SteatoNet- Modelling steatosis identifies candidate systemic flux distribution deregulations

TL;DR

SteatoNet is a comprehensive mouse-specific metabolic model that helps identify key systemic flux deregulations involved in NAFLD-related steatosis, offering insights into disease mechanisms and potential intervention points.

Contribution

This paper introduces the first multi-tissue, mouse-specific metabolic model for NAFLD, integrating hepatic pathways and systemic regulation using steady-state analysis without kinetic parameters.

Findings

Identified critical flux distribution points influencing NAFLD development

Highlighted potential systemic triggers for steatosis

Validated model suggests key network control points

Abstract

The functional diversity in factors identified in association with non-alcoholic fatty liver disease (NAFLD) necessitates the utilization of holistic approaches to investigate the network properties of NAFLD pathogenesis. We describe the generation of the first mouse-specific multi-tissue metabolic model, the SteatoNet, to investigate NAFLD-related steatosis, by utilising an object-oriented modelling approach and incorporating numerous hepatic metabolic pathways, their interaction with peripheral tissues and hierarchical feedback regulation at the transcriptional and post-translational level. The unique validated model is based on steady-state analysis of ordinary differential equations that allows investigation of systemic behaviour in the absence of estimated kinetic parameters. Model analysis indicated the importance of the metabolic flux distribution parameter in controlling metabolite concentration and identified critical focal points in the network that may play a pivotal role in initiating NAFLD. Albeit requiring experimental validation, the candidate triggers identified by SteatoNet provide insight into the network properties of NAFLD and emphasize the promising scope and potential of SteatoNet as a primary investigative tool for hypotheses generation and exploring related metabolic diseases.