The therapeutic challenge of complex diseases requires the use of combination therapies to target the distinct mechanisms and pathways involved. Such complex diseases will benefit from the design of computational models adopting a systems perspective to integrate the knowledge generated by ‘omics technologies and clinical data. Multiple Sclerosis is a prototypic debilitating complex disease in which an autoimmune attack is launched against the brain. Current therapies for MS are far from effective and target only part of the immune response. Hence, the need to develop combination therapies with good safety profiles that better control this condition, the main aim of CombMS. By understanding how current MS therapies work in biological networks and taking advantage of novel compounds, more effective combination therapies will be designed for MS. Indeed, the tools developed will be applicable to other immune and complex diseases to improve their therapeutic options in the future. The data for the modelling process will be generated from biological and clinical samples, and the predictions about combination therapies from the computational models tested using in vitro and animal models of MS. Given the limitations of animal models in translational research, we shall focus on studying the phosphoproteome in samples from individuals with MS (PBMC) using xMAP technology. The phosphoproteome has been identified as a system likely to be affected in MS and the novel therapeutic compounds that will be tested are known to act through signalling pathways involving receptor tyrosine kinases. The mechanistic modelling will be extended to the different levels of the response to therapy by analysing biological networks integrating gene and protein networks, with drugs, their effects and side-effects. As well as developing new combination therapies for MS, CombMS will provide proof of concept of the useful short term results that a systems biology drug discovery approach can provide.