Microbiota plasticity, the ability of microbial communities to adapt to changing environments, is crucial for understanding gut health. We develop a spatiotemporal model to simulate small intestinal microbiota and their interactions with host cells. We begin by constructing a community model of various microbial species using metabolic reconstructions and Flux Balance Analysis (FBA). By optimizing community growth, we investigate species interactions, applying L2-regularization and alternative objective formulations. The next phase incorporates enterocytes into the microbial community model. Utilizing metabolic reconstructions of S. thermophilus, F. prausnitzii, B. caccae, and E. rectale, we simulate interactions under conditions resembling an average Western diet. Our findings highlight significant interactions, including cross-feeding and competition among species. Finally, we expand the model into a spatiotemporal framework, simulating microbial dynamics along the small intestine. These simulations reveal how species abundance varies with distance and time, influenced by community composition and medium conditions.
Despite challenges in parameterization and validation, our model offers insights into the plasticity of small intestinal microbiota and their interactions with enterocytes, enhancing our understanding of gut microbiome dynamics.

Objective: The objective of this study was to investigate how different weight-loss interventions result in metabolic changes, at different time scales.
Methods: Mathematical models (differential equations) of energy metabolism were used to study weight loss trajectories and changes in postprandial dynamics in response to diet restriction, Roux-en-Y gastric bypass (RYGB) surgery and semaglutide (Ozempic, Wegovy) interventions. Personalized models and Virtual Patients were created and analyzed.
Results and Conclusions: Model for long-term obesity-driven development and progression of diabetes based on the 'twin cycle hypothesis' (liver cycle, pancreas cycle) expanded with inflammation contributing to glucolipotoxicity. The model identifies a window of opportunity for remission through weight loss.
RYGB surgery increases glucagon-like peptide 1 (GLP-1) and improves glucose levels, but also increases (postprandial) insulin. Strikingly, postprandial hypoglycemia is a common problem after RYGB. Model trajectory simulations suggest that interplay between changed anatomy, GLP-1 kinetics and changes in insulin sensitivity may explain the emergence of post-bariatric hypoglycemia months or years after surgery.
Both RYGB surgery and GLP-1 receptor agonism interventions weaken the appetite feedback control circuit that regulates body weight. Treatment with semaglutide not only lowers body weight, but also glucose levels, an effect that warrants further investigation for non-diabetic individuals.