Computational Biology Platform Technology for Cellular Reprogramming

Methods for interconversion between cell types (cellular reprogramming) are currently discovered through resource intensive trial and error. Experiments may test a multitude of transcription factors to identify correct combinations that influence cell fate. In addition, reprogramming approaches commonly use stem cell intermediates such as induced pluripotent stem cells (iPSCs), which are generated with low efficiency and high cost. Conversion of fibroblasts into a variety of lineages, including muscle cells, neurons, and cardiomyocytes, has been successful without a pluripotent intermediate, supporting direct reprogramming as a viable strategy in many settings. Given the current methodological inefficiencies and deficiencies in discovering reprogramming methods, we propose to improve discovery of new direct reprogramming schemes by analyzing high-throughput sequencing data with control theory techniques. Our approach models the cell state as a dynamical system, using time series genome form and function data (Hi-C and RNA-sequencing) to determine the optimal transcription factor combinations and timing to achieve reprogramming. Our goals are to provide a TRL4 compliant computational biology platform for identifying key determinants (genes, time windows) underlying cellular reprogramming, and to harness genomics data gathered in established settings of reprogramming to refine our existing data-guided cellular reprogramming algorithm.