Name
Abstract | ||
|---|---|---|
Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous updating scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life biological networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCBB.2018.2850901 | IEEE/ACM transactions on computational biology and bioinformatics |
Keywords | Field | DocType |
Biological system modeling,Biological systems,Boolean functions,Computational modeling,Explosions | Attractor,Boolean function,Boolean network,Asynchronous communication,Biological network,Computer science,Correctness,Decomposition method (constraint satisfaction),Theoretical computer science,Artificial intelligence,Modelling biological systems,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 1 | 1557-9964 |
Citations | PageRank | References |
3 | 0.45 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrzej Mizera | 1 | 29 | 7.57 |
| Jun Pang | 2 | 219 | 33.53 |
| Hongyang Qu | 3 | 592 | 35.13 |
| Qixia Yuan | 4 | 31 | 7.44 |