A Comprehensive Computational Model of Alzheimer's Disease: From Nano to Micro Scale
Posted on: 11 Sep 2024
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While many individual hypotheses about the causes of Alzheimer's Disease (AD) have been studied, large-scale efforts to integrate these factors are rare due to the complexity involved. Experimentally testing such comprehensive theories is challenging because of the numerous variables. However, computational neuroscience allows for the simultaneous study of multiple factors, prediction generation, and validation with real data.
Method: The computational model uses 19 ordinary differential equations to describe the dynamics of proteins at the nanoscale (e.g., Aẞ monomers, oligomers, plaques, tau filaments, tangles, anti-inflammatory cytokines, insulin) and cell populations at the microscale (e.g... neurons, astrocytes, macrophages, microglia). These equations are parameterized by sex and APOE status, with initial conditions taken from existing literature. The main outcomes measured are the accumulation of pathological markers of AD (such as Aẞ monomers or plaques and tau filaments or tangles) and neuronal death. The model simulates these processes in daily increments over a 50-year lifespan.
Results: The model shows that the progression of various forms of amyloid is similar for both APOE4-negative and APOE4-positive individuals, with some variations between groups. Neuronal loss occurs earlier in those with an APOE4 allele, regardless of sex. Specifically, neuronal losses were 10.9% for APOE4 women, 11.7% for APOE4+ women, 10.9% for APOE4 men, and 12.1% for APOE4+ men, aligning with existing literature. Conclusion: Computational models are a crucial first step in developing a complex, predictive framework for AD and hold significant promise for identifying effective therapeutic targets in the fight against the disease.