In this chapter, we illustrate the utilization of network analysis and mechanistic modeling, two potent branches of systems biology, to simplify the representation of intricate biological processes such as cell signaling, gene regulation, and metabolic pathways. Specifically, we demonstrate the application of a well-established method to generate a microRNA-transcription factor-gene regulatory feed-forward loop network extracted from the GEO dataset GSE163877. Furthermore, we outline a method for constructing a deterministic model using the LSODA method based on the sub-network. This model furnishes insights into the roles of crucial differentially expressed microRNAs and transcription factors in gene expression associated with Alzheimer’s disease progression. Our analysis of the model reveals elevated kinetics of synthesis for EGR1, miR-6891, miR-4786, and LTBP1. The model suggests the linear upregulation of miR-8080, miR-3921, HSPB6, and downregulation MX2 gene. The rest of the miRNA, TFs, and genes shows a momentary variation in expression and if the system is undisturbed, they attain equilibrium. Thus, we elucidate how mechanistic modeling, along with perturbation studies and network analysis of expression data, can yield diverse insights into the trajectory of disease progression.