Abstract
Gene regulatory networks play an important role the molecular mechanism underlying biological processes. Modeling of these networks is an important challenge to be addressed in the post genomic era. Several methods have been proposed for estimating gene networks from gene expression data. Computational methods for development of network models and analysis of their functionality have proved to be valuable tools in bioinformatics applications. In this paper we tried to review the different methods for reconstructing gene regulatory networks.
Highlights
A gene regulatory network or genetic regulatory network (GRN) is a collection of DNA segments in a cell which interact with each other indirectly and with other substances in the cell, thereby governing the rates at which genes in the network are transcribed into mRNA
Dynamic Bayesian networks (DBN) evolved feedback loops to effectively deal with the temporal aspects of regulatory networks but their benefits are hindered by the high computational cost required for learning the conditional dependencies in the cases where large numbers of genes are involved
In this paper we have reviewed the different modeling methods for reconstructing gene networks from gene expression data
Summary
A gene regulatory network or genetic regulatory network (GRN) is a collection of DNA segments in a cell which interact with each other indirectly (through their RNA and protein expression products) and with other substances in the cell, thereby governing the rates at which genes in the network are transcribed into mRNA. The groups of genes, regulatory proteins and their interactions are often referred to as regulatory networks, whereas the complete set of metabolites and the enzyme-driven reactions constitute the metabolic networks. The nodes of this network are genes and the edges between nodes represent gene interactions through which the products of one gene affect those of another. The first class 1) logical models, describes regulatory networks qualitatively They allow users to obtain a basic understanding of the different functionalities of a given network under different conditions. In this article we review the various modeling techniques for reconstructing gene regulatory network
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