Name
Abstract | ||
|---|---|---|
This article aims to stabilize probabilistic Boolean networks (PBNs) via a novel pinning control strategy. In a PBN, the state evolution of each gene switches among a collection of candidate Boolean functions with preassigned probability distributions, which govern the activation frequency of each Boolean function. Due to the existence of stochasticity, the mode-independent pinning controller might be disabled. Thus, both mode-independent and mode-dependent pinning controller are required here. Moreover, a criterion is derived to determine whether mode-independent controllers are applicable while the pinned nodes are given. It is worth pointing out that this pinning control is based on the
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network structure rather than
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state transition matrix. Therefore, compared with the existing results, this pinning control strategy is more practicable and has the ability to handle large-scale networks, especially sparsely connected networks. To demonstrate the effectiveness of the designed control scheme, a PBN that describes the mammalian cell-cycle encountering a mutated phenotype is discussed as a simulation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCYB.2021.3092374 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Animals,Models, Statistical,Computer Simulation,Mammals | Journal | 52 |
Issue | ISSN | Citations |
12 | 2168-2267 | 0 |
PageRank | References | Authors |
0.34 | 32 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Lin | 1 | 147 | 22.90 |
| Jinde Cao | 2 | 11399 | 733.03 |
| Jianquan Lu | 3 | 2337 | 116.05 |
| Jie Zhong | 4 | 171 | 14.53 |
| Shiyong Zhu | 5 | 64 | 6.92 |