AUTHOR=Chamberland Éléonore , Moravveji Seyedadel , Doyon Nicolas , Duchesne Simon TITLE=A computational model of Alzheimer's disease at the nano, micro, and macroscales JOURNAL=Frontiers in Neuroinformatics VOLUME=18 YEAR=2024 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2024.1348113 DOI=10.3389/fninf.2024.1348113 ISSN=1662-5196 ABSTRACT=Introduction

Mathematical models play a crucial role in investigating complex biological systems, enabling a comprehensive understanding of interactions among various components and facilitating in silico testing of intervention strategies. Alzheimer's disease (AD) is characterized by multifactorial causes and intricate interactions among biological entities, necessitating a personalized approach due to the lack of effective treatments. Therefore, mathematical models offer promise as indispensable tools in combating AD. However, existing models in this emerging field often suffer from limitations such as inadequate validation or a narrow focus on single proteins or pathways.

Methods

In this paper, we present a multiscale mathematical model that describes the progression of AD through a system of 19 ordinary differential equations. The equations describe the evolution of proteins (nanoscale), cell populations (microscale), and organ-level structures (macroscale) over a 50-year lifespan, as they relate to amyloid and tau accumulation, inflammation, and neuronal death.

Results

Distinguishing our model is a robust foundation in biological principles, ensuring improved justification for the included equations, and rigorous parameter justification derived from published experimental literature.

Conclusion

This model represents an essential initial step toward constructing a predictive framework, which holds significant potential for identifying effective therapeutic targets in the fight against AD.