Name
Abstract | ||
|---|---|---|
The accuracy in the determination of model parameters from data depends on the experimental setup. The advance in this area is often hindered by lack of communication between experimentalists and mathematical modelers. We aim to point out a potential benefit in parameter inference when the design variables are chosen optimally. Our approach, although case independent, is illustrated on FRAP (Fluorescence Recovery After Photobleaching) experimental technique. The core idea is closely related to the sensitivity analysis, namely to the maximization of a sensitivity measure depending on experimental settings. The proposed modification of the FRAP experimental protocol is simple and the enhancement of the whole parameter estimation process is significant. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-31744-1_49 | BIOINFORMATICS AND BIOMEDICAL ENGINEERING (IWBBIO 2016) |
Keywords | Field | DocType |
FRAP,Sensitivity analysis,Optimal experimental design,Parameter estimation | Computer vision,Inference,Computer science,Simulation,Algorithm,Artificial intelligence,Estimation theory,Maximization | Conference |
Volume | ISSN | Citations |
9656 | 0302-9743 | 1 |
PageRank | References | Authors |
0.48 | 1 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Štpán Papáček | 1 | 34 | 10.56 |
| Stefan Kindermann | 2 | 293 | 19.60 |