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SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data.

Steven Woodhouse1,2,3, Nir Piterman4, Christoph M Wintersteiger3

  • 1Department of Hematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 0XY, UK.

BMC Systems Biology
|May 27, 2018
PubMed
Summary
This summary is machine-generated.

The Single Cell Network Synthesis toolkit (SCNS) reconstructs executable models from single-cell gene expression data. This tool helps biologists understand cell behavior and predict gene perturbation effects without specialized computational knowledge.

Keywords:
Developmental biologyExecutable biologyGene regulatory networksSingle cell

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Single-cell gene expression data analysis is crucial for understanding cellular processes.
  • The Human Cell Atlas initiative will generate vast amounts of single-cell data.
  • Biologists require accessible computational tools for analyzing complex gene regulatory networks.

Purpose of the Study:

  • To introduce the Single Cell Network Synthesis toolkit (SCNS).
  • To provide a user-friendly computational tool for reconstructing executable models from single-cell gene expression data.

Main Methods:

  • SCNS utilizes a graphical user interface.
  • It processes single-cell qPCR or RNA-sequencing data across a time course.
  • The toolkit identifies logical rules governing cell state transitions.

Main Results:

  • SCNS reconstructs executable mechanistic models from single-cell data.
  • These models can predict the impact of gene perturbations on cell lineage development.
  • The tool facilitates the analysis of regulatory networks underlying cell behavior.

Conclusions:

  • SCNS is a valuable tool for researchers in single-cell genomics.
  • It enables deeper mechanistic insights into development, homeostasis, and disease.
  • The toolkit empowers biologists to leverage large-scale single-cell datasets effectively.