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Information-theoretic sensitivity analysis: a general method for credit assignment in complex networks.

Niklas Lüdtke1, Stefano Panzeri, Martin Brown

  • 1School of Chemistry, University of Manchester, 131 Princess Street, Manchester M1 7DN, UK. niklas.ludtke@manchester.ac.uk

Journal of the Royal Society, Interface
|June 28, 2007
PubMed
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We developed a new method to quantify the importance of system inputs by treating networks as communication channels. This approach decomposes mutual information to reveal individual input effects and interactions, even with correlated inputs.

Area of Science:

  • Systems biology
  • Network analysis
  • Information theory

Background:

  • Many systems are modeled as networks with unknown governing equations.
  • Determining the influence of individual inputs on system outputs is a key challenge.

Purpose of the Study:

  • To develop a novel methodology for quantifying the importance of system inputs in networks.
  • To provide a framework for understanding input-output associations in complex systems.

Main Methods:

  • Representing systems as communication channels.
  • Decomposing mutual information between inputs and outputs.
  • Quantifying main effects of individual inputs and their interactions.

Main Results:

Related Experiment Videos

  • The proposed method effectively quantifies input importance in network systems.
  • Mutual information decomposition reveals contributions of individual inputs and their interactions.
  • The methodology accommodates correlated inputs, overcoming limitations of variance-based approaches.

    • Conclusions:

    • The communication channel approach offers a robust way to analyze network system dynamics.
    • This method enhances understanding of how individual components influence overall system behavior.
    • The ability to handle correlated inputs expands the applicability of information-theoretic analysis in systems science.