学术报告
题目：Reverse Engineering Dynamic Gene Networks Underlying Breast Cancer Cell Lineages and Yeast Cell Cycles
报告人：Wei Wu, PhD
Associate Research Professor
School of Computer Science
Carnegie Mellon University
时间：1月4日（周三），2:00-3:30 PM
地点：生命学院610室
Seminar Abstract:
Estimating gene regulatory networks over biological lineages or time series is central to a deeper understanding of how cells evolve during development and differentiation. One challenge in estimating such evolving networks is that their host cells are not only contiguously evolving, but also can branch over time. For example, a biologist may apply several different drugs to a malignant cancer cell to analyze the changes each drug has produced in the treated cells. Cells treated with one drug are not directly related to cells treated with another drug, but rather to the malignant cancer cells that they were derived from. Underlying these intriguing dynamic systems, one expects that the interactions between genes are not always constant over time, but rather they are often transient; in other words, gene-gene interactions occur during a time interval may disappear and then reappear again later in time. This challenging behavior renders existing network inference methods inapplicable.
We proposed two novel approaches, Treegl and TV-DBN, which build on the L1 plus time-dependent penalized graphical logistic regression to effectively estimate multiple evolving gene networks corresponding to cell types related by a tree-genealogy, or cell stages related by a evolving chain, based on only a few samples from each condition. Our methods take advantage of the similarity between related networks along the biological lineage, while at the same time exposing sharp differences between the networks. We explore applications to analysis of a breast cancer development, and yeast cell cycle regulation. Based on only a few microarray measurements, our algorithms are able to produce biologically valid results that provide insight into the progression and reversion of breast cancer, and transient interactions among genes in yeast cell cycle.
Short Bio of Speaker:
Dr. Wei Wu is an associate research professor at the School of Computer Science at Carnegie Melllon University. Previously, she was a faculty member at the Division of Pulmonary, Allergy, and Critical Care Medicine at the University of Pittsburgh. Her research focuses on understanding complex human diseases by undertaking integrative approaches, which combine biology, computational and statistical learning, bioinformatics, and genomics. Dr. Wu received a Ph.D. in Computational Molecular Biology from the joint graduate program of Rutgers University and UMDNJ. She also received a M.S. in Computer Science at the University of California at Santa Cruz, where she worked on the construction of the Human Genome Browser with Professor David Haussler. She later did a postdoctoral training at Lawrence Berkeley National Lab with Dr. Mina Bissell. Her current work involves: i) understanding breast cancer mechanisms using dynamic network learning approaches, and ii) subphenotyping asthma patients using computational approaches. Her paper on developing a tree-varying network learning approach for analyzing a breast cancer progression series of cells has won the Best Paper Award at the ISMB conference this year. She is a co-Principle Investigator on two NIH R01 awards.
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