Analyzing Sensitivity in Complex Networks Using Information Theory
Author Information
Author(s): Niklas Lüdtke, Stefano Panzeri, Martin Brown, David S. Broomhead, Joshua Knowles, Marcelo A. Montemurro, Douglas B. Kell
Primary Institution: The University of Manchester
Hypothesis
Can an information-theoretic approach effectively analyze sensitivity in complex networks?
Conclusion
The study presents a novel methodology for sensitivity analysis in complex networks that quantifies the influence of individual inputs on outputs using mutual information.
Supporting Evidence
- The methodology successfully identifies key parameters influencing the NFκB signaling pathway.
- The study demonstrates that mutual information can effectively quantify input-output relationships in complex systems.
- Higher order interactions were found to contribute significantly to the overall sensitivity of the system.
Takeaway
This study shows how to figure out which parts of a complex system are most important by looking at how changes in inputs affect the outputs.
Methodology
The authors used a model of the NFκB signaling pathway and performed sensitivity analysis by randomly sampling input parameters and calculating mutual information.
Potential Biases
Potential biases in estimating mutual information due to limited sampling and the complexity of the input-output relationships.
Limitations
The methodology may struggle with high-dimensional data and requires careful handling of input correlations.
Digital Object Identifier (DOI)
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