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RNA-based Reprogramming of Human Primary Fibroblasts into Induced Pluripotent Stem Cells
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Algorithm for cellular reprogramming.

Scott Ronquist1, Geoff Patterson2, Lindsey A Muir3

  • 1Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI 48109.

Proceedings of the National Academy of Sciences of the United States of America
|October 29, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a data-guided method to optimize transcription factor (TF) use for cellular reprogramming. It models cell dynamics to predict effective TF combinations for controlling biological processes.

Keywords:
cellular reprogrammingcontrol theorygenome architecturenetworkstime series data

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Cellular Dynamics

Background:

  • Understanding subcellular events quantitatively is crucial for advancing cell biology.
  • Data-guided frameworks offer improved prediction and control over cellular processes.
  • Transcription factors (TFs) play a key role in cellular reprogramming.

Purpose of the Study:

  • To develop an approach for optimizing transcription factor (TF) usage in cellular reprogramming.
  • To create a data-guided methodology for identifying effective TF candidates.
  • To predict novel TF combinations for enhanced control over biological processes.

Main Methods:

  • Constructed an approximate dynamical model of cell-cycle-synchronized human fibroblasts.
  • Clustered gene expression based on topologically associating domains (TADs) to simplify the model.
  • Integrated gene expression dynamics with TF binding and activity data to identify optimal TFs.

Main Results:

  • Identified several TFs with validated roles in reprogramming and natural differentiation.
  • Predicted potentially effective combinations of TFs for specific cellular reprogramming tasks.
  • Demonstrated the utility of dynamical models and data-guided approaches for biological control.

Conclusions:

  • Dynamical models and data-guided methodologies significantly enhance strategies for controlling biological processes.
  • The developed approach effectively identifies and predicts transcription factors for cellular reprogramming.
  • This work highlights the potential of mathematical and computational tools in advancing cell biology.