BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities

Motivation: Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe–microbe stimulant or inhibitory e...

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Detalles Bibliográficos
Autores: Balzerani, F. (Francesco)|||/items/8b7a4faf-167a-4631-8dd0-676cfce9e04a, Valcárcel-García, L.V. (Luis Vitores)|||/items/2621f70f-28ce-4020-ab9c-93b38096a0ea, Larrañaga, P. (Pedro)|||/items/07ddcb09-5f31-48fb-a64c-25fa67d82e59, Bielza, C. (Concha)|||/items/89bb2f94-17e5-47c4-a6da-abb4bbc11c7e, Francino, M.P. (M. Pilar)|||/items/7da65cad-65d1-4cbc-b71c-b2362bd38f04, Rufián-Henares, J.Á. (Ángel José)|||/items/e6bc3d5e-2e59-491b-8075-528a4288a5cf, Planes-Pedreño, F.J. (Francisco Javier)|||/items/3234f2e5-4407-41e9-9058-1834965f6afa, Pérez-Burillo, S. (Sergio)|||/items/f693a845-e93f-45a7-80a5-d4e63a300f18, Blasco-Aramburu, T. (Telmo)|||/items/d5e49995-9c11-4047-8bbd-748bcf13cb06
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/70158
Acceso en línea:https://hdl.handle.net/10171/70158
Access Level:acceso abierto
Palabra clave:Gut microbial dynamics
BacArena
Microbe–microbe
Nutrient–microbe
Descripción
Sumario:Motivation: Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe–microbe stimulant or inhibitory effects or for nutrient–microbe inhibitory effects, typically observed in different compounds present in the daily diet. Results: Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe–microbe and nutrient–microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient–bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria–bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism.