title: Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions.
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.
authors.affiliation: Department of Genetics, Cell Biology, and Anatomy, University of Nebraska Medical Center, Omaha NE 68198, USA. nkochi@unomaha.edu.
authors.forename: Naomi
authors.initials: N
authors.lastname: Kochi
authors.affiliation: Department of Mathematics, University of Nebraska at Omaha, Omaha NE 68182, USA. thelikar2@unl.edu.|Department of Biochemistry, University of Nebraska-Lincoln, Lincoln NE 68588, USA. thelikar2@unl.edu.
authors.forename: Tomáš
authors.initials: T
authors.lastname: Helikar
authors.affiliation: Department of Mathematics, University of Nebraska at Omaha, Omaha NE 68182, USA. lallen@unomaha.edu.
authors.forename: Laura
authors.initials: L
authors.lastname: Allen
authors.affiliation: Department of Mathematics, University of Nebraska at Omaha, Omaha NE 68182, USA. jrogers@unomaha.edu.
authors.forename: Jim A
authors.initials: JA
authors.lastname: Rogers
authors.affiliation: Department of Mathematics, University of Nebraska at Omaha, Omaha NE 68182, USA. zhenyuanwang@unomaha.edu.
authors.forename: Zhenyuan
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Mathematics, University of Nebraska at Omaha, Omaha NE 68182, USA. dmatache@unomaha.edu.
authors.forename: Mihaela T
authors.initials: MT
authors.lastname: Matache
mesh_terms: D003198:Computer Simulation; D008954:Models, Biological; D060066:Protein Interaction Maps / Q000502:physiology*; D015398:Signal Transduction / Q000502:physiology*; D049490:Systems Biology / Q000379:methods*