Computational analysis

Mapping transcriptional networks and DNA microarray

Understanding how gene expression is regulated at the genomic scale is essential to biotechnological research and applications. In the past, the regulation of gene expression is studied one at a time, with tedious and time-consuming methods. High throughput technologies, such as DNA microarrays, have accelerated this process significantly. With the proper DNA probes, this technology allows the detection of mRNA levels at the genomic scale. However, there are significant challenges regarding the analysis of data and reconstruction of the regulatory networks. We have developed methods for statistical treatment of microarray data, prediction of operon structures, and deducing network dynamics using microarray data.

Network Component Analysis: A novel method for network dynamic reconstruction

DNA microarray data are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. We develop a method, called network component analysis (NCA), for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. See Liao et al. (2003) and Kao et al. (2004) for details.

Statistical treatment of DNA microarray data - Bayesian Markov Chain Monte Carlo analysis

This method normalizes raw microarray data and allows the calculation of gene-specific confidence intervals. See Tseng et al. (2001) and Hyduke et al. (2003) for details. A software package is available for download.

Prediction of operons in E. coli using both expression data and genome sequence

Operon is the smallest unit of transcription in prokaryotes. A Bayesian classification scheme was developed to predict whether the genes are in an operon or not using both DNA microarray data and genome sequecne. In general, experiments that perturb a large number of genes offer more information for operon prediction than confined perturbations. These results provide a rationale for conducting expression studies comparing conditions that causes global changes in gene expression. See Sabatti et al. (2002) for details.

Characterization of gene expression patterns in E. coli

This work characterizes the gene expression changes at the genomic scale when E. coli grows on different carbon sources, glucose, acetate, and glycerol, or when E. coli overexpresses a recombinant protein. See Oh and Liao (2000a) Oh and Liao 200b), Rohlin et al. (2002)

Selected Publications

Liao, J.C.; Boscolo, R; Yang, Y.L.; Tran,L.M.; Sabatti, C.; and Roychowdhury, V. (2003) Network component analysis: Reconstruction of regulatory signals in biological systems" Proc. Natl. Acad. Sci. USA, 100: 15522–15527.

Kao, K.C.; Yang, Y.; Boscolo, R.; Sabatti, C.;Roychowdhury, V. and Liao, J.C. (2004) Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli using network component analysis, Proc. Natl. Acad. Sci. USA, 101:641-646.

Hyduke, D.R., L. Rohlin, L., L.C. Kao, and J.C. Liao, (2003) A Software Package for cDNA Microarray Data Normalization and Assessing Confidence Intervals, OMICS: A Journal of Integrative Biology, Volume 7, 225-232

Sabatti, C. Rohlin, L, Oh, M.K., and Liao, J.C. (2002) Co-expression pattern from DNA microarray experiments as a tool for operon prediction. Nucl. Acids. Res. 2002 30: 2886-2893

Liao, JC and Sabatti, C. (2002) Microanalysis of DNA microarrays, ASM News, 68:432-437.

Oh, M.-K., L. Rohlin, and J.C. Liao (2002) “Global Expression Profiling of Acetate-grown Escherichia coli” J. Biol.Chem. 277,13175–13183.

Rohlin, L., M.K. Oh, and J.C. Liao (2002) DNA microarray for microbial biotechnology: gene expression profiles in Escherichia coli during protein overexpression, J. Chin. Inst. Chem. Eng, 33, 103-112.

Tseng, G.C., M.-K.Oh, L Rohlin, J, C. Liao, and W.H. Wong (2001) “Issues In cDNA Microarray Analysis: Quality Filtering, Channel Normalization, Models of Variations and Assessment of Gene Effects” Nucleic Acid Research, 29, 2549-2557.

Oh, M.K., and J.C. Liao (2000) “Gene Expression Profiling by DNA microarrays and Metabolic Fluxes in Escherichia coli” Biotechnol. Prog. 16, 278-286.

Oh, M.K., and J.C. Liao (2000) “DNA Microarray Detection of Metabolic Responses to Protein Overproduction in Escherichia coli” Metabolic Engineering, 2, 201-209.