2025-04-28 カリフォルニア大学バークレー校
<関連情報>
- https://engineering.berkeley.edu/news/2025/04/the-not-so-secret-life-of-gut-bacteria/
- https://journals.asm.org/doi/10.1128/msystems.01652-24
MetaBiome:エージェントベースと代謝ネットワークを統合したマルチスケールモデルにより、腸粘膜微生物群集の空間的制御を明らかにする MetaBiome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities
Javad Aminian-Dehkordi, Andrew Dickson, Amin Valiei, Mohammad R. K. Mofrad
mSystems Published:4 April 2025
DOI:https://doi.org/10.1128/msystems.01652-24
ABSTRACT
Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we present MetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using our in-silico platform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases. MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities.
