A Survey On Gene Regulatory Network With Optimization Techniques

Recognizing the connections between genes is essential to comprehending bio- logical processes in all living things with cells. Gene regulatory network is the blueprint of the connections between the genes. Understanding fundamental cellular processes and the dynamic behavior of biological systems is the primary goal of research with gene regulatory networks. In conventional biology, it is difficult to reconstruct such regulatory networks from time-series gene expression data, and it has not yet been possible to perfectly reconstruct a network that is biologically accurate. The behavior of a genome can be expressed by a biological system, but computational biology sheds light on its underlying causes. To infer the genetic relationships from the biological network dynamics obtained from the experimental time series gene expression data set, researchers have used various methods from decades. Many researchers prefer the power law-based methods like s-systems, half-systems and recurrent neural networks whereas some of them consider the probabilistic approaches like Bayesian networks or Boolean. Nowadays a new approach to graph signal processing also plays an important role in terms of reconstruction of the gene regulatory network. The objective of this paper is to give a proper overview for the recreation of the gene regulatory network from the time series datasets or from the micro array data sequences. The approaches that the researchers have employed to recreate the gene regulatory network are thoroughly surveyed in this publication. Additionally, it provides future researchers with an understanding of the advantages and disadvantages of each approach, encouraging them to think creatively and beyond the box to increase the network’s prediction accuracy. This paper also includes comparative studies regarding the inference accuracy of the regulatory network which is going to help the researchers to understand the most prominent and significant approach for this work.