title: Reduction of Boolean network models.
abstract: Boolean networks have been successfully used in modelling gene regulatory networks. However, for large networks, analysis by simulation becomes unfeasible. In this paper we propose a reduction method for Boolean networks that decreases the size of the network, while preserving important dynamical properties and topological features. As a result, the reduced network can be used to infer properties about the original network and to gain a better understanding of the role of network topology on the dynamics. In particular, we use the reduction method to study steady states of Boolean networks and apply our results to models of Th-lymphocyte differentiation and the lac operon.
authors: Department of Mathematics, University of Nebraska-Lincoln, USA. aveliz-cuba2@unl.edu
authors: Alan
authors: A
authors: Veliz-Cuba
mesh_terms: D000465:Algorithms; D002454:Cell Differentiation / Q000276:immunology; D025461:Feedback, Physiological; D053263:Gene Regulatory Networks / Q000502:physiology*; D006801:Humans; D007763:Lac Operon / Q000235:genetics; D008954:Models, Biological; D015398:Signal Transduction / Q000276:immunology; D049490:Systems Biology / Q000379:methods; D013601:T-Lymphocytes / Q000166:cytology