title: Experimental design schemes for learning Boolean network models.
abstract: BACKGROUND
  A holy grail of biological research is a working model of the cell. Current modeling frameworks, especially in the protein-protein interaction domain, are mostly topological in nature, calling for stronger and more expressive network models. One promising alternative is logic-based or Boolean network modeling, which was successfully applied to model signaling regulatory circuits in human. Learning such models requires observing the system under a sufficient number of different conditions. To date, the amount of measured data is the main bottleneck in learning informative Boolean models, underscoring the need for efficient experimental design strategies.
  
  
  RESULTS
  We developed novel design approaches that greedily select an experiment to be performed so as to maximize the difference or the entropy in the results it induces with respect to current best-fit models. Unique to our maximum difference approach is the ability to account for all (possibly exponential number of) Boolean models displaying high fit to the available data. We applied both approaches to simulated and real data from the EFGR and IL1 signaling systems in human. We demonstrate the utility of the developed strategies in substantially improving on a random selection approach. Our design schemes highlight the redundancy in these datasets, leading up to 11-fold savings in the number of experiments to be performed.
  
  
  METHODS
  Source code will be made available upon acceptance of the manuscript.
authors.affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Nir
authors.initials: N
authors.lastname: Atias
authors.affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Michal
authors.initials: M
authors.lastname: Gershenzon
authors.affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Katia
authors.initials: K
authors.lastname: Labazin
authors.affiliation: Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Roded
authors.initials: R
authors.lastname: Sharan
mesh_terms: D000465:Algorithms; D066246:ErbB Receptors / Q000378:metabolism; D006801:Humans; D007375:Interleukin-1 / Q000378:metabolism; D008954:Models, Biological; D012107:Research Design; D015398:Signal Transduction
chemical_list: D007375:Interleukin-1; D066246:ErbB Receptors