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Updated: Apr 19, 2026

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

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

Published on: October 19, 2021

Topological sensitivity analysis for systems biology.

Ann C Babtie¹, Paul Kirk¹, Michael P H Stumpf²

¹Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America

|December 17, 2014

Summary

This summary is machine-generated.

Mathematical models require careful statistical inference. This study introduces a computational framework to evaluate differential equation models in biology, assessing structural uncertainty for more reliable conclusions.

Keywords:

biological networks dynamical systems network inference robustness analysis

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Area of Science:

Computational Biology
Mathematical Modeling
Systems Biology

Background:

Mathematical models simplify complex natural systems.
Numerous models can fit biological data, leading to structural uncertainty.
Ignoring this uncertainty can bias analyses and yield misleading conclusions.

Purpose of the Study:

To develop a computational framework for evaluating differential equation models in biological systems.
To systematically assess vast sets of candidate models using experimental and prior knowledge.
To quantitatively evaluate the impact of model structure on inferences and predictions.

Main Methods:

Development of a computational framework for model evaluation.
Application of topological sensitivity analysis.
Systematic evaluation of differential equation models against experimental data and prior knowledge.

Main Results:

A method to quantitatively assess the dependence of model inferences on assumed structures.
Identification of biases and misleading conclusions arising from unaddressed structural uncertainty.
A systematic approach to navigate large sets of potential biological models.

Conclusions:

Structural uncertainty in mathematical models is a critical factor.
The developed framework enables robust evaluation of biological models.
Considering structural uncertainty is essential for accurate scientific conclusions in systems biology.