Name
Abstract | ||
|---|---|---|
Boolean networks are an important formalism for modelling biological systems and have attracted much attention in recent years. An~important challenge in Boolean networks is to exhaustively find attractors, which represent steady states of a~biological network. In this paper, we propose a~new approach to improve the efficiency of BDD-based attractor detection. Our approach includes a~monolithic algorithm for small networks, an~enumerative strategy to deal with large networks, a~method to accelerate attractor detection based on an~analysis of the network structure, and two heuristics on ordering BDD variables. We demonstrate the performance of our approach on a~number of examples and on a~realistic model of apoptosis in hepatocytes. We compare it with one existing technique in the literature. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11432-016-5594-9 | SCIENCE CHINA Information Sciences |
Keywords | Field | DocType |
Boolean networks, systems biology, binary decision diagram, attractor, verification algorithms | Attractor,Large networks,Binary decision diagram,Systems biology,Theoretical computer science,Heuristics,Modelling biological systems,Formalism (philosophy),Mathematics,Network structure | Journal |
Volume | Issue | ISSN |
59 | 8 | 1869-1919 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qixia Yuan | 1 | 31 | 7.44 |
| Hongyang Qu | 2 | 592 | 35.13 |
| Jun Pang | 3 | 219 | 33.53 |
| Andrzej Mizera | 4 | 29 | 7.57 |