Noise in multiple sclerosis: unwanted and necessary.

As our knowledge about the etiology of multiple sclerosis (MS) increases, deterministic paradigms appear insufficient to describe the pathogenesis of the disease, and the impression is that stochastic phenomena (i.e. random events not necessarily resulting in disease in all individuals) may contribute to the development of MS. However, sources and mechanisms of stochastic behavior have not been investigated and there is no proposed framework to incorporate nondeterministic processes into disease biology. In this report, we will first describe analogies between physics of nonlinear systems and cell biology, showing how small-scale random perturbations can impact on large-scale phenomena, including cell function. We will then review growing and solid evidence showing that stochastic gene expression (or gene expression "noise") can be a driver of phenotypic variation. Moreover, we will describe new methods that open unprecedented opportunities for the study of such phenomena in patients and the impact of this information on our understanding of MS course and therapy.

A mechanistic, stochastic model helps understand multiple sclerosis course and pathogenesis.

Heritable and nonheritable factors play a role in multiple sclerosis, but their effect size appears too small, explaining relatively little about disease etiology. Assuming that the factors that trigger the onset of the disease are, to some extent, also those that generate its remissions and relapses, we attempted to model the erratic behaviour of the disease course as observed on a dataset containing the time series of relapses and remissions of 70 patients free of disease-modifying therapies. We show that relapses and remissions follow exponential decaying distributions, excluding periodic recurrences and confirming that relapses manifest randomly in time. It is found that a mechanistic model with a random forcing describes in a satisfactory manner the occurrence of relapses and remissions, and the differences in the length of time spent in each one of the two states. This model may describe how interactions between "soft" etiologic factors occasionally reach the disease threshold thanks to comparably small external random perturbations. The model offers a new context to rethink key problems such as "missing heritability" and "hidden environmental structure" in the etiology of complex traits.