A computational framework for inferring species dynamics and interactions with applications in microbiota ecology
Abstract We present MBPert, a generic computational framework for inferring species interactions and predicting dynamics in time-evolving ecosystems from perturbation and time-series data. In this work, we contextualize the framework in microbial ecosystem modeling by coupling a modified generalized...
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| Format: | Article |
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Nature Portfolio
2025-08-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00568-0 |
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| author | Yuanwei Xu Georgios V. Gkoutos |
| author_facet | Yuanwei Xu Georgios V. Gkoutos |
| author_sort | Yuanwei Xu |
| collection | DOAJ |
| description | Abstract We present MBPert, a generic computational framework for inferring species interactions and predicting dynamics in time-evolving ecosystems from perturbation and time-series data. In this work, we contextualize the framework in microbial ecosystem modeling by coupling a modified generalized Lotka-Volterra formulation with machine learning optimization. Unlike traditional methods that rely on gradient matching, MBPert leverages numerical solutions of differential equations and iterative parameter estimation to robustly capture microbial dynamics. The framework is assessed within the context of two experimental scenarios: (i) paired before-and-after measurements under targeted perturbations, and (ii) longitudinal time-series data with time-dependent perturbations. Extensive simulation studies, benchmarking on standardized MTIST datasets, and application to Clostridium difficile infection in mice and repeated antibiotic perturbations of human gut micribiota, demonstrate that MBPert accurately recapitulates species interactions and predicts system dynamics. Our results highlight MBPert as a powerful and flexible tool for mechanistic insight into microbiota ecology, with broad potential applicability to other complex dynamical systems. |
| format | Article |
| id | doaj-art-2ea08d4d54194ab589d08c6c5148e8d9 |
| institution | Kabale University |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-2ea08d4d54194ab589d08c6c5148e8d92025-08-20T03:42:53ZengNature Portfolionpj Systems Biology and Applications2056-71892025-08-0111111310.1038/s41540-025-00568-0A computational framework for inferring species dynamics and interactions with applications in microbiota ecologyYuanwei Xu0Georgios V. Gkoutos1Department of Cancer and Genomic Sciences, College of Medicine and Health, University of BirminghamDepartment of Cancer and Genomic Sciences, College of Medicine and Health, University of BirminghamAbstract We present MBPert, a generic computational framework for inferring species interactions and predicting dynamics in time-evolving ecosystems from perturbation and time-series data. In this work, we contextualize the framework in microbial ecosystem modeling by coupling a modified generalized Lotka-Volterra formulation with machine learning optimization. Unlike traditional methods that rely on gradient matching, MBPert leverages numerical solutions of differential equations and iterative parameter estimation to robustly capture microbial dynamics. The framework is assessed within the context of two experimental scenarios: (i) paired before-and-after measurements under targeted perturbations, and (ii) longitudinal time-series data with time-dependent perturbations. Extensive simulation studies, benchmarking on standardized MTIST datasets, and application to Clostridium difficile infection in mice and repeated antibiotic perturbations of human gut micribiota, demonstrate that MBPert accurately recapitulates species interactions and predicts system dynamics. Our results highlight MBPert as a powerful and flexible tool for mechanistic insight into microbiota ecology, with broad potential applicability to other complex dynamical systems.https://doi.org/10.1038/s41540-025-00568-0 |
| spellingShingle | Yuanwei Xu Georgios V. Gkoutos A computational framework for inferring species dynamics and interactions with applications in microbiota ecology npj Systems Biology and Applications |
| title | A computational framework for inferring species dynamics and interactions with applications in microbiota ecology |
| title_full | A computational framework for inferring species dynamics and interactions with applications in microbiota ecology |
| title_fullStr | A computational framework for inferring species dynamics and interactions with applications in microbiota ecology |
| title_full_unstemmed | A computational framework for inferring species dynamics and interactions with applications in microbiota ecology |
| title_short | A computational framework for inferring species dynamics and interactions with applications in microbiota ecology |
| title_sort | computational framework for inferring species dynamics and interactions with applications in microbiota ecology |
| url | https://doi.org/10.1038/s41540-025-00568-0 |
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