A Topology-Aware Evolutionary Algorithm for Reverse-Engineering Gene Regulatory Networks

This chapter is concerned with modeling and simulating the dynamics of gene regulatory networks (GRNs). It explains the process of reverse-engineering GRNs from time-series gene expression data sets. The idea is to discover an optimal set of parameters for a computational model of the network that is able to adequately simulate the behavior described by the gene expression data sets. The chapter investigates three different mathematical methods used in computational models that are based on ordinary differential equations. These methods include Artificial Neural Network (ANN) method, S-System (SS) method and General Rate Law of Transcription (GRLOT) method. The mathematical models investigated in the chapter require a significant number of parameters to be fine-tuned in order for the models to accurately simulate real biological network behavior. In order to take advantage of available computational resources, parallel evolutionary algorithms are implemented using QosCosGrid-OpenMPI (QCG-OMPI).