Name
Title | ||
|---|---|---|
A new decomposition-based method for detecting attractors in synchronous Boolean networks |
Abstract | ||
|---|---|---|
Boolean networks are a well-established formalism for modelling biological systems. An important aspect of analysing a Boolean network is to identify all its attractors. This becomes challenging for large Boolean networks due to the infamous state-space explosion problem. In this paper, we propose a new strongly connected component (SCC) based decomposition method for attractor detection in large synchronous Boolean networks and prove its correctness. Experimental results show that our proposed method is significantly better in terms of performance when compared to existing methods in the literature. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.scico.2019.05.001 | Science of Computer Programming |
Keywords | Field | DocType |
Synchronous Boolean networks,Attractor detection,Decomposition,Binary decision diagram (BDD) | Boolean network,Attractor,Computer science,Correctness,Theoretical computer science,Decomposition method (constraint satisfaction),Modelling biological systems,Formalism (philosophy),Strongly connected component | Journal |
Volume | ISSN | Citations |
180 | 0167-6423 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qixia Yuan | 1 | 31 | 7.44 |
| Andrzej Mizera | 2 | 29 | 7.57 |
| Jun Pang | 3 | 219 | 33.53 |
| Hongyang Qu | 4 | 592 | 35.13 |