Genetic network inference: from co-expression clustering to reverse engineering

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2000
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Abstract
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 high-throughput 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. Contact: patrik@cs.unm.edu; sliang@mail.arc.nasa.gov; rsomogyi@incyte.com
Reference Key
openalex_W2163296727 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Patrik D’haeseleer, Shaolin Liang, Roland Somogyi
Journal BMC Bioinformatics
Year 2000
DOI
10.1093/bioinformatics/16.8.707
URL
https://academic.oup.com/bioinformatics/article-pdf/16/8/707/48842238/bioinformatics_16_8_707.pdf
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