Abstract:

Causal inference is crucial in biological research, as it enables the understanding of complex relationships and dynamic processes that drive cellular behavior, development, and disease. Within this context, gene regulatory network (GRN) inference serves as a key approach for understanding the molecular mechanisms underlying cellular function. Despite significant advancements, challenges persist in GRN inference, particularly in dynamic rewiring, inferring causality, and context specificity. To tackle these issues, we present SiCNet, a novel causal network construction method that utilizes single-cell gene expression profiles and a causal inference strategy to construct molecular regulatory networks at a single cell level. Additionally, SiCNet utilizes cell-specific network information to construct network outdegree matrix (ODM), enhancing the performance of cell clustering. It also enables the construction of context-specific GRNs to identify key regulators of fate transitions for diverse processes such as cellular reprogramming and development. Furthermore, SiCNet can delineate the intricate dynamic regulatory processes involved in development, providing deep insights into the mechanisms governing cellular transitions and the gene regulation across developmental stages.

Time：2025/05/17 8:00-12:00

Location：1421 Beiheng Building