As high throughput technologies continue to advance, various types of genomics data quickly accumulate to an unprecedented scale. The Cancer Genome Atlas (TCGA) project, as an example, has revealed large amount of tumor-related genome mutations, transcriptomics, epigenetics, proteomics, and metabolomics data. The wealth of data ignites the hope for the systematic investigation of cancer development molecular mechanisms and to identify driver mutations or drug targets for its treatment. However the pluralism of causes and heterogeneity makes cancer a Gordian knot with long expected unpicking. The current genomics data integration approaches typically reply on network representation considering all molecular components in a cell as nodes, and their functional relationships as edges, usually with weights assigned. The network representation allows observation of multiple components simultaneously, and by deploying mathematical models working on network topological structures and the dynamics, people could explore and validate hypotheses of cancer development mechanisms through in silico studies. In this talk, I will discuss both achievement and limitations of cancer genomics network analysis. I will introduce our effort of bringing natural selective pressure into network analysis. Preliminary results from a simple model organism, E. coli, show that evolutionary persistence is the driving force of network modules, and determines regulation pattern of gene functional co-expression. You will also see further comparisons between naturally evolved biological networks and man-made systems, specifically, the gene regulatory network of E. coli compared to the call graph network of the Linux operating system. We conclude through such comparisons that modularity underlies the most of network robustness and its regulation dynamics. I will discuss the importance of focusing on network topological structures in the studies of cancer and other diseases.
NYU Shanghai STEM seminar series is a weekly seminar series on every Wednesday, starting from 12th October 2016.
Please see below tentative schedule of STEM seminar series in 2017 Spring Semester.
- Feb. 8: NO SEMINAR
- Feb. 15: Li Li, Associate Professor of Neural Science and Psychology
- Feb. 22: Xiao-Jing Wang, Global Professor of Neural Science
- Mar. 1: Hanghui Chen, Assistant Professor of Physics
- Mar. 8: Vladas Sidoravicius, Professor of Mathematics
- Mar. 15: Keith Ross, Professor of Engineering and Computer Science
- Mar. 22: Olivier Marin, Associate Professor of Computer Science
- Mar. 29: Xinying Cai, Assistant Professor of Neural and Cognitive Sciences
- Apr. 5: Spring Recess, NO SEMINAR
- Apr. 12: Jungseog Kong, Assistant Professor of Biology
- Apr. 19: Gang Fang, Assistant Professor of Biology
- Apr. 26: Pierre Michael Tarres, Visiting Professor of Mathematics
- May 3: Jeff Erlich, Assistant Professor of Neural and Cognitive Sciences
- May 10: Laurent Mertz, Visiting Assistant Professor of Mathematics
- May 17: NO SEMINAR