Project Abstract Cell fate engineering, for example the directed differentiation of pluripotent stem cells, holds great promise for disease modeling, drug screening, and regenerative medicine. However, our ability to engineering cell fate with fidelity has been impeded by an incomplete understanding of inter- and intracellular networks that govern differentiation, and by the lack of adequate computational tools to distill testable hypothesis from the mountains of data coming from single cell omics technologies. In the parent award of this grant, we are addressing the following unanswered questions and unmet challenges that emerged from our prior work. First, we are extending our computational methods that assess the outcomes of cell fate engineering to more data types, thus increasing the comprehensiveness of their results. Second, we are substantially improving and extending our regulatory network tools so that they are statistically calibrated and so that they can ingest chromatin accessibility and expression data simultaneously to discover binding site motifs of orphan transcription factors. Third, we are devising computational methods to generate reliable cell fate engineering recipes that account for not only transcriptional networks but also how signaling pathways inform them, and that account for temporal dynamics. The Imaging system that we are requesting in this supplement grant is crucial for us to experimentally assess the predictions that emerge from our computational algorithms to improve the directed differentiation of pluripotent stem cells to, first, mesoderm sub-types, and second, to articular chondrocytes. Meeting these goals will shed light on how signaling pathways and intracellular regulatory networks interact during differentiation, and it will help to make cell fate engineering more reliable and controllable.