Volen Center for Complex Systems 135
MS 018, Brandeis University
Waltham, MA 02453
Openings: I am recruting PhD students to work with me on Big Data Analytics in interdisciplinary fields, such as, Bioinformatics, Glycomics (Glycan mass spec analysis), Biomedical Informatics, and so on. Candidates with strong mathematical background and programming skills are preferred.
Glycomics -- the new frontier: coming soon ...
High-Content Neuronal Analysis: Cell-based high-content screening is becoming a widely used high-throughput methodology in therapeutic drug discovery and functional genomics. One of the challenges in high-content screens is to reliably and automatically analyze a large quantity of high-content images. Images of neuronal cell cultures are particularly challenging to analyze because of their complex morphology. My group has developed a robust pipeline for quantifying and comparing the morphology of neuronal cell cultures (Wu et al 2010). Our analysis pipeline has been a key innovation for high-throughput neuronal screening and has been applied to several large-scale high-content screening projects (Sepp et al. 2008, Wu et al. 2010, Schoemans et al. 2010, Schulte et al. 2011). Our computational results have led to successful follow-up studies (Sepp et al 2008, Schulte et al 2010). We are also developing a novel computing paradigm that integrates unsupervised pattern mining techniques, visual data exploration interfaces and interactive image analysis techniques to facilitate the application of the HCS technology to biomedical research. A prototype, called imCellPhen -- interactive mining of cellular phenotypes, has been realized (details).
Computational Systems Biology: Animal organogenesis is a complicate process controlled by a network of intercellular signaling, intracellular signal transduction, and transcriptional regulation. The construction of quantitative models for this network can provide insights into the underlying biological mechanisms. We have developed a mathematical model to model the biological network that governs C. elegans vulval induction. The model was automatically learned from heterogeneous biological data and is capable of simulating vulval induction under various different genetic conditions. (details). In another ongoing project, we inferred a large-scale multi-cellular regulatory network that contains 2000+ cells and governs the development of Drosophila eye (details) from high-content images and other biological data. At the same time, we are generating a comprehensive signaling network in Drosophila by using tandem affinity purification/mass spectrometry protein-protein interaction mapping (details).
- Kwon Y., Vinayagam A., Sun X., Dephoure N., Gygi S.P., Hong P., and Perrimon P. (2013). The Hippo Signaling Pathway Interactome. Science.
- Bei and Hong (2013). A Novel Approach to Minimize False Discovery Rate in Significance Analysis. BMC Systems Biology.
- Sun, Hong, Kulkarni, Kwon, and Perrimon (2013). PPIRank – An Advanced Method for Ranking Protein-Protein Interations in TAP/MS Data. BMC Proteome Science.
- Bei and Hong (in press) Psychiatric Insights into Liver Cirrhosis and Their Correlations with Traditional Chinese Medicine Diagnostics. BIBM 2013. Congratulation to Bei for his BIBM Student Travel Award.