Understanding the dynamics of human liver metabolism is fundamental for effective diagnosis and treatment of liver diseases in general and the metabolism of drugs in particular. This knowledge can be obtained with systems biology/medicine approaches that account for the complexity of hepatic responses and their systemic consequences in other organs. Computational modelling can reveal hidden principles of the system by classification of individual components, analysing their interactions and simulating the effects that are difficult to investigate experimentally. Herein we review the state-of-the-art computational models that describe liver dynamics from the metabolic, gene regulatory and signal transduction perspectives. We focus especially on large-scale liver models described either by genome scale metabolic networks (GSMN) or object-oriented approach. We also discuss the benefits and limitations of each modelling approach and their value for clinical applications in diagnosis, therapy and prevention of liver diseases as well as precision medicine in hepatology. This article is protected by copyright. All rights reserved.