%0 Journal Article
%T An Overview of the Statistical Methods Used for Inferring Gene Regulatory Networks and Protein-Protein Interaction Networks
%A Amina Noor
%A Erchin Serpedin
%A Mohamed Nounou
%A Hazem Nounou
%A Nady Mohamed
%A Lotfi Chouchane
%J Advances in Bioinformatics
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/953814
%X The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed. 1. Introduction Postgenomic era is marked by the availability of a deluge of genomic data and has, thus, enabled the researchers to look towards new dimensions for understanding the complex biological processes governing the life of a living organism [1¨C5]. The various life sustaining functions are performed via a collaborative effort involving DNA, RNA, and proteins. Genes and proteins interact with themselves and each other and orchestrate the successful completion of a multitude of important tasks. Understanding how they work together to form a cellular network in a living organism is extremely important in the field of molecular biology. Two important problems in this considerably nascent field of computational biology are the inference of gene regulatory networks and the inference of protein-protein interaction networks. This paper first looks at how the genes and proteins interact with themselves and then discusses the inference of an integrative cellular network of genes and proteins combined. Gene regulation is one of the many fascinating processes taking place in a living organism whereby the expression and repression of genes are controlled in a systematic manner. With the help of the enzyme RNA polymerase, DNA transcribes into mRNA which may or may not translate into proteins. It is found that in certain special cases mRNA is reverse-transcribed to DNA. The processes of transcription and translation are schematically represented in Figure 1, where the interactions in black show the most general framework and the interactions
%U http://www.hindawi.com/journals/abi/2013/953814/