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

BMC Systems Biology

Mihaela T Matache³  Zhenyuan Wang³  Jim A Rogers³  Laura Allen³  Tomáš Helikar¹  Naomi Kochi² 

[1] Department of Biochemistry, University of Nebraska-Lincoln, Lincoln NE 68588, USA;Department of Genetics, Cell Biology, and Anatomy, University of Nebraska Medical Center, Omaha NE 68198, USA;Department of Mathematics, University of Nebraska at Omaha, Omaha NE 68182, USA

关键词: Sensitivity;    Choquet integral;    Nonadditive set functions;    Signal transduction;    Node attributes;    Information fusion;   

Others  :  1127081
DOI  :  10.1186/s12918-014-0092-4

 received in 2013-10-09, accepted in 2014-07-21,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Kochi et al.; licensee BioMed Central Ltd.

【 图 表 】

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