Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions

Naomi Kochi, Tomáš Helikar, Laura Allen, Jim A. Rogers, Zhenyuan Wang, Mihaela T. Matache

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Background: An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network. Results: We find that for a biochemical signal transduction network consisting of several main signaling pathways whose nodes represent signaling molecules (mainly proteins), the algebraic method provides a robust classification of attribute contributions. This method indicates that for the biochemical network, the most significant impact is generated mainly by the combined effects of two attributes: out-degree, and average sensitivity of nodes. Conclusions: The results support the idea that both topological and dynamical properties of the nodes need to be under consideration. The algebraic method is robust against the choice of initial conditions and partition of data sets in training and testing sets for estimation of the nonadditive set functions of the information fusion procedure.

Original languageEnglish (US)
Article number92
JournalBMC systems biology
Volume8
Issue number1
DOIs
StatePublished - Sep 5 2014

Fingerprint

Boolean Networks
Information Services
Information fusion
Information Fusion
Biological Networks
Sensitivity analysis
Sensitivity Analysis
Hamming distance
Signal transduction
Boolean functions
Algebraic Methods
Vertex of a graph
Attribute
Proteins
Molecules
Mutation
Testing
Biochemical Networks
Average Distance
Signal Transduction

Keywords

  • Choquet integral
  • Information fusion
  • Node attributes
  • Nonadditive set functions
  • Sensitivity
  • Signal transduction

ASJC Scopus subject areas

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions. / Kochi, Naomi; Helikar, Tomáš; Allen, Laura; Rogers, Jim A.; Wang, Zhenyuan; Matache, Mihaela T.

In: BMC systems biology, Vol. 8, No. 1, 92, 05.09.2014.

Research output: Contribution to journalArticle

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