Dynamics of Amyloid Beta Protein Signaling under Complexity Reduction: A Mathematical Modeling and BooleSim Simulation Approach

Understanding how complex biological systems behave over time is essential for predicting their future states, especially when studying Alzheimer's disease. Mathematical modeling of such systems offers a valuable approach to exploring not only theoretical aspects of network behavior but also their biological and medical implications. This study investigates how reducing the complexity of the amyloid-beta signaling pathway influences its dynamic behavior. We apply Boolean modeling and network simulations in BooleSim using data from the SIGNOR database under different initial conditions that reflect healthy and disease-related cellular states. Complexity reduction involved removing non-essential interactions and simplifying regulatory motifs. Our findings show that while simplification can shorten the time to reach steady states, it does not eliminate important regulatory pathways unless critical nodes are removed. Importantly, pro-disease nodes such as BAX and GSK3ß retained their functional significance even in simplified models, confirming their central role in Alzheimer's pathology and supporting their relevance as potential therapeutic targets, consistent with current Alzheimer's drug development strategies. This work illustrates how simplified Boolean modeling can provide a practical framework for analyzing neurodegenerative systems while preserving essential biological insights.