Modeling biological systems emerging from the observation of many different (omics) data types is a current challenge of systems approaches (biology, medicine, pharmacology, nutrition) that aim at fostering personalized diagnoses and interventions. The constant improvement of high-throughput technology is making complexity of models and analysis explode. We developed some pieces of technology that can help addressing these challenges. This talk will show how multi-level data can be pre-processed to obtain robust biomarkers with a rank-based novel algorithm. How data can be turned into pathways ranked by their level of activity according to a specific experimental condition. Then, selected pathways (possibly biomarker- related) can be semi-automatically mapped into graphical models suitable for stochastic simulation and in-silico what-if experiments. We will also show how NLP can be an essential tool in all the steps mentioned above.