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Updated: May 7, 2026

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation
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Integrating fluorescent biosensor data using computational models.

Eric C Greenwald1, Renata K Polanowska-Grabowska, Jeffrey J Saucerman

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.

Methods in Molecular Biology (Clifton, N.J.)
|September 21, 2013
PubMed
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This tutorial demonstrates building computational models for interpreting FRET biosensor data. It shows how to simulate signaling networks, validate models with experimental data, and generate testable hypotheses for biological responses.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Fluorescence Resonance Energy Transfer (FRET)-based biosensors are crucial for measuring biological signaling dynamics.
  • Interpreting complex biosensor data often requires sophisticated computational approaches.
  • Existing methods may lack detailed guidance for constructing predictive models of cellular signaling.

Purpose of the Study:

  • To provide a comprehensive tutorial on building computational models for FRET biosensor data analysis.
  • To illustrate the process using a model of cyclic adenosine monophosphate (cAMP) production and protein kinase A (PKA) activation.
  • To empower biologists with tools for deeper investigation of biological responses.

Main Methods:

  • Defining computational models based on hypothesized signaling network structures and kinetic parameters.

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Last Updated: May 7, 2026

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation
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Published on: February 8, 2015

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09:30

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Published on: January 18, 2017

  • Simulating models using Virtual Cell software.
  • Acquiring, processing, and validating experimental FRET biosensor data.
  • Fitting computational model parameters to experimental data for enhanced predictive accuracy.

    • Main Results:

    • A detailed computational model for cAMP production and PKA activation was constructed and simulated.
    • The process of integrating experimental FRET data for model parameterization was successfully demonstrated.
    • The validated model was utilized to perform computational experiments interrogating the signaling network.

    Conclusions:

    • Computational modeling offers a powerful framework for integrating and interpreting FRET biosensor data.
    • Parameter fitting using experimental data significantly improves model predictive capabilities.
    • This approach enables the generation of testable hypotheses to elucidate complex biological signaling pathways.