Genetic Network Inference: From Co-Expression Clustering To Reverse Engineering

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Genetic Network Inference: From Co-Expression Clustering To Reverse Engineering (2000)

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by Patrik D'Haeseleer , Shoudan Liang , Roland Somogyi

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@MISC{D'Haeseleer00geneticnetwork,
    author = {Patrik D'Haeseleer and Shoudan Liang and Roland Somogyi},
    title = {Genetic Network Inference: From Co-Expression Clustering To Reverse Engineering},
    year = {2000}
}

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Abstract

motivation: Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using highthroughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.

Keyphrases

genetic network inference co-expression clustering discrete boolean network therapeutic targeting shared regulatory input functional relationship various aspect functional pathway causal connection gene regulatory network predictive modeling several approach molecular biological non-linear model modeling approach systematic experimental verification genetic network co-expression profile advanced analysis aim complex molecular computational technology gene expression assay distance measure living organism multiple-cluster membership reverse engineering underlying biological system

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