Computational modeling of the immune response in multiple sclerosis using epimod framework
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RESEARCH
Open Access
Computational modeling of the immune response in multiple sclerosis using epimod framework Simone Pernice1†, Laura Follia1,5†, Alessandro Maglione3†, Marzio Pennisi2, Francesco Pappalardo4, Francesco Novelli5, Marinella Clerico3, Marco Beccuti1* , Francesca Cordero1† and Simona Rolla3†
From 3rd International Workshop on Computational Methods for the Immune System Function (CMISF 2019) San Diego, CA, USA. 18-21 November 2019 *Correspondence: [email protected] † Simone Pernice, Laura Follia and Alessandro Maglione contributed equally to this work † Francesca Cordero, Simona Rolla: These authors jointly supervised this work 1 Department of Computer Science, University of Turin, Turin, Italy Full list of author information is available at the end of the article
Abstract Background: Multiple Sclerosis (MS) represents nowadays in Europe the leading cause of non-traumatic disabilities in young adults, with more than 700,000 EU cases. Although huge strides have been made over the years, MS etiology remains partially unknown. Furthermore, the presence of various endogenous and exogenous factors can greatly influence the immune response of different individuals, making it difficult to study and understand the disease. This becomes more evident in a personalizedfashion when medical doctors have to choose the best therapy for patient well-being. In this optics, the use of stochastic models, capable of taking into consideration all the fluctuations due to unknown factors and individual variability, is highly advisable. Results: We propose a new model to study the immune response in relapsing remitting MS (RRMS), the most common form of MS that is characterized by alternate episodes of symptom exacerbation (relapses) with periods of disease stability (remission). In this new model, both the peripheral lymph node/blood vessel and the central nervous system are explicitly represented. The model was created and analysed using Epimod, our recently developed general framework for modeling complex biological systems. Then the effectiveness of our model was shown by modeling the complex immunological mechanisms characterizing RRMS during its course and under the DAC administration. Conclusions: Simulation results have proven the ability of the model to reproduce in silico the immune T cell balance characterizing RRMS course and the DAC effects. Furthermore, they confirmed the importance of a timely intervention on the disease course. Keywords: Multiple sclerosis, Immune system, Computational modeling, Stochastic modeling, Petri net
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