Modeling of Gene Regulatory Networks Using State Space Models

Computational Genomics is now becoming the growing area for researchers to decipher biology from genome sequences and related high throughput data. In the post genomic era, there is huge amount of genomic data available because of different advanced experimental technology like microarray technology, Chromatin immune-precipitation with array hybridization (ChIP-chip) etc. [1]. In order to analyze and getting informative knowledge from these data, the efficient statistical approaches are required. These methods are very useful in knowing the interactions among different genes through their by-products, which ultimately regulate the expression of thousand genes in a living cell. Of all the available datasets, gene expression data is the most widely used for gene regulatory network inference. Regulation of gene expression is primarily mediated by regulatory proteins called Transcription Factors (TF). These DNA binding proteins bind to the promoter regions at the start of the genes and thereby controlling the transcription of the genes which ultimately regulates the expression of that gene [2] More specifically, TFs regulate the initiation of transcription through different strategies operating on the transcription mechanism. The interactions between the genes give rise to a complex network like structure known as Gene Regulatory Networks(GRN). The understanding of the nature of information on genes and their regulators is improved by the use of network theory, which permits us to uncover some patterns present in gene regulation [3]. Formally, GRNs are modelled as directed graphs which are composed of vertices or nodes as bio-molecules (e.g. genes) and directed edges (connection between genes) represent the regulatory interaction (activatory or inhibitory type).