UC San Diego

Advancements in next-generation sequencing technologies have fueled the development of high throughput profiling assays. The sheer volume of data generated by these experiments grants us an unprecedented opportunity to deepen our understanding of complex biological systems, but also raise many computational challenges. With multi-omics data becoming very common in modern biological research, one of the most urgent tasks is to develop novel algorithms to perform integrated data analyses of these data. In the thesis, we developed network-based frameworks to integrate a variety of omics data. We combined the strength of different high throughput assays and analyzed multi-omics data to infer molecular interactions and build regulatory networks. Using network-based approaches, we addressed two important biological problems: identifying protein complexes mediating the formation of chromosome loops and identifying driver TFs in different biological processes. We further conducted computational and biological experiments to validate our findings. Our study provides new insights into the processes of CD8+ T cell development and mouse embryogenesis. The identified driver TFs can be used as the blueprint for future mechanistic study.