Name
Abstract | ||
|---|---|---|
The ability to correlate mathematical models with experimental data is fundamental for a wide range of quantitative biology disciplines. Modelling typically requires accurate knowledge of kinetic rate constants, which may be extracted by parameter estimation using physical observations of overall system behaviors. Synthetic gene networks have well characterized connectivity and are easily manipulated for validation purposes, making them ideal for studying parameter estimation techniques. Here we use two synthetic gene networks, a transcriptional cascade and a pulse generating network, to study the efficacy of a simple statistical parameter fitting algorithm. The fitting was performed on experimental data and computer-generated data (to test how well the algorithm works under ideal conditions with perfect information). Most of the experimental parameter estimations yielded tight ranges of kinetic values for both gene networks. However, the results using simulated data indicate that the algorithm was able to provide better parameter estimates for the pulse generating network than for the transcriptional cascade. This is likely a result of the larger amount of time-series data available for the pulse generator and its greater level of phenotypical complexity, leading to tighter constraints for optimization. The variation in the magnitudes of the standard deviations between parameter estimates may give an indication of system sensitivity to specific kinetic rate constants. In the future, we also plan to verify the experimental parameter estimation results by constructing network variants and attempting to predict behaviors using values obtained in this study. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICASSP.2005.1416417 | ICASSP |
Keywords | Field | DocType |
genetics,parameter estimation,statistical analysis,kinetic rate constants,parameter estimation,phenotypical complexity,pulse generating network,statistical parameter fitting,synthetic gene networks,time-series data,transcriptional cascade gene network | Statistical parameter,Time series,Mathematical optimization,Experimental data,Computer science,Algorithm,Pulse generator,Cascade,Estimation theory,Statistics,Gene regulatory network,Standard deviation | Conference |
Volume | ISSN | Citations |
5 | 1520-6149 | 4 |
PageRank | References | Authors |
0.59 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| david braun | 1 | 56 | 6.31 |
| Subhayu Basu | 2 | 59 | 45.52 |
| Ron Weiss | 3 | 42 | 2.95 |