Title: "Exploiting network-based approaches for understanding the dynamic and evolutionary constraints imposed on regulatory systems"

Speaker: Sarath Chandra Janga, School of Informatics IUPUI, Center for Computational Biology & Bioinformatics, IU School of Medicine, Indianapolis, IN

Place: LILY G126
Date: September 4, 2012; Tuesday
Time: 4:30pm

An important notion that is emerging in post-genomic biology is that cellular components can be visualized as a network of interactions between different molecules like proteins, RNA, DNA and metabolites. This has led to the application of network theory to a wide range of biological problems including understanding regulation of gene expression, function prediction and drug discovery settings. While in transcriptional networks, typically trans-acting elements like TFs and sigma factors form one set of nodes and their target genes, of which they control the activity, form the other set of nodes. The links between them which have directionality from the trans-acting elements to their target genes, controlled by their cis-regulatory elements, form a complex and directional network of interactions. In the first half of my talk, I will focus on our recent understanding of the structure of the transcriptional regulatory networks in prokaryotic and eukaryotic organisms. I will then present evidence that transcriptional regulation plays a significant role in shaping the organization of genes on chromosomes in both the major domains of life, bacteria and eukarya. In the second half, I will present our efforts to systematically dissect the expression dynamics of RNA-binding proteins (RBPs) in post-transcriptional networks formed by RBPs and their target RNAs in the model eukaryote, S. cerevisiae. Our analysis shows that RBPs generally exhibit high protein stability, translational efficiency and protein abundance but their encoding transcripts tend to have low half-life. Analysis of the RBP-RNA interaction network revealed that the number of distinct targets bound by an RBP (connectivity) is strongly correlated with its protein stability, translational efficiency and abundance. We also note that RBPs show less noise in their expression in a population of cells, with highly connected RBPs showing significantly lower noise indicating that highly connected RBPs are likely to be tightly regulated at the protein level as significant changes in their expression may bring about large-scale changes in global expression levels by affecting their targets. Towards the end, I will briefly introduce the notion of Drug-Target networks and present some of the problems, which are being addressed using this framework.

Associated Reading:
Goodarzi H, Najafabadi HS, Oikonomou P, Greco TM, Fish L, Salavati R, Cristea IM, Tavazoie S. 2012. Systematic discovery of structural elements governing stability of mammalian messenger RNAs. Nature. Apr 8;485(7397):264-8. doi: 10.1038/nature11013.

Additional References:
1. Martinez-Antonio A, Janga SC, Thieffry D, Functional organisation of Escherichia coli transcriptional regulatory network, Journal of Molecular Biology, 2008
2. Janga SC, Collado-Vides J, Babu MM, Transcriptional regulation constrains the organization of genes on eukaryotic chromosomes, Proc Natl Acad Sci U S A, 2008
3. Janga, SC, Salgado H, Martinez-Antonio A, Transcriptional regulation shapes the organization of genes on bacterial chromosomes, Nucleic Acids Research, 2009
4. Mittal N, Roy N, Babu MM, Janga SC, Dissecting the expression dynamics of RNA-binding proteins in post-transcriptional regulatory networks, Proc Natl Acad Sci USA, 2009
5. Mittal N, Scherrer T, Gerber AP, Janga SC, Interplay between post-transcriptional and post-translational interactions of RNA-binding proteins, Journal of Molecular Biology, 2011
6. Janga SC, From specific to global analysis of post-transcriptional regulation in eukaryotes: Post-transcriptional regulatory networks, Briefings in Functional Genomics, 2012 (In Press)
7. Janga SC, Tzakos A, Structure and organization of drug-target networks: insights from genomic approaches for drug discovery, Mol. Biosyst, 2009

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