Name
Abstract | ||
|---|---|---|
We propose a compositional approach to the dynamics of gene regulatory networks based on the stochastic pi-calculus, and develop a representation of gene network elements which can be used to build complex circuits in a transparent and efficient way. To demonstrate the power of the approach we apply it to several artificial networks, such as the repressilator and combinatorial gene circuits first studied in Combinatorial Synthesis of Genetic Networks [1]. For two examples of the latter systems, we point out how the topology of the circuits and the interplay of the stochastic gate interactions influence the circuit behavior. Our approach may be useful for the testing of biological mechanisms proposed to explain the experimentally observed circuit dynamics. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11732488_10 | LECTURE NOTES IN COMPUTER SCIENCE |
Keywords | Field | DocType |
gene network,gene regulatory network | Stochastic dynamics,Computer science,Stochastic neural network,Gene Circuits,Mechanism (biology),Theoretical computer science,Repressilator,Artificial intelligence,Gene regulatory network,Electronic circuit,Machine learning | Conference |
Volume | ISSN | Citations |
3939 | 0302-9743 | 37 |
PageRank | References | Authors |
2.38 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ralf Blossey | 1 | 46 | 4.20 |
| Luca Cardelli | 2 | 6221 | 1002.41 |
| Andrew Phillips | 3 | 227 | 17.50 |