Biological network mapping and source signal deduction.
BACKGROUND
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.


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


BACKGROUND
Supplementary data are available at Bioinformatics online.
Brynildsen
Mark P
MP
Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA 90095, USA.
Wu
Tung-Yun
TY
Jang
Shi-Shang
SS
Liao
James C
JC
D000465:Algorithms; D001499:Bayes Theorem; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005656:Fungal Proteins / Q000378:metabolism; D020869:Gene Expression Profiling; D005800:Genes, Fungal; D053858:Metabolic Networks and Pathways; D015233:Models, Statistical; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism; D013056:Spectrophotometry, Ultraviolet
D005656:Fungal Proteins