Project Summary/Abstract The study of gene regulation provides fundamental mechanistic knowledge to decipher the development of com- plex biological systems and design treatment strategies for diseases by manipulating cell states. Development of single-cell multimodal and multiomics sequencing technologies allows for profiling of additional epigenomics information, such as cis-regulatory elements, which enables inference of gene regulation beyond simply gene coexpression. However, to more accurately identify gene regulation, computational methods are urgently needed to incorporate both transcriptomics and epigenomics data measured in the same cell. Recent studies show that epigenomics changes may foreshadow gene transcription, indicating that gene regulation is an asynchronous process. A set of regulators may not influence the target gene along the whole continuous biological process but only in a small time window. However, current gene regulatory networks inferred from time series data provide average regulation across time rather than characterizing regulation dynamics. Also, prior evidence shows that cell–cell communication and interaction are present in spatially localized cell populations as a form of regular- ized activity. Further incorporating time-varying developmental trajectories in tissue-spatial landscapes will help categorize the spatial heterogeneity of gene regulation. However, we still lack computational methods to incor- porate spatio-temporal trajectories in gene regulation inference. To close these gaps, my research program will (1) develop computational methods and statistical approaches to infer both static and dynamic gene regulatory networks, (2) leverage the spatial trajectories inferred from multiomics and spatial transcriptomics data to charac- terize spatial gene regulation, and (3) develop control strategies for gene regulatory networks to achieve desired states using multi-sample single-cell multiomics data. My long-term goal is to characterize gene regulation across multiple samples and in spatially separated tissue regions and identify the minimum set of driver genes to manip- ulate cell states (e.g., cell types). Building on my previous experience in gene regulatory network inference and methods development to analyze multi-sample single-cell genomics and epigenomics data, I will develop novel statistical methods to infer gene regulatory networks using multi-sample single-cell multiomics data and char- acterize time-lagged activities. I will build on my recent work on single-cell pseudotime analysis and single-cell spatio-temporal trajectories to infer gene regulation in temporal and spatio-temporal contexts. Further, building on my theoretical research in network control and methods development in analyzing multi-sample single-cell omics data, I will develop control strategies for gene regulatory networks to achieve desired cell states or cell de- velopmental trajectories with a minimum set of driver genes. Finally, I will systematically evaluate these methods across tissues and sample conditions using publicly available data. All methods will be generally applicable in different tissue or disease contexts. These methods will be released to the community with publicly and freely available open-source software that can benefit the broad scientific community.

Project Narrative How gene expression is regulated can inform the development of complex biological systems and help design treatment strategies for diseases by manipulating cell states; however, to accurately infer gene regulatory net- works, we urgently need to develop robust computational methods that incorporate rich transcriptomics and epigenomics data measured in single cells. This research program will develop computational methods for in- ferring and analyzing gene regulatory networks using multi-sample single-cell multiomics and spatial genomics data, use spatial trajectories from spatial transcriptomics data to characterize spatio-temporal gene regulation, and build network control strategies to manipulate networks to desired states (such as cellular healthy states). This research is expected to significantly reduce the wasted resources in downstream validation experiments and reduce the cost in disease treatment.