Charting Human Gut Microbiome States with Statistical Physics

Advances in next-generation sequencing and bioinformatics have transformed our understanding of the human microbiome, revealing its critical role in maintaining human health and its complex involvement in disease states known as dysbiosis. Despite remarkable progress, significant challenges remain in accurately classifying microbial species, estimating their abundance and interpreting compositional data, particularly in distinguishing disease states from healthy microbiomes. This thesis investigates these challenges using a multidisciplinary approach that integrates environmental sequencing technologies, theoretical ecology and statistical physics. Using shotgun sequencing data, we explore the existence of macroecological laws that persist across microbial communities regardless of their environmental context. Our analyses reveal distinct patterns of species interactions and functional correlations within these communities, providing mechanistic explanations for the emergence of chronic inflammation in the human gut. Using models such as Langevin dynamics, disordered systems and random matrix theory, we analyse the emergence of species interaction networks and their impact on microbial stability and function. These models help to clarify ecological and statistical principles such as the emergence of statistical laws, environmental noise and disorder in the description of microbial dynamics. Finally, we investigate the functional organisation of the microbiome by introducing a novel meta'omic profiling tool. The proposed approach unifies the analysis of the three main 'omics (metagenomics, metatranscriptomics and metaproteomics) and introduces novel data capable of capturing fine-scale relationships in microbial communities. The preliminary results of the analysis carried out with the proposed tool confirm previous findings and open up the possibility of introducing new patterns to characterise ecosystem organisations. Overall, this research advances our knowledge of microbial ecosystems, provides new perspectives on the systemic shifts that accompany the transition from health to disease, and highlights the potential of integrating microbial ecology with statistical and theoretical models to unravel the complex dynamics of the gut microbiota. This integrated framework promises to advance our understanding of microbial community dynamics and their broad implications for human health.