We are part of the Sys4MS consortium, which is composed of researchers from five European centers: (1) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) in Spain, (2) Uniklinik RWTH Aachen (UKA) in Germany, (3) IRCCS Azienda Ospedaliera Universitaria San Martino/ IST in Italy, (4) University of Oslo (UiO) in Norway and (5) Charité University Medicine Berlin in Germany. By taking advantage of the multidisciplinary expertise of its partners and benefiting from the results of the previous CombiMS project (http://www.combims.eu; Kotelnikova et al. 2015 and BernardoFaura et al. manuscript in preparation) carried out by some members, the Sys4MS team plans to collect comprehensive data from a cohort of patients and utilize this information to develop mathematical models, which in turn can be used to generate predictive algorithms. These algorithms are expected to prognosticate the disease course and future disability in specific subgroups of MS patients and also to aid the selection of the best therapy for each individual.
We at the RWTH Aachen University Hospital will contribute to the aims of the Sys4MS consortium by building logic models for individual patients and patient subgroups based on phosphoproteomics data that reflect signaling in immune cells, using the CellNOpt method (Terfve et al. 2012; www.cellnopt.org). These models will then be analyzed to understand differences among subgroups of patients, identify biomarkers, and propose novel personalized therapies for MS.
Kotelnikova, Ekaterina, Marti BernardoFaura, Gilad Silberberg, Narsis A. Kiani, Dimitris Messinis, Ioannis N. Melas, Laura Artigas, et al. 2015. “Signaling Networks in MS: A SystemsBased Approach to Developing New Pharmacological Therapies.” Multiple Sclerosis 21 (2): 138–46.
Terfve, Camille, Terfve Camille, Cokelaer Thomas, Henriques David, Macnamara Aidan, Goncalves Emanuel, Melody K. Morris, Martijn van Iersel, Douglas A. Lauffenburger, and SaezRodriguez Julio. 2012. “CellNOptR: A Flexible Toolkit to Train Protein Signaling Networks to Data Using Multiple Logic Formalisms.” BMC Systems Biology 6 (1): 133.