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Related Concept Videos

Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Parkinson Disease ll: Pathophysiology01:24

Parkinson Disease ll: Pathophysiology

Parkinson disease (PD) is a progressive neurodegenerative disorder primarily affecting movement, with additional non-motor features. Its pathophysiology involves complex interactions among genetic susceptibility, environmental exposures, and cellular dysfunction, including dopaminergic neuron loss, protein aggregation, and mitochondrial impairment.Selective NeurodegenerationA key feature is the degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to reduced...
Myasthenia Gravis ll: Pathophysiology01:22

Myasthenia Gravis ll: Pathophysiology

The disease process of myasthenia gravis begins at the neuromuscular junction, where antibodies attack key proteins needed for muscle activation. This immune reaction weakens signal transmission, leading to the characteristic muscle fatigue and weakness that define the condition.Immune-Mediated DamageIn most individuals, antibodies target acetylcholine receptors (AChRs) on the postsynaptic membrane of muscle cells. By blocking acetylcholine binding, these antibodies prevent the nerve signal...
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Pharmacokinetic Models: Overview

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There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...

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Related Experiment Video

Updated: May 11, 2026

Modeling Multiple Sclerosis in the Two Sexes: MOG35-55-Induced Experimental Autoimmune Encephalomyelitis
05:44

Modeling Multiple Sclerosis in the Two Sexes: MOG35-55-Induced Experimental Autoimmune Encephalomyelitis

Published on: October 13, 2023

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

Isabella Bordi1, Renato Umeton, Vito A G Ricigliano

  • 1Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 2, 00185 Rome, Italy.

International Journal of Genomics
|May 15, 2013
PubMed
Summary

Multiple sclerosis relapses and remissions occur randomly over time, not periodically. A new model suggests random external factors interacting with subtle causes explain the disease

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Statistics

Background:

  • Multiple sclerosis (MS) etiology involves heritable and nonheritable factors, but their small effect sizes offer limited explanation for disease onset.
  • The erratic course of MS, characterized by relapses and remissions, suggests underlying dynamic processes influencing disease activity.
  • Understanding the temporal patterns of MS relapses and remissions is crucial for developing effective therapeutic strategies and comprehending disease mechanisms.

Purpose of the Study:

  • To model the erratic disease course of multiple sclerosis using time series data of relapses and remissions.
  • To investigate the temporal distribution of multiple sclerosis relapses and remissions, specifically examining periodic versus random occurrences.
  • To develop and validate a mechanistic model that explains the dynamics of multiple sclerosis relapses and remissions, considering underlying etiologic factors.

Main Methods:

  • Analysis of time series data comprising relapses and remissions from 70 multiple sclerosis patients not receiving disease-modifying therapies.
  • Statistical modeling to determine the distribution of relapse and remission intervals, testing for exponential decay and periodic patterns.
  • Development of a mechanistic model incorporating random forcing to simulate and explain the observed disease course dynamics.

Main Results:

  • Multiple sclerosis relapses and remissions were found to follow exponential decaying distributions, indicating random occurrences rather than periodic patterns.
  • A mechanistic model with random forcing satisfactorily described the timing of relapses and remissions and the duration of disease states.
  • The model suggests that interactions between subtle etiologic factors, amplified by small external perturbations, can trigger disease onset or changes in disease state.

Conclusions:

  • The random nature of multiple sclerosis relapses and remissions can be explained by a mechanistic model involving random external perturbations acting on underlying etiologic factors.
  • This modeling approach provides a new framework for understanding complex traits, potentially addressing issues like 'missing heritability' and 'hidden environmental structure'.
  • The findings highlight the importance of considering stochastic processes in the etiology and disease course of multiple sclerosis.