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Related Concept Videos

Morphogenesis02:19

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A global sensitivity analysis approach for morphogenesis models.

Sonja E M Boas1,2, Maria I Navarro Jimenez3, Roeland M H Merks4,5

  • 1Life Sciences, CWI, Science Park 123, Amsterdam, 1098XG, The Netherlands. boas@cwi.nl.

BMC Systems Biology
|November 22, 2015
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Summary
This summary is machine-generated.

Global sensitivity analysis offers a new way to understand how parameters affect developmental processes like morphogenesis. This method helps identify key factors and interactions in complex models, potentially simplifying them and aiding experimental validation.

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Area of Science:

  • Developmental Biology
  • Computational Biology
  • Systems Biology

Background:

  • Morphogenesis involves complex, multi-factorial models often treated as 'black boxes'.
  • Understanding input-output relationships and parameter uncertainty in these models is challenging.
  • Sensitivity analysis tools are needed to dissect the influence of individual parameters and their interactions.

Purpose of the Study:

  • Introduce a workflow for global sensitivity analysis in morphogenesis models.
  • Assess the impact of single parameters and their interactions on model output.
  • Provide a method to enhance understanding of complex biological systems.

Main Methods:

  • Developed and applied a global sensitivity analysis workflow.
  • Utilized a published cellular Potts model (CPM) of vascular morphogenesis.
  • Analyzed the impact of parameters representing cell properties and behaviors.

Main Results:

  • Successfully identified dominant parameters in the vascular morphogenesis model, aligning with prior research.
  • Quantified the relative impact of single parameters and their interactions.
  • Discovered parameter interactions that offer new insights into in silico sprouting mechanisms.
  • Indicated potential model reduction by one parameter.

Conclusions:

  • Global sensitivity analysis is a viable approach for studying morphogenesis mechanisms.
  • Comparing parameter impact rankings with experimental data can help falsify models.
  • The workflow is adaptable to various 'black-box' models, including high-throughput in vitro systems.