Mathematical Modeling of Neuroinflammation in Neurodegenerative Diseases

Area of Science:

• Neuroscience
• Computational Biology
• Systems Biology

Background:

• Age-related neurodegenerative diseases (e.g., Alzheimer's, Parkinson's) pose a growing public health challenge.
• Chronic central nervous system inflammation (neuroinflammation) is a common pathological feature across these diseases.
• Current therapeutic strategies for neurodegenerative diseases are limited due to complex pathology and challenges in preclinical translation.

Purpose of the Study:

• To provide a background on neuroinflammation in the context of neurodegenerative diseases.
• To review and summarize existing mathematical models of neuroinflammation.
• To identify suitable modeling approaches for addressing challenges in CNS disease research.

Main Methods:

• Literature review of neuroinflammation and mathematical modeling techniques.
• Discussion of various mathematical formalisms including ordinary differential equations (ODEs), partial differential equations (PDEs), delay differential equations (DDEs), and Boolean logic models.
• Analysis of the suitability of different modeling approaches for CNS disease complexity.

Main Results:

• Neuroinflammation is a key, yet complex, component of neurodegenerative diseases.
• A range of mathematical models exist, employing diverse mathematical frameworks.
• Logic-based modeling approaches appear particularly adept at handling the complexities inherent in modeling central nervous system diseases.

Conclusions:

• Effective disease-modifying therapies for neurodegenerative diseases are lacking.
• Systems-level mathematical models offer a powerful approach to tackle the complexity of neuroinflammation and CNS diseases.
• Logic-based modeling is a promising formalism for advancing the understanding and treatment of these conditions.