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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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A constraint-based modelling approach to metabolic dysfunction in Parkinson's disease
Longfei Mao1, Averina Nicolae1, Miguel A P Oliveira1
1Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts-Fourneaux, L-4362 Esch-Belval, Luxembourg.
Computational and Structural Biotechnology Journal
|October 28, 2015
Summary
This review proposes using constraint-based modeling to understand Parkinson's disease (PD) pathology. This approach can help decipher the complex biochemical pathways contributing to dopaminergic neuron degeneration in PD.
Area of Science:
- Neuroscience
- Systems Biology
- Computational Biology
Background:
- Sporadic Parkinson's disease (PD) is characterized by the degeneration of dopaminergic neurons in the substantia nigra pars compacta.
- The precise causes of this neurodegeneration remain incompletely understood, with multiple biochemical pathways implicated.
- Constraint-based modeling (CBM) offers a powerful computational approach to investigate complex biological systems.
Purpose of the Study:
- To outline a framework for applying CBM to dopaminergic neuronal metabolism in Parkinson's disease.
- To systematically examine the contribution of various biochemical pathway dysfunctions to neurodegeneration.
- To advance our understanding of the multi-factorial mechanisms underlying Parkinson's disease pathology.
Main Methods:
- Review of current literature on Parkinson's disease pathogenesis.
- Exploration of constraint-based modeling principles and their application to metabolic networks.
- Proposal of a methodological framework for integrating CBM with neurobiological data.
Main Results:
- Identification of CBM as a promising, yet underutilized, tool in Parkinson's disease research.
- Highlighting the potential of CBM to dissect complex, multi-systemic pathway interactions.
- Establishing a conceptual basis for future computational studies on neurodegenerative mechanisms.
Conclusions:
- Constraint-based modeling provides a systematic approach to investigate Parkinson's disease.
- This framework can elucidate the intricate interplay of biochemical dysfunctions leading to dopaminergic neuron loss.
- Future research employing CBM is crucial for deciphering the multi-factorial etiology of Parkinson's disease.
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