ABI: Innovation: Computationally uncovering dynamic transcription factor interactions within and across organisms

点击翻译按钮获取中文摘要

This project entails the development of new computational methods to uncover the dynamic variation of transcriptional regulatory networks. While nearly all cells within an organism have the same DNA, they can exhibit very different characteristics as different genes are turned on, or expressed, within them. Transcription factor interactions, comprising regulatory networks, control which genes are expressed, and thus the dynamics of these interactions across cells, conditions and organisms are a critical feature of proper biological functioning. To date, however, most existing knowledge about regulatory networks is static in nature: for nearly all organisms, transcription factor interactions are known under only a small number of conditions of interest. To help fill this gap and begin to uncover the dynamic nature of transcriptional regulatory networks, novel computational approaches will be developed to predict and compare condition-specific transcriptional interactions within an organism and varying transcriptional interactions across organisms. Software for these tasks will be released and made available to the broader scientific community. Additionally, significant new outreach efforts will be undertaken to recruit a diverse group of students to take part in this research.More specifically, new computational approaches will be developed to uncover and differentially analyze condition-specific and cross-organism variation both in regulatory interactions between transcription factors and their genomic targets, as well as in interactions amongst regulators themselves, as these interactions are a central mechanism by which regulatory specificity is achieved. The approaches will leverage large numbers of transcription factors with known binding specificities, existing chromatin accessibility data across numerous conditions that reveal which portions of a genome are accessible to be bound by transcription factors in those conditions, and comparative analysis of multiple closely related fully sequenced organisms. The results of this research will be disseminated at http://compbio.cs.princeton.edu.