(6dg) Analysis of Transcription Networks

Organisms use a variety of cellular networks to coordinate responses from internal and external stimuli. With the advent of high-throughput technologies, such as DNA microarray and ChIP-chip binding assays, it is now possible to study these networks at the system level. In particular, DNA microarray is a popular technique that has generated a multitude of gene expression data. Gene expression is modulated by transcription factors (TF) through a protein-DNA interaction network that dictates which TF controls which gene promoter. This network, commonly referred to as the transcription network, integrates signals from upstream pathways into a cohesive cellular response. Understanding the transcription network and signals transduced by it (transcription factor activities, TFA) is key to advancements in metabolic engineering, including biofuel production, and drug discovery and design. Fundamental questions our research has addressed include, how are transcription networks structured, what capabilities do these structures entail, how do transcription networks respond to environmental stress, and how do we identify the direct targets of stimuli from gene expression data and other available sources. To this end, we have characterized the ability of transcription networks to generate gene expression (1), developed a method to cope with uncertainty in transcription networks caused by network dynamics (eg. due to environment and stress) and noise (2), designed a method to identify transcription networks and TFAs solely from gene expression data (3), developed techniques to infer functional roles for TFs (4, 5), and are currently attempting to identify direct targets of butanol in Escherichia coli through the use of DNA microarray experiments and bioinformatics techniques we have developed, in an effort to increase the tolerance of E. coli to this biofuel.

1. Brynildsen MP, Tran LM, Liao JC. (2006) Versatility and connectivity efficiency of bipartite transcription networks. Biophys J., Oct 15;91(8):2749-59. (Epub 2006 Jun 30)

2. Brynildsen MP, Tran LM, Liao JC. (2006) A Gibbs sampler for the identification of gene expression and network connectivity consistency. Bioinformatics, Dec 15;22(24):3040-6 (Epub 2006 Oct 23)

3. Brynildsen MP, Wu TY, Jang SS, Liao JC. (2007) Biological network mapping and source signal deduction. Bioinformatics, (Epub May 15)

4. Yang YL, Suen J, Brynildsen MP, Galbraith S, Liao JC. (2005) Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics, Jun 10;6(1):90.

5. Tran LM, Brynildsen MP, Kao KC, Suen JK, Liao JC. (2005) gNCA: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation. Metab Eng., Mar;7(2):128-41.