Integrating cellular metabolism into a multiscale whole-body model

Understanding how cells process nutrients and external compounds is central to human physiology. Prior research has shown that metabolic networks are essential for catalytic conversions within cells. However, the full physiological role of these networks remains unclear without a whole-body perspective. No prior work had resolved how individual cell metabolism interacts with tissue and organism-level processes. This gap motivated the development of models that integrate cellular and systemic scales. Existing models often focus on isolated cell types or broad physiological systems. That uncertainty drove the need for a unified framework. The challenge lies in linking metabolic states across multiple biological scales. This paper's contribution is a novel approach for integrating these scales dynamically.

The authors aimed to develop a method for integrating cellular metabolism into whole-body models. They sought to bridge the gap between single-cell metabolic networks and systemic physiology. The motivation was to better understand how cellular processes influence organism-wide outcomes. This approach could help explain how drugs affect tissues and organs. The study focused on liver metabolism due to its central role in detoxification and drug processing. The goal was to create a model that captures interactions across multiple biological scales. The researchers wanted to test this model on specific medical conditions and drug effects. By doing so, they hoped to provide new insights into disease mechanisms and therapeutic strategies.

The researchers used dynamic flux balance analysis to integrate cellular networks into whole-body models. They selected a genome-scale reconstruction of a human hepatocyte's metabolism. This model was embedded into a physiologically-based pharmacokinetic framework for the liver. The PBPK model represents standard physiological parameters and tissue interactions. The integration allowed tracking of metabolite fluxes across cellular and organ levels. The model was tested using three scenarios: hyperuricemia therapy, ammonia detoxification, and paracetamol toxicity. Each scenario involved simulating metabolic responses under different conditions. The approach enabled the simultaneous analysis of multiple biological layers and their interactions.

The multiscale model successfully integrated hepatocyte metabolism into a whole-body framework. Simulations showed how metabolic states in liver cells influence systemic outcomes. The model revealed detailed interactions between cellular and tissue-level processes. For hyperuricemia therapy, the model identified key metabolic pathways involved in uric acid regulation. In ammonia detoxification, the model highlighted the role of liver enzymes in clearing ammonia. Paracetamol-induced toxicity simulations showed how drug metabolism leads to toxic byproducts. The model captured the dynamic fluxes of metabolites across different biological scales. These findings suggest that the approach can provide mechanistic insights into disease and drug effects.

The authors propose that their approach offers a new way to study metabolism at multiple scales. They suggest that integrating cellular networks into whole-body models can enhance understanding of physiological processes. The model's ability to simulate liver metabolism supports its use in drug development and diagnostics. The findings may help explain how cellular changes contribute to systemic diseases. The approach provides a framework for testing therapeutic interventions in silico. The results suggest that multiscale modeling can reveal previously unknown interactions. The authors propose that this method could support future research in systems medicine. They suggest that the model's flexibility allows for expansion to other tissues and conditions.