Background & Objectives: Gene regulatory network inference is a problem of prime importance in the budding field of systems biology and bioinformatics. The process which uses the information present in the genes to produce functional gene products is termed gene expression, and it can be measured using the microarray technology. The scope of this work is to propose a series of information theoretic algorithms which utilize the gene expression data to reverse engineer gene regulatory networks. Methods: Using information theoretic tools, this work proposes several algorithms to indicate how the genes are interacting with each other in a biological system. One of the advantages associated with these information theoretic algorithms is that they work with minimum assumptions. The concept of mutual information is a fundamental quantity in information theory that is used extensively in this work to measure the degree of dependency between genes. There are situations when two genes are interacting but only via an intermediate gene. By employing data processing inequality and condition mutual information, this work shows the possibility to identify such indirect interactions. Results: The computer simulations illustrate that the proposed information theoretic methods provide some of the most robust algorithms for inferring the topology of gene regulatory networks. While estimating the genetic interactions, it is necessary to put a constraint on the complexity of the network so as to avoid over-fitting the network. To this end, minimum description length principle and Bayesian information criterion are used for model selection. Results performed on both synthetic and realistic gene regulatory networks attest the superior performance of the proposed algorithms. Conclusions: This work describes different information theoretic approaches for determining the possible interactions between genes using gene expression data. The performance metrics used to assess these algorithms are also discussed. It is concluded that information theoretic methods represent powerful tools for inference of gene regulatory networks.


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