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.
ASJC Scopus subject areas
- Computational Mathematics
- Molecular Biology
- Statistics and Probability
- Computer Science Applications
- Computational Theory and Mathematics