Presentation Information
[SS23-01]Functional reduction of microbial communities via metabolic trait modeling
*Risa Sasaki1, Mayumi Seto1 (1. Nara Women’s University (Japan))
Keywords:
microbial community interaction,metabolic function,population dynamics,trait-based modeling
In general, population dynamics models describe interactions among populations at the species level. However, in the case of microorganisms, the concept of species as an evolutionary unit may not always be suitable for representing units of interaction. Moreover, because of their phenotypic plasticity, even the same species can exhibit different functions depending on environmental conditions, thereby altering their relationship with others. Consequently, redefining the “grouping” of microorganisms based on their fundamental interactions may help in understanding the complex microbial community dynamics sometimes referred to as dark matter. Microbial interactions are often indirect, occur through the exchange of metabolic handoffs, rather than the direct relationships seen in predator and prey or host and parasite systems. In other words, microorganisms that share similar metabolic functions can be regarded as a group exploiting the same niche, and thus classifying them according to the similarity of their metabolic functions is a valid approach from theoretical ecological perspectives. In this presentation, we propose a method for constructing functional groups by focusing on the similarity of central metabolic functions, specifically those involved in energy metabolism and biomass synthesis, that underpin microbial growth. The interactions among these functional groups can be explicitly represented by describing the exchange of materials according to their functions. This approach directly links the chemical reactions in the environment with the functional groups, and is therefore expected to advance the development of quantitative theoretical ecological research that integrates ecology with geosciences and environmental issues.