Newly emerging advances in both measurement as well as bio-inspired computation techniques have facilitated the development of so-called lipidomics technologies and offer an excellent opportunity to understand regulation at the molecular level in many diseases such as cancer. The analysis and the understanding of the global interactional behavior of lipidomic networks remains a challenging task and can not be accomplished solely based on intuitive reasoning. The present contribution aims at developing novel computational approaches to assess the topological and functional aspects of lipidomic networks and discusses their benefits compared to recently proposed techniques. Graph-clustering methods are introduced as powerful correlation networks which enable a simultaneous exploration and visualization of co-regulation in glioblastoma data. The dynamic description of the lipidomic network is given through multi-mode nonlinear autonomous stochastic systems to model the interactions at the molecular level and to study the success of novel gene therapies for eradicating the aggressive glioblastoma. These new paradigms are providing unique "fingerprints" by revealing how the intricate interactions at the lipidome level can be employed to induce apoptosis (cell death) and are thus opening a new window to biomedical frontiers.