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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
Published on: December 15, 2023
Model Validation Pipeline Against Longitudinal Alzheimer's Biomarker Data
Rabha W Ibrahim1,2, Mona Hmoud AlSheikh3
1Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, 602105, India. rabhaibrahim@yahoo.com.
Neuroinformatics
|May 23, 2026
Summary
This study models Alzheimer's disease using a novel fractional-order approach. The model accurately simulates disease progression and demonstrates how treatments can reduce tau buildup and protect neurons.
Area of Science:
- Mathematical Biology
- Computational Neuroscience
- Biophysics
Background:
- Alzheimer's disease is characterized by the accumulation of amyloid-beta and tau proteins.
- Understanding the spatiotemporal dynamics of these proteins is crucial for developing effective treatments.
- Fractional calculus offers a powerful framework for modeling complex biological processes with memory effects.
Purpose of the Study:
- To develop a novel fractional-order model for Alzheimer's disease (AD) progression.
- To investigate the spatiotemporal dynamics of amyloid-beta and tau proteins using a generalized Atangana-Baleanu-Caputo (ABC) operator.
- To simulate the impact of therapeutic interventions on AD pathology and neuron survival.
Main Methods:
- Development of a fractional-order mathematical model incorporating a [Formula: see text]-generalized Atangana-Baleanu-Caputo (ABC) operator.
- Analysis of fractional parameters (α, [Formula: see text], τ) influencing memory depth, kernel deformation, and temporal scaling.
- Numerical simulations to explore disease spread, nonlocal interactions, and the effects of a treatment term with drug diffusion and decay.
Main Results:
- Intermediate fractional orders ([Formula: see text]) yield biologically realistic propagation delays in disease spread.
- Lower [Formula: see text] values accelerate tau diffusion across the brain connectome due to enhanced nonlocal interactions.
- Increasing the scaling parameter τ slows tau accumulation, simulating effective clearance or treatment efficacy.
- Sustained low drug decay rates ([Formula: see text]) significantly reduce tau concentrations and preserve neuron populations.
Conclusions:
- The developed [Formula: see text]-ABC fractional-order model effectively captures the hereditary and memory-dependent aspects of Alzheimer's disease progression.
- The model provides a flexible computational platform for simulating various therapeutic strategies and predicting disease trajectories.
- Findings highlight the potential of fractional calculus in understanding complex neurodegenerative diseases and evaluating treatment outcomes.
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