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Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
Published on: July 19, 2019
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A computational approach based on the colored Petri net formalism for studying multiple sclerosis
Simone Pernice1, Marzio Pennisi2, Greta Romano1
1Department of Computer Science, University of Turin, Turin, Italy.
BMC Bioinformatics
|December 12, 2019
Summary
A new computational method models Relapsing Remitting Multiple Sclerosis (RRMS) progression. This approach analyzes drug effects, like daclizumab, and RRMS in pregnancy, offering insights into disease dynamics.
Area of Science:
- Computational Biology
- Systems Biology
- Immunology
Background:
- Multiple Sclerosis (MS) is an immune-mediated CNS disease damaging myelin.
- Relapsing Remitting Multiple Sclerosis (RRMS) is a common form with relapses and remissions.
- Previous treatments like daclizumab showed efficacy but significant side effects.
Purpose of the Study:
- To introduce a novel computational methodology for studying RRMS.
- To model and analyze RRMS dynamics using Petri Nets.
- To investigate specific RRMS scenarios, including drug effects and pregnancy.
Main Methods:
- Utilized an extended Colored Petri Net (PN) formalism for system description and ODE derivation.
- Implemented Latin Hypercube Sampling with the PRCC index for ODE parameter calibration.
- Developed and analyzed an RRMS model within the GreatSPN suite.
Main Results:
- Successfully constructed and studied a computational model of RRMS.
- The methodology enabled the analysis of daclizumab's effects on RRMS.
- The model also facilitated the study of RRMS progression during pregnancy.
Conclusions:
- A new computational methodology for studying RRMS has been proposed.
- The developed model accurately reproduces expected RRMS behaviors.
- This approach offers a framework for analyzing complex disease dynamics.
