It has been extensively described that the over-expression of sets of carefully selected transcription factors can induce a directed cell conversion between cell types. However, since there are a large number of both transcription factors and cell types the bottleneck has become correctly identifying which set of transcription factors is required for any given cell conversion. To alleviate this, we have developed Mogrify, a predictive system that combines gene expression data with regulatory network information to predict the reprogramming factors necessary to induce any cell conversion. Mogrify correctly predicts the transcription factors used in known transdifferentiations, has been used to perform new cell conversions and is available to the community at www.mogrify.net.

Here I will present two examples of the application of Mogrify; Firstly, how we have used the predictions to perform and experimentally validate novel cell conversions without the need for trial-and-error selection of the transcription-factor sets. Secondly, how we are adapting the algorithm to facilitate the identification of alternatives to transcription factor over-expression for driving cell conversion. Both of these examples taken together will demonstrate how taking a data-driven approach (such as Mogrify) can accelerate the field of cell conversion.