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- W4324149346 abstract "Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on their fixed points and attractors. We consider marker properties, which specify that some components are fixed to a specific value. We study 4 variants of the marker reprogramming problem: the reprogramming of fixed points, of minimal trap spaces, and of fixed points and minimal trap spaces reachable from a given initial configuration with the most permissive update mode. The perturbations consist of fixing a set of components to a fixed value. They can destroy and create new attractors. In each case, we give an upper bound on their theoretical computational complexity, and give an implementation of the resolution using the BoNesis Python framework. Finally, we lift the reprogramming problems to ensembles of BNs, as supported by BoNesis, bringing insight on possible and universal reprogramming strategies. This paper can be executed and modified interactively." @default.
- W4324149346 created "2023-03-15" @default.
- W4324149346 creator A5021072417 @default.
- W4324149346 date "2023-03-14" @default.
- W4324149346 modified "2023-09-27" @default.
- W4324149346 title "Reprogramming of locally-monotone Boolean networks with BoNesis" @default.
- W4324149346 doi "https://doi.org/10.24072/pci.mcb.100183" @default.
- W4324149346 hasPublicationYear "2023" @default.
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