Fighting Multiple Sclerosis with Mathematics

Important results from the project

By developing mathematical models of MS at the systems level, bridging molecular process to clinical phenotype, this project will generate novel insights into the processes leading to MS development. This project aims to develop and apply computational methods enabling integration between molecular data and clinical readouts in order to generate experimentally testable predictions. Our findings may capture key steps and central players in MS that may lead to pinpoint novel Anti-MS drugs targets and ultimately contribute to improving the life quality of patients.

Expected long term effects

We have implemented 3 types of mathematical models of MS, at different level. The most comprehensive one which connect clinical data to the molecular process enable us to gain insights into the dynamic behavior of MS development. We have found phenotypic ‘omics’ signature of MS. These ‘omics’ signatures will help to understand the molecular mechanisms underpinning prognosis and response to therapy of individuals suffering from MS. Using these signatures and our experimentally tested model , we could formally describe the interactions comprising the clinical phenotype of MS.

Approach and implementation

We have applied and developed probabilistic machine learning bioinformatics tools, and differential equation models (ODE). The methods and their application in the current project holds promise to be used in other disease areas as well where there are similar challenges in terms of a gap between rich molecular data and the clinical description of patients and difficulties in prognosis and selection of therapy.