|Title:||Reconstructing Gene Regulatory Network Using Linear Time-Variant Model|
|Keywords:||Gene Regulatory Network, Reverse Engineering, Multi-Objective Optimization|
With the advent of high throughput DNA microarray technology, the field of functional genomics has been revolutionized by the large amount of gene expression data generated in recent years. The analysis of these large scale data has become very useful for investigating gene functions and the interactions among the genes. However, there are few known data analysis techniques capable of fully exploiting this new class of data. In this research work, we have presented a multi-objective evolutionary strategy for efficiently attaining the skeletal structure of the biomolecular networks and estimating the effective regulatory parameters from the gene expression time-series data using the linear time-variant formalism. Here, Elitist Differential Evolution for Multi-objective Optimization, a versatile, robust and well-known Multi-Objective Evolutionary Algorithm has been used. The suitability of the proposed method has been verified in gene network reconstruction experiments, varying the noise level present in gene expression profiles. And finally, we have applied the methodology for analyzing the real expression dataset of SOS DNA repair system in Escherichia coli and succeeded to reconstruct the network of some key regulators.