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Related Concept Videos

Cellular Differentiation00:57

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How does a complex organism such as a human develop from a single cell? It all starts from a single fertilized egg which gives rise to a vast array of cell types, such as nerve cells, muscle cells, and epithelial cells that characterize the adult? Throughout development and adulthood, cellular differentiation leads cells to assume their final morphology and physiology. Differentiation is the process by which unspecialized cells become specialized to carry out distinct functions.
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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Modeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks.

András Hartmann1, Srikanth Ravichandran1, Antonio Del Sol2

  • 1Computational Biology Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-Belval, Luxembourg.

Methods in Molecular Biology (Clifton, N.J.)
|May 8, 2019
PubMed
Summary

Understanding gene regulatory networks (GRNs) is key to controlling cell fate. This study presents computational methods to infer cell-specific GRNs, aiding cellular reprogramming and differentiation research.

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Area of Science:

  • Cellular Biology
  • Computational Biology
  • Systems Biology

Background:

  • Gene expression regulation is crucial for cellular function, but its complexity hinders global understanding.
  • Gene regulatory networks (GRNs) model gene-gene interactions, offering a systematic approach to study cellular behavior.
  • Precise knowledge of cell type-specific GRNs is vital for controlling cell differentiation and reprogramming, with potential clinical applications.

Purpose of the Study:

  • To describe computational methodologies for inferring cell type-specific gene regulatory networks.
  • To provide a systematic approach for studying gene regulation in the context of cellular differentiation and reprogramming.
  • To enable control of cell fates through precise knowledge of GRNs.

Main Methods:

  • Computational pipeline including gene expression data processing and prior knowledge network characterization.
  • Algorithm to remove non-cell type-specific edges and topological characterization of inferred networks.
  • Boolean network simulations to mimic cellular transitions and identify determinants of reprogramming/differentiation.

Main Results:

  • Development of tailor-made computational methodologies for cell fate control applications.
  • Inference of cell type-specific GRNs through a detailed computational pipeline.
  • Identification of key determinants driving cellular reprogramming and differentiation.

Conclusions:

  • The presented computational methods offer a robust framework for understanding and manipulating gene regulatory networks.
  • This approach facilitates precise control over cell fate, paving the way for potential clinical applications in regenerative medicine.
  • The study provides a strategy to identify critical factors influencing cellular transitions, advancing the field of systems biology.