[4S01m] 09. GteX - Part 4

10:00 AM - 10:30 AM JST(1:00 AM - 1:30 AM UTC)

[4GteX-01-KL] Individual-Based Simulations to Investigate the Dynamics of Microbial Interactions

○Kohei Takahashi¹ (1. Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology (Eawag) (Switzerland))

Keywords:

Individual-Based Simulation,Microbial interaction,Conjugation,Microscopic Image

Microorganisms in natural and engineered environments exist not as isolated entities, but as components of highly interactive networks. Microbial communities, in particular, function as integrated systems in which cell–cell interactions determine material production, nutrient cycling, and ecosystem stability. To achieve GX goals, it is essential to move beyond single-strain optimization and instead understand and engineer interaction mechanisms at the community level. In this presentation, I introduce an interaction-centered framework that integrates experimental environmental microbiology with individual-based computational modeling. The individual-based agent model enables direct simulation of cell growth, spatial competition, and probabilistic cell-to-cell physical interaction such as conjugation, allowing us to dissect interaction rules that cannot be fully resolved experimentally. As an example, I demonstrate how microbial interactions shape plasmid conjugation dynamics in environmental communities. Conjugative plasmids mediate horizontal gene transfer among diverse taxa, thereby restructuring ecological fitness and antibiotic resistance within communities. By combining fluorescence-based conjugation assays and individual-based simulations, we evaluated how recipient traits, spatial structure, and growth heterogeneity govern realized interaction outcomes. Our results demonstrate that interaction outcomes are emergent properties determined not only by intrinsic genetic compatibility but also by spatial organization and community-level trait distributions. This systems-level perspective provides a foundation for the controlling of microbial interactions. This work highlights how computationally informed microbial interaction engineering can contribute to next-generation biotechnological solutions for GX.