Modeling human gut microbiota: from steady states to dynamic systems

Sammanfattning: Human gut microbes are an essential part of human sub-microscopic systems and involved in many critical biological processes such as Type 2 diabetes (T2D) and osteoporosis. However, the underlying mechanisms are unclear. Several mathematical modeling approaches, such as genome-scale metabolic models (GEMs) and ordinary differential equation (ODE) based models, have been used to simulate the dynamics of human gut microbiota. This thesis aims to explore, simulate, and predict the behavior of gut microbial ecosystems and the relationships between gut microbes and humans by modeling. The importance of the gut microbiome for bone metabolism and T2D has been demonstrated in mice and human cohorts. We first reconstructed a GEM for Limosilactobacillus reuteri ATCC PTA 6475, which is a probiotic that significantly reduces bone loss in older women with lower bone mineral density. To investigate the associations between T2D and the gut microbiota, GEMs for 827 gut microbial species and 1,779 community-level GEMs for T2D cohorts have also been constructed. With these GEMs, we investigated metabolic potentials such as short-chain fatty acids, amino acids, and vitamins that play vital roles in the host metabolism regulation. Furthermore, the integration of the models with machine learning method provides potential insights into the possible roles of gut microbiota in T2D. Cybernetic models, which simulate metabolic rates by integrating the control of enzyme synthesis and enzyme activities, have been applied to explore the dynamic behaviors of small-size metabolic networks. However, only a few studies have applied cybernetic theory to the microbial community so far. The remaining part of this thesis focuses on the use of cybernetic models to explore human gut microbiota's interactions and population dynamics. Considering the high computing burden of the current cybernetic modeling approach for processing the full-size GEMs, we have developed a computing-efficient strategy for model reconstruction and simulation to reveal the metabolic dynamics of human gut microbiota. In this thesis, we explore the human gut microbiota from single L. reuteri species to microbial gut communities, from simple steady state systems by GEMs to complex dynamic systems by cybernetic model.