This paper argues that the human microbiome cannot be understood through static snapshots of composition alone, but must be interpreted as a dynamic subsystem coupled to immune, metabolic, and neuroendocrine networks. Using the Universal Resonance Model (URM) as a conceptual frame, it reframes microbiome research from lists of taxa and group comparisons to trajectories, stability, recovery, and proximity to transition. The microbiome is presented not as a direct cause of disease, but as a modifier of system dynamics—shaping baselines, buffering capacity, and vulnerability to collapse. Through theoretical analysis and testable predictions, the paper shows how microbial dynamics, rather than composition, may better explain why disease begins when it does, why recovery varies, and why similar profiles lead to different outcomes.
Anita Domargård (Wed,) studied this question.