Background: Based on two major signaling pathways, EGF (Epidermic Growth Factor) and TGF-b (Transforming Growth Factor), altered during tumor progression of hepatocellular carcinoma, we propose to combine biological and computational analysis to decipher complexity of the tumor progression and identify new targets aimed to inhibit, diminish or reverse the tumoral phenotype. This project lies within the scope of Systems biology to shift towards a more global picture of the cellular mechanisms than with the gene-based approaches for instance and this in order to understand how cellular components work together as a connected system. Methods and Results: By using experimental data and online public databases, we develop an integrative model for cell cycle and signaling pathways. We propose discrete multi-clock models to characterize the influence of the EGF and TGF-b pathways in controlling cell proliferation and consequently tumor progression in the liver. Discrete models are especially suitable for qualitative biological data, which constitute most of the regulatory interaction data available. As each signal within a pathway follows its own clock, we now introduce multi-clock technique to model the dynamic of the biological interactions. We validated our approach with published cell cycle models and we evaluate the robustness of hepatocellular carcinoma cell line by using data from RNA interference experiments to constraint our model. This might help identify pathway checkpoints and buffering effects between different paths of the EGF and TGF-b pathways network, allowing to design news markers and new therapeutically targets for hepatocellular carcinomas.