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Commitment is the  process whereby stem cells:
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Reprogramming Mouse Embryonic Fibroblasts with Transcription Factors to Induce a Hemogenic Program
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Computational blueprints for cell fate programming.

Pengyi Yang1

  • 1Computational Systems Biology Unit, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia; School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia; Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia; Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia.

Stem Cell Reports
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

Computational methods accelerate cell fate programming for disease modeling and drug discovery. This work synthesizes approaches into iterative pipelines for efficient protocol design.

Keywords:
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Area of Science:

  • Cellular reprogramming and synthetic biology.

Background:

  • Cell fate programming is crucial for disease modeling, drug discovery, and regenerative medicine.
  • Existing differentiation protocols face scalability challenges due to combinatorial complexity.
  • Computational methods are increasingly applied but adoption for protocol design is uneven.

Purpose of the Study:

  • To synthesize computational approaches for cell fate programming.
  • To present pragmatic computational blueprints for protocol design.
  • To establish an iterative design-test-learn pipeline for cell fate programming.

Main Methods:

  • Leveraging computational methods like cell annotation, network inference, and trajectory analysis.
  • Integrating single-cell and spatial omics, perturbation screens, and deep learning.
  • Developing pragmatic computational blueprints within an iterative framework.

Main Results:

  • Identification of transcription factors and small molecules for cell fate programming.
  • Expansion of predictive scope using advanced omics and deep learning techniques.
  • Addressing challenges in domain shift, interpretability, and reproducibility.

Conclusions:

  • Computational strategies offer pragmatic blueprints for cell fate programming.
  • An iterative design-test-learn pipeline enhances protocol development.
  • Synthesized approaches facilitate advancements in regenerative medicine and drug discovery.