title: Modeling and analyzing complex biological networks incooperating experimental information on both network topology and stable states.
abstract: BACKGROUND
  Linking the topology of a complex network to its long-term behavior is a basic problem in network theory, which has been on the focus of many recent research publications. To obtain a suitable Boolean model for a biological system, one must analyze the initial model and compare it with other experimental evidence, and if necessary, make adjustments by changing the topology of the wiring diagram. However, our knowledge on how to link the topology of a network to its long-term behavior is very limited due to the complexity of the problem. Since the need to consider complex biological networks has become ever greater, develop both theoretical foundation and algorithms for model selection and analysis has been brought to the forefront of biological network study.
  
  
  RESULTS
  This article proposes a novel method to study intrinsically the relationship between experimental data and the possible Boolean networks, which can be used to model the underlying system. Simple and easy to use criteria for a Boolean network to have both a given network topology and a given set of stable states are derived. These criteria can be used to guide the selection of a Boolean network model for the system, as well as to gain information on the intrinsic properties, such as the robustness and the evolvability, of the system. A Boolean model for the fruit fly Drosophila melanogaster is used to explain the method.
authors.affiliation: Department of Mathematical Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA. ymzou@uwm.edu
authors.forename: Yi Ming
authors.initials: YM
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D000818:Animals; D004331:Drosophila melanogaster / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D049490:Systems Biology