To effectively intervene when cells are trapped in pathological modes of operation it is necessary to build models that capture relevant network structure and include characterization of dynamical changes within the system. The model must be of sufficient detail that it facilitates the selection of intervention points where pathological cell behavior arising from improper regulation can be stopped. What is known about this type of cellular decision-making is consistent with the general expectations associated with any kind of decision-making operation. If the result of a decision at one node is serially transmitted to other nodes, resetting their states, then the process may suffer from mechanistic inefficiencies of transmission or from blockage or activation of transmission through the action of other nodes acting on the same node. A standard signal-processing network model, Bayesian networks, can model these properties. This paper employs a Bayesian tree model to characterize conditional pathway logic and quantify the effects of different branching patterns, signal transmission efficiencies and levels of alternate or redundant inputs. In particular, it characterizes master genes and canalizing genes within the quantitative framework. The model is also used to examine what inferences about the network structure can be made when perturbations are applied to various points in the network.