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Inferring signalling dynamics by integrating interventional with observational data.

Mathias Cardner1,2, Nathalie Meyer-Schaller3, Gerhard Christofori3

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.

Bioinformatics (Oxford, England)
|September 13, 2019
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Summary
This summary is machine-generated.

We developed a new computational method to infer cell signaling networks and signal progression using combined perturbation and time-series data. This approach helps understand complex biological processes like epithelial-mesenchymal transition (EMT).

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Inferring cell signaling networks typically requires extensive interventional, time-resolved perturbation experiments, which are often infeasible for large networks.
  • Commonly, researchers have access to either steady-state perturbation data or non-interventional time-series data, but integrating these is challenging.
  • Understanding the coordination of epithelial-mesenchymal transition (EMT) in murine mammary gland cells is crucial for developmental and cancer research.

Purpose of the Study:

  • To develop a novel computational method for inferring cell signaling networks and their dynamics.
  • To integrate disparate data types, specifically steady-state perturbation data and non-interventional time-series data.
  • To apply the method to understand the transcription factor and microRNA network coordinating EMT and its signal progression.

Main Methods:

  • Developed a method within the framework of nested effects models to integrate perturbation and non-interventional time-series data.
  • Formulated the model extension as an integer linear program solvable by heuristic algorithms.
  • Applied the method to RNA sequencing data from an EMT experiment.

Main Results:

  • Successfully inferred the cell signaling network and signal progression during EMT.
  • Validated parts of the inferred network using experimental methods like luciferase reporter assays.
  • The method efficiently infers signal progression and identifies the necessity of regulators at specific time points during EMT.

Conclusions:

  • The developed method effectively integrates different data types to infer complex signaling networks and their temporal dynamics.
  • This approach provides insights into the regulatory mechanisms of biological processes such as EMT.
  • The computational tool is available as an R package, facilitating its application in other research areas.