Name
Title | ||
|---|---|---|
Feasible parameter space characterization with adaptive sparse grids for nonlinear systems biology models |
Abstract | ||
|---|---|---|
Mathematical models are commonly used to interrogate and control biological systems. However, such models are often uncertain and sloppy, with multiple parameter sets equally capable of reproducing the experimental data. These features make systems biology models unreliable when used to support a model-based control strategy. Multi-scenario control can help account for this uncertainty, but a computationally feasible method for characterizing all data-consistent regions of the global parameter space is necessary. Herein, we propose a tool for multi-scenario control in which sparse grid-based optimization is paired with a grid focusing algorithm to characterize acceptable regions of the uncertain parameter space. The grid focusing algorithm is first demonstrated on a test function before being applied within a multi-scenario control framework to an uncertain model of cell differentiation. The results show the algorithm's ability to identify disparate low-cost regions of the parameter space and selectively increase the grid resolution in these areas to help determine appropriate model scenarios for the multi-scenario controller. While particularly relevant to biological systems, this approach is broadly applicable to the control of any uncertain system. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ACC.2011.5990834 | American Control Conference |
Keywords | Field | DocType |
adaptive control,biology,nonlinear control systems,optimisation,uncertain systems,adaptive sparse grid,grid focusing algorithm,mathematical model,model-based control strategy,multiscenario control,nonlinear system biology model,parameter space characterization,sparse grid-based optimization,uncertain system,sparse grids,cost function,prediction model,predictive models,system biology,data consistency,biological systems,cell differentiation,parameter space,polynomials,nonlinear system | Mathematical optimization,Control theory,Control theory,Computer science,Test functions for optimization,Systems biology,Control engineering,Parameter space,Adaptive control,Mathematical model,Sparse grid,Grid | Conference |
ISSN | ISBN | Citations |
0743-1619 | 978-1-4577-0080-4 | 1 |
PageRank | References | Authors |
0.36 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noble, S.L. | 1 | 1 | 0.36 |
| Gregery T Buzzard | 2 | 1 | 0.69 |
| Rundell, A.E. | 3 | 25 | 4.08 |