Computational Biology Platform Technology for Cell Conversion and Differentiation

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 genomics data to determine the optimal transcription factor combinations and timing to achieve reprogramming. Our objective in this Phase II effort is to refine our Computational Toolbox and develop a Biological Toolbox for systematically optimizing and validating predictions from our Computational Toolbox, yielding a Bio-Computational Platform that operates as a template for improving any cellular reprogramming system.