ENTEROTYPE-LIKE MICROBIOME STRATIFICATION AS EMERGENT STRUCTURE IN COMPLEX ADAPTIVE SYSTEMS: A MATHEMATICAL MODEL

Metagenomics provides a new window into the structure and function of the human gut microbiome. Results suggest that certain properties of the adult gut microbial community are conserved across host populations and show remarkable resilience. The non-random clustering of taxa in gut microbiomes is evidence for the existence of functional networks in communities that may be involved in underlying dynamical processes giving rise to these patterns. Models for understanding the underlying structure (including enterotypes) and derived properties are in demand by researchers. We propose a simple random function system to model adaption and self-organization of the microbiome taxonomic space when fostering the optimal functioning of the system. The construction of this model is based on key facts of microbiota functioning, reported in recent studies. We aim to demonstrate the existence of a probability distribution as a microbiome attractor resulting from an intermittent adaption process. Its mathematical structural properties explain the stability of gut microbiota and its resilience to perturbation after occasional stress. The model is consistent with microbiome clustering results and provides precise mathematical meaning to reported gradients among enterotypes. The model also explains how intermittent perturbations, such as long-term dietary patterns, may affect microbiome structure; these results are consistent with reported experimental results. The mathematical facts implied by the model reveal an underlying mechanism that may explain gut microbiome structure and related experimental findings. Within this framework, stability and resilience properties of human gut microbiota are explained as a consequence of the model.

Automated summary

Metagenomics provides insights into the structure and function of the human gut microbiome, showing commonalities and resilience across host populations. The non-random clustering of taxa suggests functional networks in the microbiome community. Researchers seek models to understand the underlying structure and properties of microbiomes.