Link prediction of complex networks from temporal quantity variation assigned to elements

This paper presents a method to predict gene regulatory networks from gene expression time course data, where each gene has multiple types of quantity values and each quantity type might be influenced by all other quantity types. This problem is considerably harder than the prediction from time course data of only one quantity type. The method is based on a ultra high speed gene network simulator Starpack and evolutionary mechanisms. The prediction is a combination of two stage loop, each stage using evolutionary mechanism. Networks are simulated with Starpack, and those producing time course data similar to the target gene expression data are selected as candidates. The simulation of the second stage has higher precision than the first stage, serving as local optimization process. A synthetic network was used for evaluation, and results suggest necessity of improvements.