Network models are a common and powerful formalism for studying cell functioning and its dysfunction during cancer, taking into account the interconnections between a large number of molecular entities. However, the analysis of these large networks remains a challenge. Notably, not many integrative studies include phosphoproteomics data, an important layer of information for understanding signaling pathways, due to the challenges in analyzing phosphoproteome data. In Medulloblastoma (MB), the most common malignant pediatric brain tumor, we have revealed that key signaling pathways are controlled at the post-transcriptional level in two out of four disease subgroups. Thus, we propose here a novel integrative and modeling strategy to accurately include information about phosphoproteome and its potential functional consequences to understand better the disease. Two main computational approaches will be explored: (i) stochastic blockmodeling (SBM) of multipartite networks and (ii) dynamical simulation of multi-layer networks. First, we propose to use SBM algorithms developed for multipartite graphs to identify clusters of patients based on their connection in multiple molecular networks (proteomics, phosphoproteomics and transcriptomics). The output of the SBMs analyses will be interpreted as weighted signatures per MB subgroup encompassing transcription factors, proteins and phosphorylated states of kinases. We will use this information to build mathematical models specific for each subgroup. Stochastic and dynamical simulations of the discrete models will be performed to predict possible points of intervention. Predicted targets will be functionally validated and their therapeutic relevance evaluated in the light of clinical data. The proposed study seeks to make important methodological advances to address the problem of the reduction and modeling of biological networks. Ultimately, the project will lead to the delineation of tailored therapies in MB and beyond.