"In the category of logical models, Boolean networks were recently used to analyze the relationship between regulation functions and network stability in a yeast transcriptional network and the dynamics of cell-cycle regulation . The structure of Boolean networks can be learned from gene expression profiles. Boolean networks can provide important iological insights into regulation functions and the existence and nature of steady states (i.e.,polarity gene expression) and network robustness. Nevertheless, as the number of global states is exponential in the number of entities and the analysis relies on an exhaustive enumeration of all possible trajectories, this method is computationally expensive and only practical for small networks . Due to insufficient experimental data or incomplete understanding of a system, several candidate regulatory functions may be possible for an entity. To express uncertainty in regulatory logic, the Probabilistic Boolean Network (PBN) was developed and used to model a 15-gene subnetwork inferred from human glioma expression data . The synchronous dynamics of a Boolean network can be
captured by a Petri net, which is a non-deterministic model widely used for detecting active pathways and state cycles and for analyzing large metabolic pathways and regulatory networks. Another model, module networks, infers the regulation logic of gene modules as a decision tree, given gene expression data. The Boolean implication networks presented by Sahoo et al. used scatter plots of the expression between two genes to derive the implication relations in the whole genome. To date, Boolean implication networks have not been applied in biomarker discovery. (Nancy Lan Guo- Network Medicine: New Paradigm in the -Omics Era)"
Last date updated on June, 2014