Inferring dynamic gene networks under varying conditions for transcriptomic network comparison

Teppei Shimamura, Seiya Imoto, Rui Yamaguchi, Masao Nagasaki, Satoru Miyano

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Motivation: Elucidating the differences between cellular responses to various biological conditions or external stimuli is an important challenge in systems biology. Many approaches have been developed to reverse engineer a cellular system, called gene network, from time series microarray data in order to understand a transcriptomic response under a condition of interest. Comparative topological analysis has also been applied based on the gene networks inferred independently from each of the multiple time series datasets under varying conditions to find critical differences between these networks. However, these comparisons often lead to misleading results, because each network contains considerable noise due to the limited length of the time series. Results: We propose an integrated approach for inferring multiple gene networks from time series expression data under varying conditions. To the best of our knowledge, our approach is the first reverse-engineering method that is intended for transcriptomic network comparison between varying conditions. Furthermore, we propose a state-of-the-art parameter estimation method, relevanceweighted recursive elastic net, for providing higher precision and recall than existing reverse-engineering methods. We analyze experimental data of MCF-7 human breast cancer cells stimulated by epidermal growth factor or heregulin with several doses and provide novel biological hypotheses through network comparison. Availability: The software NETCOMP is available at Contact: Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Article numberbtq080
Pages (from-to)1064-1072
Number of pages9
Issue number8
Publication statusPublished - 2010 Mar 1

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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