Open Access

Computational approaches for predicting key transcription factors in targeted cell reprogramming (Review)

  • Authors:
    • Guillermo‑Issac Guerrero‑Ramirez
    • Cesar‑Miguel Valdez‑Cordoba
    • Jose‑Francisco Islas‑Cisneros
    • Victor Trevino
  • View Affiliations

  • Published online on: May 29, 2018     https://doi.org/10.3892/mmr.2018.9092
  • Pages: 1225-1237
  • Copyright: © Guerrero‑Ramirez et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

There is a need for specific cell types in regenerative medicine and biological research. Frequently, specific cell types may not be easily obtained or the quantity obtained is insufficient for study. Therefore, reprogramming by the direct conversion (transdifferentiation) or re‑induction of induced pluripotent stem cells has been used to obtain cells expressing similar profiles to those of the desired types. Therefore, a specific cocktail of transcription factors (TFs) is required for induction. Nevertheless, identifying the correct combination of TFs is difficult. Although certain computational approaches have been proposed for this task, their methods are complex, and corresponding implementations are difficult to use and generalize for specific source or target cell types. In the present review four computational approaches that have been proposed to obtain likely TFs were compared and discussed. A simplified view of the computational complexity of these methods is provided that consists of three basic ideas: i) The definition of target and non‑target cell types; ii) the estimation of candidate TFs; and iii) filtering candidates. This simplified view was validated by analyzing a well‑documented cardiomyocyte differentiation. Subsequently, these reviewed methods were compared when applied to an unknown differentiation of corneal endothelial cells. The generated results may provide important insights for laboratory assays. Data and computer scripts that may assist with direct conversions in other cell types are also provided.
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August-2018
Volume 18 Issue 2

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Spandidos Publications style
Guerrero‑Ramirez GI, Valdez‑Cordoba CM, Islas‑Cisneros JF and Trevino V: Computational approaches for predicting key transcription factors in targeted cell reprogramming (Review). Mol Med Rep 18: 1225-1237, 2018
APA
Guerrero‑Ramirez, G., Valdez‑Cordoba, C., Islas‑Cisneros, J., & Trevino, V. (2018). Computational approaches for predicting key transcription factors in targeted cell reprogramming (Review). Molecular Medicine Reports, 18, 1225-1237. https://doi.org/10.3892/mmr.2018.9092
MLA
Guerrero‑Ramirez, G., Valdez‑Cordoba, C., Islas‑Cisneros, J., Trevino, V."Computational approaches for predicting key transcription factors in targeted cell reprogramming (Review)". Molecular Medicine Reports 18.2 (2018): 1225-1237.
Chicago
Guerrero‑Ramirez, G., Valdez‑Cordoba, C., Islas‑Cisneros, J., Trevino, V."Computational approaches for predicting key transcription factors in targeted cell reprogramming (Review)". Molecular Medicine Reports 18, no. 2 (2018): 1225-1237. https://doi.org/10.3892/mmr.2018.9092