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 personalized-fashion 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.

Computational modeling of the immune response in multiple sclerosis using epimod framework

Pernice S.
Co-first
;
Follia L.
Co-first
;
Maglione A.
Co-first
;
Novelli F.;Clerico M.;Beccuti M.
;
Cordero F.
Co-last
;
Rolla S.
Co-last
2020

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 personalized-fashion 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.
21
Suppl 17
550
550
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03823-9
Computational modeling; Immune system; Multiple sclerosis; Petri net; Stochastic modeling; Algorithms; Daclizumab; Humans; Immune System; Immunosuppressive Agents; Multiple Sclerosis, Relapsing-Remitting; Stochastic Processes; Models, Biological; User-Computer Interface
Pernice S.; Follia L.; Maglione A.; Pennisi M.; Pappalardo F.; Novelli F.; Clerico M.; Beccuti M.; Cordero F.; Rolla S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1795050
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