Name
Abstract | ||
|---|---|---|
In this paper, we introduce a framework allowing to model and analyse efficiently Gene Regulatory Networks (GRNs) in their temporal and stochastic aspects. The analysis of stable states and inference of Rene Thomas discrete parameters derives from this logical formalism. We offer a compositional approach which comes with a natural translation to the Stochastic pi-Calculus. The method we propose consists in successive refinements of generalised dynamics of GRNs. We illustrate the merits and scalability of our framework on the control of the differentiation in a GRN generalising metazoan segmentation processes, and on the analysis of stable states within a large GRN studied in the scope of breast cancer researches. |
Year | Venue | DocType |
|---|---|---|
2011 | Lecture Notes in Bioinformatics | Journal |
Volume | ISSN | Citations |
6575 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Loïc Paulevé | 1 | 204 | 18.68 |
| Morgan Magnin | 2 | 115 | 12.71 |
| Olivier Roux | 3 | 10 | 2.44 |