Hybrid optimization method with general switching strategy for parameter estimation

1 Citation

Eva Balsa-Canto, Martin Peifer, Julio R Banga, Jens Timmer and Christian Fleck Hybrid optimization method with general switching strategy for parameter estimation, 2008, BMC Systems Biology, 2:26.

2 Summary

Parameter estimation in systems biology model requires finding the global optimum of the objective function. This is usually done in terms of deterministic local optimizers which run the risk of precisely finding only a local optimum or by stochastic global optimizers which find the global optimum, but do not fully converge. Hence, the authors proposed a hybrid optimization which switches from stochastic global optimization to a local multiple shooting approach with a switching point which is determined dynamically. The hybriod method was demonstrated to vastly outperform stochastic global optimization and single as well as multiple shooting local methods in terms of computation time.

3 Study outcomes

3.1 Multiple vs Single Shooting

As a deterministic optimizer, the multiple shooting method is better at finding the global optimum than a single shooting approach, as can be seen from Figure 1 and 2 in the original publication. However, this comes at the cost of increased computational cost.

3.2 Comparison of Global Optimizers

A comparison of single shooting, multiple shooting, a stochastic global optimizer and the hybrid method showed that the hybrid method decreased the computation time significantly compared to the alternatives in the two tested models, which is listed in Table 1 and Table 2 and demonstrated in Figure 3 in the publication.

4 Study design and evidence level

4.1 General aspects

You can describe general design aspects here. The study designs for describing specific outcomes are listed in the following subsections:

4.2 Design for Outcome O1

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4.3 Design for Outcome O2

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4.4 Design for Outcome O

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6 References

The list of cited or related literature is placed here.