ABSTRACT
Multiple Sclerosis (MS) is a devastating autoimmune disease which deteriorates the connections in central nervous system (CNS) through the attacks to oligodendrocytes. Studying its origin and progression, in addition to clinical developments such as MRI brain images, cerebrospinal fluid (CSF) variation and quantitative measures of disability (EDSS), which sought to early diagnosis and efficient therapy, there is an increasing interest in developing computational models using the experimental data obtained from MS patients. From the perspective of mathematical modelling, although the origin of systemic symptoms might be attributed to cellular phenomena in microscopic level such as axonal demyelination, symptoms mainly are observed in macroscopic levels. How to fill the gap between these two levels of system modelling, however, remains as a challenge in systems biology studies. Trying to provide a conceptual framework to bridge between these two levels of modelling in systems biology, we have suggested a mesoscopic model composed of interacting neuronal population, which successfully replicates the changes in neuronal population synchrony due to MS progression.
