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A mathematical model of insulin resistance in Parkinson’s disease

Paper ID Volume ID Publish Year Pages File Format Full-Text
15060 1370 2015 14 PDF Available
A mathematical model of insulin resistance in Parkinson’s disease

•Inflammation and oxidative stress cause insulin resistance in Parkinson’s disease.•Parkinson’s disease is accentuated by insulin resistance.•A combination of treatment options proves effective against Parkinson’s disease.•Delayed treatment states provide a more realistic view of drug efficacy.

This paper introduces a mathematical model representing the biochemical interactions between insulin signaling and Parkinson’s disease. The model can be used to examine the changes that occur over the course of the disease as well as identify which processes would be the most effective targets for treatment. The model is mathematized using biochemical systems theory (BST). It incorporates a treatment strategy that includes several experimental drugs along with current treatments. In the past, BST models of neurodegeneration have used power law analysis and simulation (PLAS) to model the system. This paper recommends the use of MATLAB instead. MATLAB allows for more flexibility in both the model itself and in data analysis. Previous BST analyses of neurodegeneration began treatment at disease onset. As shown in this model, the outcomes of delayed, realistic treatment and full treatment at disease onset are significantly different. The delayed treatment strategy is an important development in BST modeling of neurodegeneration. It emphasizes the importance of early diagnosis, and allows for a more accurate representation of disease and treatment interactions.

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BST, biochemical systems theory; PLAS, power law analysis and simulation; PD, Parkinson’s disease; T2DM, type-II diabetes mellitus; ROS, reactive oxygen species; RNS, reactive nitrogen species; ODE, ordinary differential equation; PPARG, peroxisome-prolif
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A mathematical model of insulin resistance in Parkinson’s disease
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 56, June 2015, Pages 84–97
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Physical Sciences and Engineering Chemical Engineering Bioengineering