Identifying Control Targets for Regulating Mild Cognitive Impairment Using Reduced Computational Models of a Life Kinetic Network

Abstract

Network reduction, which emphasized preserving feedback loops, identified
potential ways to regulate dynamics in a computational model of mild cognitive
impairment (MCI). Control sets capable of modulating MCI-associated
attractors were identified in the full and reduced networks, though fewer in the
latter. While the reduced size conferred a computational advantage, further
validation is needed to determine the physiological relevance and translatability
of proposed targets. Results demonstrate preserved dynamical features relevant
for identifying and modulating MCI attractors despite network reduction,
suggesting potential for data-driven intervention strategies. However, rigorous
experimental validation and refinement through iterative experiment-modeling
cycles will be essential for rigorously evaluating and progressively shaping in
silico predictions into mechanism-based MCI therap