title: Template-based intervention in Boolean network models of biological systems.
abstract: BACKGROUND
  A grand challenge in the modeling of biological systems is the identification of key variables which can act as targets for intervention. Boolean networks are among the simplest of models, yet they have been shown to adequately model many of the complex dynamics of biological systems. In our recent work, we utilized a logic minimization approach to identify quality single variable targets for intervention from the state space of a Boolean network. However, as the number of variables in a network increases, the more likely it is that a successful intervention strategy will require multiple variables. Thus, for larger networks, such an approach is required in order to identify more complex intervention strategies while working within the limited view of the network's state space. Specifically, we address three primary challenges for the large network arena: the first challenge is how to consider many subsets of variables, the second is to design clear methods and measures to identify the best targets for intervention in a systematic way, and the third is to work with an intractable state space through sampling.
  
  
  RESULTS
  We introduce a multiple variable intervention target called a template and show through simulation studies of random networks that these templates are able to identify top intervention targets in increasingly large Boolean networks. We first show that, when other methods show drastic loss in performance, template methods show no significant performance loss between fully explored and partially sampled Boolean state spaces. We also show that, when other methods show a complete inability to produce viable intervention targets in sampled Boolean state spaces, template methods maintain significantly consistent success rates even as state space sizes increase exponentially with larger networks. Finally, we show the utility of the template approach on a real-world Boolean network modeling T-LGL leukemia.
  
  
  CONCLUSIONS
  Overall, these results demonstrate how template-based approaches now effectively take over for our previous single variable approaches and produce quality intervention targets in larger networks requiring sampled state spaces.
authors.affiliation: Department of Mathematics and Computer Science, The Citadel, Charleston, 29409 SC USA.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Verdicchio
authors.affiliation: Integrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, 85004 AZ USA.
authors.forename: Seungchan
authors.initials: S
authors.lastname: Kim
keywords: Attractors; Boolean networks; Intervention; Leukemia; Logic minimization