The asymmetric Hopfield model is used to simulate signaling dynamics in
gene/transcription factor networks. The model allows for a direct mapping of a
gene expression pattern into attractor states. We analyze different control
strategies aiming at disrupting attractor patterns using selective local fields
representing therapeutic interventions. The control strategies are based on the
identification of signaling bottlenecks, which are single nodes or strongly
connected clusters of nodes that have a large impact on the signaling. We
provide a theorem with bounds on the minimum number of nodes that guarantee
controllability of bottlenecks consisting of strongly connected components. The
control strategies are applied to the identification of sets of proteins that,
when inhibited, selectively disrupt the signaling of cancer cells while
preserving the signaling of normal cells. We use an experimentally validated
non-specific network and a specific B cell interactome reconstructed from gene
expression data to model cancer signaling in lung and B cells, respectively.
This model could help in the rational design of novel robust therapeutic
interventions based on our increasing knowledge of complex gene signaling
networks
