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Int J Mol Sci. 2015 Sep 7;16(9):21215-36. doi: 10.3390/ijms160921215.

Physiological Dynamics in Demyelinating Diseases: Unraveling Complex Relationships through Computer Modeling.

Author information

1
NeuroLinx Research Institute, La Jolla, CA 92039, USA. jay@neurolinx.org.
2
Department of Neurology, Institute of Physiology, Universitätsklinikum Münster, 48149 Münster, Germany. stefan-bittner@outlook.com.
3
NeuroLinx Research Institute, La Jolla, CA 92039, USA. klaus@neurolinx.org.
4
Department of Neurology, Institute of Physiology, Universitätsklinikum Münster, 48149 Münster, Germany. sven.meuth@ukmuenster.de.
5
Neurosciences and Mental Health, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada. steve.prescott@sickkids.ca.
6
Department of Physiology and the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON M5G 1X8, Canada. steve.prescott@sickkids.ca.

Abstract

Despite intense research, few treatments are available for most neurological disorders. Demyelinating diseases are no exception. This is perhaps not surprising considering the multifactorial nature of these diseases, which involve complex interactions between immune system cells, glia and neurons. In the case of multiple sclerosis, for example, there is no unanimity among researchers about the cause or even which system or cell type could be ground zero. This situation precludes the development and strategic application of mechanism-based therapies. We will discuss how computational modeling applied to questions at different biological levels can help link together disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism. By making testable predictions and revealing critical gaps in existing knowledge, such models can help direct research and will provide a rigorous framework in which to integrate new data as they are collected. Nowadays, there is no shortage of data; the challenge is to make sense of it all. In that respect, computational modeling is an invaluable tool that could, ultimately, transform how we understand, diagnose, and treat demyelinating diseases.

KEYWORDS:

computational model; demyelination; drug discovery; multiple sclerosis; myelin; neurodegenerative disease

PMID:
26370960
PMCID:
PMC4613250
DOI:
10.3390/ijms160921215
[Indexed for MEDLINE]
Free PMC Article

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