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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Profile likelihood in systems biology.

Clemens Kreutz1, Andreas Raue, Daniel Kaschek

  • 1Physics Department, University of Freiburg, 79104 Freiburg, Germany. ckreutz@fdm.uni-freiburg.de

The FEBS Journal
|April 16, 2013
PubMed
Summary
This summary is machine-generated.

Systems Biology uses mathematical models to understand biological processes. This study reviews profile likelihood for assessing uncertainty in these models, demonstrating its use with an erythropoietin (EPO) receptor model.

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Area of Science:

  • Systems Biology
  • Mathematical Modeling
  • Computational Biology

Background:

  • Systems Biology relies on mathematical models to interpret biological processes.
  • Limited experimental data and noisy measurements necessitate robust methods for uncertainty quantification in models.
  • Complex biological systems often result in weakly specified model components due to sparse data.

Purpose of the Study:

  • To review the profile likelihood concept for assessing uncertainty in mathematical models.
  • To demonstrate the application of profile likelihood for parameter and prediction uncertainty in Systems Biology.
  • To evaluate the utility of profile likelihood using a model of the erythropoietin (EPO) receptor.

Main Methods:

  • Review of the profile likelihood statistical method.
  • Application of profile likelihood to a mathematical model of the erythropoietin (EPO) receptor.
  • Assessment of parameter and prediction uncertainty derived from the model.

Main Results:

  • Profile likelihood provides a robust framework for quantifying uncertainty in Systems Biology models.
  • The method effectively translates experimental uncertainty to model parameters and predictions.
  • Demonstrated successful application to the erythropoietin (EPO) receptor model.

Conclusions:

  • Profile likelihood is a valuable tool for uncertainty assessment in Systems Biology.
  • This approach enhances the reliability of predictions from complex mathematical models.
  • The study highlights the potential of profile likelihood for analyzing biological systems with limited data.