Mathematical modeling of steatotic liver cell metabolic network

Metabolic dysfunction–associated steatotic liver disease (MASLD), previously known as non alcoholic fatty liver disease (NAFLD), is a chronic liver disease characterized by excessive fat accumulation in the hepatocytes, leading to liver steatosis and potential progression to more severe liver conditions, annually responsible of 1 out of every 25 deaths worldwide. Due to the lack of pharmacological and targeted treatments, this study aims to build a liver numerical digital twin, reproducing the kinetic model of hepatic lipid droplet metabolism, encompassing FFA uptake, TAG esterification, and lipid droplet dynamics, based on experimental in vitro and in vivo findings. By simulating lipid droplets size distributions in hepatocytes under varying conditions, one of the goals is to highlights the role of regulatory surface proteins (RSPs) in cellular lipid accumulation. Additionally, a novel approach is employed by utilizing sensitivity analysis methods to identify the most significant input parameters. These parameters are then used as key features in the integration of a neural network, enhancing the numerical solution of the model’s ordinary differential equations (ODEs) and potentially improving both predictive accuracy and computational effciency in modeling hepatocyte lipid content.