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Found 3 papers, 1 papers with code
Boolean Networks as Predictive Models of Emergent Biological Behaviors
Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e. g., the unknown values of kinetic parameters for biochemical reactions).
Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks
Driving a network's feedback vertex set (FVS) by node-state override into a state consistent with a target attractor is proven to force every system trajectory to the target attractor, but in biological networks, the FVS is typically larger than can be realistically manipulated.
Structure-based approach to identifying small sets of driver nodes in biological networks
Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures.
