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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial Gene Control

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data.

Andrea Ocone1, Laleh Haghverdi1, Nikola S Mueller1

  • 1Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany and Department of Mathematics, Technical University Munich, 85747 Garching, Germany.

Bioinformatics (Oxford, England)
|June 15, 2015
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Summary

This study introduces a new computational framework to reconstruct gene expression dynamics from single-cell snapshot data. The method recovers temporal information, enabling the reverse engineering of gene regulatory networks and cellular differentiation pathways.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Systems Biology

Background:

  • High-dimensional single-cell snapshot data are crucial for understanding cellular mechanisms.
  • Existing data lack temporal information, limiting mathematical models to static biological features.

Purpose of the Study:

  • To develop a computational framework for recovering temporal dynamics from single-cell snapshot data.
  • To enable the reverse engineering of gene expression and regulatory network structures.

Main Methods:

  • A modular framework combining dimensionality reduction and cell time-ordering algorithms.
  • Generation of pseudo time-series observations from static single-cell data.
  • Learning transcriptional Ordinary Differential Equation (ODE) models and performing network structure model selection.

Main Results:

  • Successfully recovered temporal behavior and gene expression dynamics from synthetic and real hematopoietic stem cell data.
  • Reconstructed gene expression dynamics during differentiation pathways.
  • Inferred the structure of a key gene regulatory network.

Conclusions:

  • The developed framework effectively reconstructs dynamic biological processes from static single-cell data.
  • This approach advances the understanding of gene regulatory networks and cellular differentiation.