Sensitivity Analysis of Transition Phases of Perturbed Gene Pathways with a Neural Network

This Demonstration shows for the first time, a sensitivity analysis method based on an artificial neural network to find transition phases from homeostasis toward liver cirrhosis in hepatocytes infected by HCV. Currently, finding transition phases represents one of the fundamental steps in understanding tumor transformation. The Demonstration examines the cytokeratins gene expression profile, which was organized in a pathway by means of Ingenuity Pathway Analysis (IPA) software. Gene expression values were obtained from DNA microarrays of normal hepatocytes versus cirrhosis with HCV infection. The Demonstration describes a fast way of "handling" gene expression parameters that determine the transition from a healthy condition to cirrhosis within the cytokeratins context. This approach permits singling out critical "catastrophe points", simply perturbing an independent variable (such as a gene within the cytokeratins pathway) and visualizing how the perturbation impacts a particular dependent variable. This Demonstration could answer two fundamental questions: (1) how moving the control bar for a specific gene can generate the transition from normal condition to cirrhosis, and (2) moving more control bars, what minimal movement of a group of genes can produce the same perturbation. This method could be useful in measuring relationship fluxes among genes, which currently have not been described in the literature, suggesting new experimental designs.