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Bioinformatics Advance Access originally published online on May 11, 2007
Bioinformatics 2007 23(14):1783-1791; doi:10.1093/bioinformatics/btm246
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Biological network mapping and source signal deduction

Mark P. Brynildsen 1, Tung-Yun Wu 2, Shi-Shang Jang 2 and James C. Liao 1,*

1Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA and 2Department of Chemical Engineering, National Tsing-Hua University, Hsinchu 30043, Taiwan, R.O.C

*To whom correspondence should be addressed.


  Abstract

Motivation: Many biological networks, including transcriptional regulation, metabolism, and the absorbance spectra of metabolite mixtures, can be represented in a bipartite fashion. Key to understanding these bipartite networks are the network architecture and governing source signals. Such information is often implicitly imbedded in the data. Here we develop a technique, network component mapping (NCM), to deduce bipartite network connectivity and regulatory signals from data without any need for prior information.

Results: We demonstrate the utility of our approach by analyzing UV-vis spectra from mixtures of metabolites and gene expression data from Saccharomyces cerevisiae. From UV-vis spectra, hidden mixing networks and pure component spectra (sources) were deduced to a higher degree of resolution with our method than other current bipartite techniques. Analysis of S.cerevisiae gene expression from two separate environmental conditions (zinc and DTT treatment) yielded transcription networks consistent with ChIP-chip derived network connectivity. Due to the high degree of noise in gene expression data, the transcription network for many genes could not be inferred. However, with relatively clean expression data, our technique was able to deduce hidden transcription networks and instances of combinatorial regulation. These results suggest that NCM can deduce correct network connectivity from relatively accurate data. For noisy data, NCM yields the sparsest network capable of explaining the data. In addition, partial knowledge of the network topology can be incorporated into NCM as constraints.

Availability: Algorithm available on request from the authors. Soon to be posted on the web, http://www.seas.ucla.edu/~liaoj/

Contact: liaoj{at}ucla.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Chris stoeckert

Received on November 25, 2006; revised on April 30, 2007; accepted on April 30, 2007

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