Project Abstract Cell fate engineering, for example the directed differentiation of pluripotent stem cells or the direct conversion among somatic cell types, holds great promise to improve disease modeling, drug screening, and to lead to regenerative medicine therapies. 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 accurate and testable hypothesis from the mountains of data coming from single cell omics technologies. In the initial period of funding under the MIRA, we started to address these challenges by developing novel theoretical and computational methods to define cell type identity from single cell RNA- Seq (scRNA-Seq) data with an emphasis on developmental cell types that emerge during mesoderm development and subsequent commitment to lineages of the synovial joint. As part of this work, we generated scRNA-Seq data of the developing synovial joint, we adopted a pluripotent stem cell-to-chondrocyte differentiation protocol, and we invented a generally applicable platform for assessing cell type identity at the single cell level of resolution. We also developed a computational method to infer dynamic regulatory networks accurately and to integrate them with signaling pathways. Now, we propose to address the following unanswered questions and unmet challenges. First, we will substantially improve and extend our computational methods that assess the outcomes of cell fate engineering by extending them to more data types, and thus increasing the comprehensiveness of its results, and by predicting not only cell identity but function. Second, we will substantially improve and extend our regulatory network tools so that their predictions are statistically calibrated and so that they can be applied to chromatin accessibility and expression data simultaneously with the intention of discovering binding site motifs of orphan transcription factors. Third, we will devise and experimentally test 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. Finally, we will follow up on observations from applying our dynamic network tool to in vitro gastrulation that indicates that some signaling pathways influence differentiation more by re-wiring network topology than by directly impacting expression of effector target genes. Collectively meeting these goals will help to make cell fate engineering more reliable and controllable, and it will shed light on how signaling pathways and intracellular regulatory networks interact during development.