Name
Title | ||
|---|---|---|
Kinetic Theory Modeling and Efficient Numerical Simulation of Gene Regulatory Networks Based on Qualitative Descriptions. |
Abstract | ||
|---|---|---|
In this work, we begin by considering the qualitative modeling of biological regulatory systems using process hitting, from which we define its probabilistic counterpart by considering the chemical master equation within a kinetic theory framework. The last equation is efficiently solved by considering a separated representation within the proper generalized decomposition framework that allows circumventing the so-called curse of dimensionality. Finally, model parameters can be added as extra-coordinates in order to obtain a parametric solution of the model. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3390/e17041896 | ENTROPY |
Keywords | Field | DocType |
statistical mechanics,kinetic theory | Statistical mechanics,Mathematical optimization,Computer simulation,Curse of dimensionality,Parametric statistics,Kinetic theory,Probabilistic logic,Statistics,Gene regulatory network,Master equation,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 4 | 1099-4300 |
Citations | PageRank | References |
1 | 0.78 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Chinesta | 1 | 36 | 18.92 |
| Morgan Magnin | 2 | 115 | 12.71 |
| Olivier Roux | 3 | 10 | 2.44 |
| A. Ammar | 4 | 17 | 6.79 |
| Elías Cueto | 5 | 8 | 3.77 |