Inference of scale-free networks from gene expression time series.

Full Abstract

Quantitative time-series observation of gene expression is becoming possible, for example by cell array technology. However, there are no practical methods with which to infer network structures using only observed time-series data. As most computational models of biological networks for continuous time-series data have a high degree of freedom, it is almost impossible to infer the correct structures. On the other hand, it has been reported that some kinds of biological networks, such as gene networks and metabolic pathways, may have scale-free properties. We hypothesize that the architecture of inferred biological network models can be restricted to scale-free networks. We developed an inference algorithm for biological networks using only time-series data by introducing such a restriction. We adopt the S-system as the network model, and a distributed genetic algorithm to optimize models to fit its simulated results to observed time series data. We have tested our algorithm on a case study (simulated data). We compared optimization under no restriction, which allows for a fully connected network, and under the restriction that the total number of links must equal that expected from a scale free network. The restriction reduced both false positive and false negative estimation of the links and also the differences between model simulation and the given time-series data.

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Author information

Author/s: Daisuke, Tominaga (T); Horton, Paul (P);

Affiliation: Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Aomi 2-42, Koto, Tokyo 135-0064, Japan. tominaga(-atsign-)cbrc.jp

Journal and publication information

Publication Type: Journal Article

Journal: Journal of bioinformatics and computational biology (J Bioinform Comput Biol), published in England. (Language: eng)

Reference: 2006-Apr; vol 4 (issue 2) : pp 503-14

Dates: Created 2006/07/04; Completed 2006/09/15; Revised 2008/11/21;

PMID: 16819798, status: MEDLINE (last retrieval date: 12/26/2008)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Gene Expression Profiling - methods
Models, Biological
Proteome - metabolism
Signal Transduction - physiology
Time Factors

Associated Chemicals: Proteome (0)

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