---

The properties of scale-free and small-world are believed to reflect the functional units of networks. However, when we applied these network properties to a signaling pathway, no significant differences were found between the original undirected graphs and the graphs in which inactive proteins were eliminated from the gene expression data. For the signaling networks, we analysed them by focusing on those pathways which best reflect cellular functions. The analysis therefore started from ligands and progressed to transcription factors and cytoskeletal proteins. We employed Python module to assess a target network. This involved comparing the original and restricted signaling cascades as digraph using microarray gene expression profiles of the pre-critical and post-critical stages of late onset Alzheimer’s disease. The commonly used method of shortest path analysis neglects to consider the influences of alternative pathways that can affect the activation of transcription factors or cytoskeletal proteins. We introduced network analysis to python modules, including k-shortest paths and k-cycles, which allowed us to attain reasonable computational time and identify these pathways. This technique reflected results found in vivo, and discovered pathways not found when shortest path or degree analysis was applied. Our module enabled us to comprehensively analyse the characteristics of biomolecular networks and also enabled analysis of the effects of diseases considering the control structures as an alternative path, a feedback loop, and a feedforward loop.