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Algorithm for cellular reprogramming

  1. Indika Rajapaksea,f,1
  1. aDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI 48109;
  2. bDepartment of Curriculum Design, IXL Learning, Raleigh, NC 27560;
  3. cDepartment of Pediatrics and Communicable Diseases, University of Michigan, Ann Arbor, MI 48109;
  4. dDepartment of Biological Sciences, University of Maryland, College Park, MD 20742;
  5. eDepartment of Hematology/Oncology, University of Michigan, Ann Arbor, MI 48109;
  6. fDepartment of Mathematics, University of Michigan, Ann Arbor, MI 48109;
  7. gJohn A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138
  1. Edited by Steven Henikoff, Fred Hutchinson Cancer Research Center, Seattle, WA, and approved September 26, 2017 (received for review July 14, 2017)

Significance

Reprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

Abstract

The day we understand the time evolution of subcellular events at a level of detail comparable to physical systems governed by Newton’s laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology. With data-guided frameworks we can develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. Here we describe an approach for optimizing the use of transcription factors (TFs) in cellular reprogramming, based on a device commonly used in optimal control. We construct an approximate model for the natural evolution of a cell-cycle–synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points during the cell cycle. To arrive at a model of moderate complexity, we cluster gene expression based on division of the genome into topologically associating domains (TADs) and then model the dynamics of TAD expression levels. Based on this dynamical model and additional data, such as known TF binding sites and activity, we develop a methodology for identifying the top TF candidates for a specific cellular reprogramming task. Our data-guided methodology identifies a number of TFs previously validated for reprogramming and/or natural differentiation and predicts some potentially useful combinations of TFs. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes.

Footnotes

  • ?1To whom correspondence should be addressed. Email: indikar{at}umich.edu.
  • Author contributions: R.B. and I.R. designed research; S.R., G.P., A.B., R.B., and I.R. performed research; S.R., G.P., S.L., H.C., M.B., M.S.W., A.B., R.B., and I.R. analyzed data; and S.R., G.P., L.A.M., S.L., A.B., R.B., and I.R. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • This article contains supporting information online at www.danielhellerman.com/lookup/suppl/doi:10.1073/pnas.1712350114/-/DCSupplemental.

This is an open access article distributed under the PNAS license.

Online Impact

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