I am interested in Systems Biology and Statistical Analysis on Genomics Data. My goals are to develop mathematical and statistical methodologies to understand more on biological processes or networks through integrating various large scale genomics data. One of my current focus is to model the static and dynamic transcriptional regulatory networks for cell cycle and salt stress response in Yeast. This research involves statistical data analysis on genomics data, kinetic aspects of transcription, translation, protein-DNA and protein-protein interactions, graphic models in describing transcriptional regulatory network, and data mining for model inference in a high-dimensional space. The methodologies are developed using interdisciplinary approaches with more emphasis on statistics. This research is funded by the National Institutes of Health. Another research direction is to develop a pathway-based method to understand more on functional characteristics of human leukemogenic genes. The data from various laboratories or platforms will be integrated through an efficient statistical method. The prior pathway knowledge will be incorporated in the framework of pathway-based models. This research is funded by Yale Center for Clinical Investigation. I also collaborate with biologists to analyze high throughput data. My goal is to translate new methodologies in statistics into large-scale biological experimental designs and data analyses, where many new challenges are encountered.
- Modeling for Transcriptional Regulatory Network N Sun, RJ Carroll, H Zhao. A Bayesian Error Analysis Model for reconstructing transcriptional regulatory networks. Proc. Natl. Acad. Sci. USA. 2006, 103(21): 7988-7993.
- Pathway-based Analysis N Sun, L Ma, D Pan, H Zhao and XW Deng. Evaluation of light regulatory potential of Calvin cycle steps based on large-scale gene expression profiling data. Plant Molecular Biology, 2003, 53: 467–478.
- Y Jiao, SL Tausta, N Gandotra, N Sun, T Liu, NK Clay, T Ceserani, M Chen, L Ma, M Holford, H Zhang, H Zhao, X-W Deng & T Nelson. A transcriptome atlas of rice cell types uncovers cellular, functional and developmental hierarchies. Nature Genetics Published online: 04 January 2009; | doi:10.1038/ng.282
- L Ma, C Chen, X Liu, Y Jiao, N Su, L Li, X Wang, M Cao, N Sun, X Zhang, J Bao, J Li, S Pedersen, L Bolund, H Zhao, L Yuan, G K-S Wong, J Wang, XW Deng, J Wang. A microarray analysis of the rice transcriptome and its comparison to Arabidopsis. Genome Research, 2005, 15: 1274-1283.