This project will create a computational framework to help scientists understand the complex network of events inside living cells. Researchers can now measure thousands of different molecules at once, generating massive datasets. However, a major bottleneck is making sense of this data to uncover cause-and-effect relationships. Building these "causal models" is currently a slow, manual process that limits the pace of discovery. This research will automate the construction of these models, allowing for large-scale analysis of biological processes. The project will expand these automated methods to analyze data from individual cells and across different organisms. It will also integrate these causal models with advanced artificial intelligence techniques to improve biological predictions. By transforming how researchers analyze complex biological data, this work will accelerate discoveries across many fields. The project also contributes to education by developing two new university courses and training the next generation of data scientists to use cutting-edge methods for analyzing complex systems. The research will develop a computational infrastructure for interpreting high-throughput, multi-omic profiles through the lens of causal biological networks. The project will extend the CausalPath modeling framework to new domains, including the interpretation of single-cell omics trajectories and its application to model organisms through homology-based mapping. The explanator