Name
Abstract | ||
|---|---|---|
Classical random Boolean networks (RBN) are not well suited to describe experimental data from time-course microarray, mainly because of the strict assumptions about the synchronicity of the regulatory mechanisms. In order to overcome this setback, a generalization of the RBN model is described and analyzed. Gene products (e.g., regulatory proteins) are introduced, with each one characterized by a specific decay time, thereby introducing a form of memory in the system. The dynamics of these networks is analyzed, and it is shown that the distribution of the decay times has a strong effect that can be adequately described and understood. The implications for the dynamical criticality of the networks are also discussed. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1089/cmb.2010.0069 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
criticality,gene regulatory networks,random Boolean networks,time-course microarray,timing | Modeling and simulation,Computer science,Computational mathematics,Boolean model,Theoretical computer science,Synchronicity,Theoretical models,Bioinformatics,Criticality,Gene regulatory network | Journal |
Volume | Issue | ISSN |
18.0 | 10 | 1066-5277 |
Citations | PageRank | References |
15 | 0.86 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex Graudenzi | 1 | 90 | 17.99 |
| Roberto Serra | 2 | 203 | 29.48 |
| Marco Villani | 3 | 188 | 35.04 |
| Chiara Damiani | 4 | 66 | 11.46 |
| Annamaria Colacci | 5 | 51 | 5.43 |
| Stuart Kauffman | 6 | 245 | 32.76 |