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Hybrid optimization method with general switching strategy for parameter estimation.

Balsa-Canto E, Peifer M, Banga JR, Timmer J, Fleck C - BMC Syst Biol (2008)

Bottom Line: If these parameters are unknown, results from simulation studies can be misleading.Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly.Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Process Engineering Group, Spanish Council for Scientific Research, IIM-CSIC, Spain. ebalsa@iim.csic.es

ABSTRACT

Background: Modeling and simulation of cellular signaling and metabolic pathways as networks of biochemical reactions yields sets of non-linear ordinary differential equations. These models usually depend on several parameters and initial conditions. If these parameters are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by fitting the model to experimental data before analyzing the system. This involves parameter estimation which is usually performed by minimizing a cost function which quantifies the difference between model predictions and measurements. Mathematically, this is formulated as a non-linear optimization problem which often results to be multi-modal (non-convex), rendering local optimization methods detrimental.

Results: In this work we propose a new hybrid global method, based on the combination of an evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and efficient alternative for the solution of large scale parameter estimation problems.

Conclusion: The presented new hybrid strategy offers two main advantages over previous approaches: First, it is equipped with a switching strategy which allows the systematic determination of the transition from the local to global search. This avoids computationally expensive tests in advance. Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly. Because multiple-shooting avoids possible spurious solutions in the vicinity of the global optimum it often outperforms the frequently used initial value approach (single-shooting). Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.

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Comparison of the multistart of the generalized-quasi-Newton within single and multiple-shooting for the JAK/STAT5 pathway. Shown is the percentage of convergence to the global minimum, local minima or failure of the optimisation method using 100 restarts. The initial guess of each restart is randomly chosen from interval [0, 5] (Box 5), [0, 10] (Box 10), and [0, 100] (Box 100) using a uniform distribution. a) Noise-to-signal ratio is zero. As anticipated, multiple-shooting (right panel) performs better than single shooting (left panel). b) Same as in a), but using a noise-to-signal ratio of 10%.
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Figure 1: Comparison of the multistart of the generalized-quasi-Newton within single and multiple-shooting for the JAK/STAT5 pathway. Shown is the percentage of convergence to the global minimum, local minima or failure of the optimisation method using 100 restarts. The initial guess of each restart is randomly chosen from interval [0, 5] (Box 5), [0, 10] (Box 10), and [0, 100] (Box 100) using a uniform distribution. a) Noise-to-signal ratio is zero. As anticipated, multiple-shooting (right panel) performs better than single shooting (left panel). b) Same as in a), but using a noise-to-signal ratio of 10%.


Hybrid optimization method with general switching strategy for parameter estimation.

Balsa-Canto E, Peifer M, Banga JR, Timmer J, Fleck C - BMC Syst Biol (2008)

Comparison of the multistart of the generalized-quasi-Newton within single and multiple-shooting for the JAK/STAT5 pathway. Shown is the percentage of convergence to the global minimum, local minima or failure of the optimisation method using 100 restarts. The initial guess of each restart is randomly chosen from interval [0, 5] (Box 5), [0, 10] (Box 10), and [0, 100] (Box 100) using a uniform distribution. a) Noise-to-signal ratio is zero. As anticipated, multiple-shooting (right panel) performs better than single shooting (left panel). b) Same as in a), but using a noise-to-signal ratio of 10%.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2373877&req=5

Figure 1: Comparison of the multistart of the generalized-quasi-Newton within single and multiple-shooting for the JAK/STAT5 pathway. Shown is the percentage of convergence to the global minimum, local minima or failure of the optimisation method using 100 restarts. The initial guess of each restart is randomly chosen from interval [0, 5] (Box 5), [0, 10] (Box 10), and [0, 100] (Box 100) using a uniform distribution. a) Noise-to-signal ratio is zero. As anticipated, multiple-shooting (right panel) performs better than single shooting (left panel). b) Same as in a), but using a noise-to-signal ratio of 10%.
Bottom Line: If these parameters are unknown, results from simulation studies can be misleading.Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly.Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Process Engineering Group, Spanish Council for Scientific Research, IIM-CSIC, Spain. ebalsa@iim.csic.es

ABSTRACT

Background: Modeling and simulation of cellular signaling and metabolic pathways as networks of biochemical reactions yields sets of non-linear ordinary differential equations. These models usually depend on several parameters and initial conditions. If these parameters are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by fitting the model to experimental data before analyzing the system. This involves parameter estimation which is usually performed by minimizing a cost function which quantifies the difference between model predictions and measurements. Mathematically, this is formulated as a non-linear optimization problem which often results to be multi-modal (non-convex), rendering local optimization methods detrimental.

Results: In this work we propose a new hybrid global method, based on the combination of an evolutionary search strategy with a local multiple-shooting approach, which offers a reliable and efficient alternative for the solution of large scale parameter estimation problems.

Conclusion: The presented new hybrid strategy offers two main advantages over previous approaches: First, it is equipped with a switching strategy which allows the systematic determination of the transition from the local to global search. This avoids computationally expensive tests in advance. Second, using multiple-shooting as the local search procedure reduces the multi-modality of the non-linear optimization problem significantly. Because multiple-shooting avoids possible spurious solutions in the vicinity of the global optimum it often outperforms the frequently used initial value approach (single-shooting). Thereby, the use of multiple-shooting yields an enhanced robustness of the hybrid approach.

Show MeSH