Bacterial communities are ubiquitous entities found across diverse environments. Due to their potential applications and ecological significance such as organic matter decomposition and nitrogen fixation, research focusing on bacterial communities has been increasing, with one major research theme being the elucidation of processes that drive community transition. In this context, the presenter has previously investigated the reproducibility of community transitions by repeatedly culturing and observing environmental bacterial communities in the laboratory environment, thereby clarifying the balance between deterministic and stochastic processes within these communities.

While directly using environmental bacterial communities allows for the simple recreation of diverse communities in laboratory settings and this offers a top-down approach to hypothesis exploration, it presents challenges for bottom-up empirical observation. Specifically, it is difficult to precisely control the inoculation density of individual species, posing experimental limitations.

To overcome these challenges, it is essential to develop a synthetic bacterial community system that meets several criteria: the ability to conduct repeated observations, the inclusion of multiple species (>5 species), and the capacity to quantify the absolute abundance of each bacterium within the community.

In this presentation, the newly developed synthetic bacterial community system and the results obtained from it will be introduced. Through a preliminary experiment combining six standard bacterial strains with known genomes, we successfully constructed a system that meets the aforementioned criteria. Moreover, by applying the experimental data to the Lotka-Volterra competition equations, we not only estimated the competition coefficients between species but also achieved accurate predictions of community transitions when all six species were combined, based on results from two- and three-species mixtures.

We believe that integrating this bottom-up community experimental system, which enables a detailed understanding of internal processes, with the results of previous top-down exploratory studies will allow for robust validation of existing hypotheses. Furthermore, the proposed method offers the flexibility to increase both the number of conditions and species, making it highly adaptable and promising for future research and data acquisition.