Magnesium-based biomaterials belong to the third generation of biomaterials that are also bioactive. These smart materials combine bioactivity and biodegradability, and elicit specific cellular responses at the molecular level. In fact, osteoinductive properties have been observed in mesenchymal stem cells in the presence of Magnesium. The mechanistic understanding of the physiological effects however, remains a difficult task as Mg is involved in a multitude of biological reactions. The study of protein interactions may shed light on the molecular processes in Mg-stimulated cells, therefore, suitable data mining tools are required to analyze the large amount data generated via proteomics. Protein compositions over time between two conditions (human mesenchymal stem cells cultured with and without Mg degradation products) were analyzed using Vester's Sensitivity Model. Proteins whose dynamics significantly change from one setup to the other were classified into four categories: passive, active, critical, and buffering according to their regulatory activity. In this work, we demonstrated the use of Vester's Sensitivity Model as an appropriate data mining tool. Protein network analyses highlighted the primary role of Mg-based implant degradation on cell metabolism without deleterious effect on cell viability. Furthermore, key proteins involved in calcium-dependant cellular activities were emphasized leading to further studies.