Name
Abstract | ||
|---|---|---|
Random Boolean networks (RBNs) have been a popular model of genetic regulatory networks for more than four decades. However, most RBN studies have been made with random topologies, while real regulatory networks have been found to be modular. In this work, we extend classical RBNs to define modular RBNs. Statistical experiments and analytical results show that modularity has a strong effect on the properties of RBNs. In particular, modular RBNs have more attractors, and are closer to criticality when chaotic dynamics would be expected, than classical RBNs. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1162/artl_a_00042 | Artificial Life |
Keywords | Field | DocType |
random boolean network,analytical result,popular model,chaotic dynamic,genetic regulatory network,modular rbns,classical rbns,modular random boolean networks1,rbn study,real regulatory network,statistical experiment,topology,modularity,criticality,cellular automata | Attractor,Computer science,Network topology,Theoretical computer science,Artificial intelligence,Criticality,Modular design,Chaotic,Gene regulatory network,Modularity | Journal |
Volume | Issue | ISSN |
17 | 4 | 1064-5462 |
Citations | PageRank | References |
7 | 0.59 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rodrigo Poblanno-Balp | 1 | 7 | 0.59 |
| Carlos Gershenson | 2 | 392 | 42.34 |