Systems medicine modeling for multiple sclerosis. Issue 3 (June 2017) - Kotelnikova, Ekaterina; Zubizarreta, Irati; Pulido-Valdeolivas, Irene; Villoslada, Pablo
Abstract: Multiple Sclerosis (MS) is an autoimmune disease in which axons and myelin of the central nervous system (CNS) are damaged by inflammation and degenerative processes leading to significant disability. The course of the MS is hard to predict, limiting the decision making process for patient management and therapeutic options. Many genetic, lifestyle and environmental factors could influence the risks of MS development. Subtle abnormalities in the control of the immune response can be amplified and deregulated decades later suggesting that MS and other autoimmune diseases can be envisioned as dynamic diseases. Based on this premise, mathematical modeling of MS pathogenesis is an approach that would bring this dynamic concept to the understanding of the disease and therefore be useful for improving our understanding for the application of precision medicine. Modeling can be focused on different biological scales from genes and signaling pathways to cell–cell interactions, interplay between inflammatory and degenerative processes at tissue level, and predictions of clinical outcomes. Models of the autoimmune process as well as the damage of the CNS has been developed using ordinary differential equations and fitted parameters with experimental datasets. Simulations support the concept of MS as a dynamic disease that is being produced by subtle differences in the molecular and cellular control of the immune-nervous interplay amplified along time. These new systems medicine tools can now be applied for patient stratification and personalized medicine. Highlights: Predicting disease course and response to therapy is difficult in MS. Systems medicine models of MS focused in network and mathematical models. Computational models of MS will allow systems medicine applications.