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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Multi-element probabilistic collocation for sensitivity analysis in cellular signalling networks.

J Foo¹, S Sindi, G E Karniadakis

¹Division of Applied Mathematics, Brown University, Providence, RI 02912, USA.

IET Systems Biology

|July 31, 2009

Summary

This summary is machine-generated.

The multi-element probabilistic collocation method (ME-PCM) offers efficient sensitivity analysis for cellular signaling models. This tool precisely identifies key parameters influencing biological processes like apoptosis.

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Area of Science:

Systems Biology
Computational Biology
Biophysics

Background:

Differential equation models are crucial for understanding complex cellular signaling networks.
Sensitivity analysis helps identify critical parameters that influence model behavior.
Existing methods may lack efficiency or comprehensive parameter space coverage.

Purpose of the Study:

To formulate the multi-element probabilistic collocation method (ME-PCM) for sensitivity analysis of differential equation models.
To apply and validate ME-PCM on a cellular apoptosis signaling network model.
To demonstrate the method's capability in analyzing local sensitivities across the parameter space.

Main Methods:

Formulation of the multi-element probabilistic collocation method (ME-PCM).
Implementation of a simple and efficient sampling algorithm for local sensitivity quantification.
Application to an ordinary differential equation model of apoptosis signaling.

Main Results:

ME-PCM successfully quantified local sensitivities within the parameter space of the apoptosis network.
The method confirmed previously identified regions of sensitivity.
Detailed analysis revealed sensitivity concerning variations in chemical species over time and total species exposure.

Conclusions:

ME-PCM is a versatile and efficient tool for sensitivity analysis in cellular signaling models.
The method provides detailed insights into parameter influence on biological network dynamics.
ME-PCM demonstrates generality through analysis of various biologically relevant markers.