determining which minimal groups of strains are able to thrive in a given medium—referred to as “minimal communities.” Answering this question is essential for understanding microbial distribution, enhancing laboratory cultivation, and designing synthetic communities (SynComs). Here, we introduce misosoup, a Python package for identifying minimal communities (minimal supplying community search). Through genome-scale constraint-based metabolic modeling, misosoup enables the systematic identification of communities that support microbial growth in environments where individual strains fail to survive alone. We validate misosoup against experimentally verified minimal communities, demonstrating its ability to predict known cooperative interactions, cocultures, and consortia with biotechnological potential. We further illustrate the use of misosoup to investigate broad microbial ecology questions by applying it to a set of 60 marine microbes, finding pervasive cross-feeding-driven niche expansion, and showing how the detailed outputs provided by misosoup facilitate research on hot topics such as the identification of functional groups. In summary, misosoup provides a powerful tool for microbial ecology and community design, with potential applications in both research and biotechnological innovation. IMPORTANCE Microbes often rely on each other to survive, especially in environments where they cannot live alone. Understanding which small groups of microbes can thrive together—called minimal communities—is key to improving laboratory research, designing synthetic ecosystems, and exploring how microbes spread in nature. To support this, we developed misosoup, a Python tool that identifies these communities using advanced metabolic modeling. misosoup helps scientists discover how microbes cooperate by sharing nutrients, a process known as metabolic cross-feeding. When tested on sets of species from different origins, the tool showed that species could thrive in more environments when part of a group. This finding highlights the importance of cooperation in microbial life. misosoup not only predicts these interactions but also provides detailed insights that can guide ecological studies and biotechnological innovation. By revealing how microbes support each other, misosoup contributes to a deeper understanding of life’s interconnectedness and offers tools for solving real-world challenges. Microbes often rely on each other to survive, especially in environments where they cannot live alone. Understanding which small groups of microbes can thrive together—called minimal communities—is key to improving laboratory research, designing synthetic ecosystems, and exploring how microbes spread in nature. To support this, we developed misosoup, a Python tool that identifies these communities using advanced metabolic modeling. misosoup helps scientists discover how microbes cooperate by sharing nutrients, a process known as metabolic cross-feeding. When tested on sets of species from different origins, the tool showed that species could thrive in more environments when part of a group. This finding highlights the importance of cooperation in microbial life. misosoup not only predicts these interactions but also provides detailed insights that can guide ecological studies and biotechnological innovation. By revealing how microbes support each other, misosoup contributes to a deeper understanding of life’s interconnectedness and offers tools for solving real-world challenges.