Name
Abstract | ||
|---|---|---|
The dynamics of stochastic reaction networks within cells are inevitably modulated by factors considered extrinsic to the network such as, for instance, the fluctuations in ribosome copy numbers for a gene regulatory network. While several recent studies demonstrate the importance of accounting for such extrinsic components, the resulting models are typically hard to analyze. In this work we develop a general mathematical framework that allows to uncouple the network from its dynamic environment by incorporating only the environment's effect onto the network into a new model. More technically, we show how such fluctuating extrinsic components (e.g., chemical species) can be marginalized in order to obtain this decoupled model. We derive its corresponding process-and master equations and show how stochastic simulations can be performed. Using several case studies, we demonstrate the significance of the approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1371/journal.pcbi.1003942 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
biomedical research,bioinformatics | Markov process,Computer science,Stochastic process,Probability distribution,Dynamical systems theory,Bioinformatics,Mathematical model,Gene regulatory network,Master equation | Journal |
Volume | Issue | Citations |
10 | 12 | 3 |
PageRank | References | Authors |
0.50 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoph Zechner | 1 | 5 | 2.32 |
| Heinz Koeppl | 2 | 159 | 36.18 |