Name
Abstract | ||
|---|---|---|
One way of coping with the complexity of biological systems is to use the simplest possible models which are able to reproduce at least some nontrivial features of reality. Although two value Boolean models have a long history in technology, it is perhaps a little bit surprising that they can also represent important features of living organizms. In this paper, the scalar equation approach to Boolean network models is further developed and then applied to two interesting biological models. In particular, a linear reduced scalar equation is derived from a more rudimentary nonlinear scalar equation. This simpler, but higher order, two term equation gives immediate information about both cycle and transient structure of the network. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TNN.2004.824262 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
Boolean functions,biology,nonlinear equations,synchronisation,biological applications,linear reduced scalar equation,nonlinear scalar equation,synchronous Boolean network | Boolean function,Boolean network,Applied mathematics,Synchronization,Nonlinear system,Computer science,Scalar (physics),Pure mathematics,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 2 | 1045-9227 |
Citations | PageRank | References |
50 | 6.38 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Farrow | 1 | 50 | 6.38 |
| Jack Heidel | 2 | 84 | 13.49 |
| John Maloney | 3 | 50 | 6.38 |
| J. A. Rogers | 4 | 85 | 13.53 |