A defining contribution of systems biology has been to reveal how cellular circuitry works to govern the state of a cell. Often, cell-state is determined by the activity of a small number of hyperconnected transcriptional regulators (TRs; e.g., transcription factors, (de)acetylases, (de)methylases, and other genes that act at the level of DNA to affect transcription). The activity of these TRs can be detected from the transcription of their targets, but doing so requires accurate gene regulatory networks (GRNs). The best way to construct GRNs is by combining computationally inferred networks with experimental perturbation data, but until recently this has not been feasible in human cells. As a first step in that direction, I undertook to perform a large-scale Transcriptional REgulator Knock-down (TREK), at two timepoints, in two cancer cell lines, at single-cell level, and to use the resulting data to improve our ability to infer the regulatory state of the cell. In all, I constructed regulons for over 900 TRs and described the dynamics both over time and across contexts. I have significantly improved our GRNs and, consequently, our ability to measure protein activity and identify cell-state regulators.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-x4yq-8m79 |
| Date | January 2021 |
| Creators | Tan, Xiangtian |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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