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Bioinformatics Vol. 16 no. 8 2000
Pages 707-726
© 2000 Oxford University Press

Computing with Genetic Networks

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

Patrik D’haeseleer 1, Shoudan Liang 2,3 and Roland Somogyi 3

1 University of New Mexico, Department of Computer Science, Albuquerque, NM 87131, USA
2 NASA Ames Research Center, Moffett Field, CA 94035, USA
3 Incyte Pharmaceuticals, Inc., 3174 Porter Dr, Palo Alto, CA 94304, USA

Received on December 20, 1999 ; revised on February 29, 2000 ; accepted on March 3, 2000

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{at}cs.unm.edu; sliang{at}mail.arc.nasa.gov; rsomogyi{at}incyte.com


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