アブストラクト
| Date | November 5, 2010 |
| Speaker | Prof. Luonan Chen, Key Laboratory of Systems Biology, Chinese Academy of Sciences; Visiting Professor, Institute of Industrial Science, The University of Tokyo |
| Title | Screening Biomolecular Networks Based on High Throughput Data |
| Abstract | We developed a novel framework to screen a general biomolecular network (i.e. directed, undirected and mixed networks) by graphical model, based on the high throughput data. Rather than reverse-engineering a biomolecular network, we identify the active networks or pathways among all available experimentally verified networks, e.g. from the networks of KEGG or other databases. To verify the theoretical framework, we have performed comprehensive active regulatory network survey by network screening in 4weeks (w), 8-12w, and 18-20w Goto-Kakizaki (GK) rat liver microarray data for identifying significant transcriptional regulatory networks in the liver contributing to diabetes. The comprehensive survey of the consistency between the networks and the measured data by the network screening approach in the case of non-insulin dependent diabetes in the GK rat reveals: 1. More pathways are active during inter-middle stage diabetes; 2. Inflammation, hypoxia, increased apoptosis, decreased proliferation, and altered metabolism are characteristics and display as early as 4weeks in GK strain; 3. Diabetes progression accompanies insults and compensations. |