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Reverse engineering of gene regulatory networks from biological data

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Abstract Reverse engineering of gene regulatory networks (GRNs) is one of the most challenging tasks in systems biology and bioinformatics. It aims at revealing network topologies and regulation relationships between components from biological data. Owing to the development of biotechnologies, various types of biological data are collected from experiments. With the availability of these data, many methods have been developed to infer GRNs. This paper firstly provides an introduction to the basic biological background and the general idea of GRN inferences. Then, different methods are surveyed from two aspects: models that those methods are based on and inference algorithms that those methods use. The advantages and disadvantages of these models and algorithms are discussed. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Biological Data Mining

Examples of GRNs.

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An illustration of corresponding relationship between topology and the matrix.

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An example of a DBN.

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An example of a discrete Bayesian network.

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An example of a Boolean network.

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An illustration of different cases of reverse engineering of GRNs.

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An illustration of gene expression matrix.

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An illustration of TF regulation and the ‘central dogma’ of molecular biology.

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The general procedure of inferring GRNs and its role in biological research.

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Algorithmic Development > Biological Data Mining

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