||February 6, 2008
||Dr. Ruisheng Wang, Chinese Academy of Science, Beijing, China
||Inferring transcriptional interactions and regulator activities from
Identifying the relationships between transcription factors (TFs)
their targets from gene expression data is of utmost importance for
understanding the complex regulatory mechanisms in cellular systems.
However, the transcription factor activities (TFAs) cannot be measured
directly by standard microarray experiment owing to various
posttranslational modifications. In particular, cooperative mechanism and
combinatorial control are common in gene regulation, which means TFs usually
recruit other proteins cooperatively to facilitate transcriptional reaction
processes. In this talk, I describe a novel method for inferring
transcriptional regulatory networks (TRN) from gene expression data based on
protein transcription complexes and multiple data sources. In addition, I
will also talk about a new technique to combine multiple time-course
microarray datasets from different conditions for inferring the TRN in a
more accurate manner.
The method theoretically ensures the derivation of the most consistent
network structure with respect to all of the datasets, thereby not only
significantly alleviating the problem of data scarcity but also remarkably
improving the prediction reliability.
1. Y.Wang, T.Joshi, D.Xu, X-S.Zhang, L.Chen, Inferring Gene Regulatory
Networks from Multiple Microarray Datasets, Bioinformatics, 22, 2413 - 2420,
2. R. Wang, Y.Wang, X-S. Zhang, L.Chen. Inferring Transcriptional Regulatory
Networks from High-throughput Data. Bioinformatics,
3. R.Wang, Y.Wang, L-Y.Wu, X-S.Zhang, L.Chen. Analysis on Multi-domain
Cooperation for Predicting Protein-Protein Interactions. BMC Bioinformatics,
8:391, doi:10.1186/1471-2105-8-391, 2007.