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BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology.

Villaverde AF, Henriques D, Smallbone K, Bongard S, Schmid J, Cicin-Sain D, Crombach A, Saez-Rodriguez J, Mauch K, Balsa-Canto E, Mendes P, Jaeger J, Banga JR - BMC Syst Biol (2015)

Bottom Line: The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging.For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation.Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods.

View Article: PubMed Central - PubMed

Affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain. afvillaverde@iim.csic.es.

ABSTRACT

Background: Dynamic modelling is one of the cornerstones of systems biology. Many research efforts are currently being invested in the development and exploitation of large-scale kinetic models. The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging. The community has already developed many methods and software packages which aim to facilitate these tasks. However, there is a lack of suitable benchmark problems which allow a fair and systematic evaluation and comparison of these contributions.

Results: Here we present BioPreDyn-bench, a set of challenging parameter estimation problems which aspire to serve as reference test cases in this area. This set comprises six problems including medium and large-scale kinetic models of the bacterium E. coli, baker's yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The level of description includes metabolism, transcription, signal transduction, and development. For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation.

Conclusions: This suite of benchmark problems can be readily used to evaluate and compare parameter estimation methods. Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods. The suite, including codes and documentation, can be freely downloaded from the BioPreDyn-bench website, https://sites.google.com/site/biopredynbenchmarks/ .

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Dispersion of convergence curves. Results of 20 parameter estimation runs of the B4 benchmark (CHO cells) with the eSS method. The figures plot the objective function value as a function of the computation time (in log-log scale). Results obtained on a computer with Intel Xeon Quadcore processor, 2.50 GHz, using Matlab 7.9.0.529 (R2009b) 32-bit.
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Fig4: Dispersion of convergence curves. Results of 20 parameter estimation runs of the B4 benchmark (CHO cells) with the eSS method. The figures plot the objective function value as a function of the computation time (in log-log scale). Results obtained on a computer with Intel Xeon Quadcore processor, 2.50 GHz, using Matlab 7.9.0.529 (R2009b) 32-bit.

Mentions: These computation times can be used as a reference to select the appropriate benchmarks to test a particular optimization method, depending on its focus and the available time. Due to the stochastic nature of the eSS algorithm, results may vary among optimization runs. Figure 4 shows the dispersion of 20 different optimization results for benchmark B4.Figure 4


BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology.

Villaverde AF, Henriques D, Smallbone K, Bongard S, Schmid J, Cicin-Sain D, Crombach A, Saez-Rodriguez J, Mauch K, Balsa-Canto E, Mendes P, Jaeger J, Banga JR - BMC Syst Biol (2015)

Dispersion of convergence curves. Results of 20 parameter estimation runs of the B4 benchmark (CHO cells) with the eSS method. The figures plot the objective function value as a function of the computation time (in log-log scale). Results obtained on a computer with Intel Xeon Quadcore processor, 2.50 GHz, using Matlab 7.9.0.529 (R2009b) 32-bit.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4342829&req=5

Fig4: Dispersion of convergence curves. Results of 20 parameter estimation runs of the B4 benchmark (CHO cells) with the eSS method. The figures plot the objective function value as a function of the computation time (in log-log scale). Results obtained on a computer with Intel Xeon Quadcore processor, 2.50 GHz, using Matlab 7.9.0.529 (R2009b) 32-bit.
Mentions: These computation times can be used as a reference to select the appropriate benchmarks to test a particular optimization method, depending on its focus and the available time. Due to the stochastic nature of the eSS algorithm, results may vary among optimization runs. Figure 4 shows the dispersion of 20 different optimization results for benchmark B4.Figure 4

Bottom Line: The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging.For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation.Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods.

View Article: PubMed Central - PubMed

Affiliation: Bioprocess Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain. afvillaverde@iim.csic.es.

ABSTRACT

Background: Dynamic modelling is one of the cornerstones of systems biology. Many research efforts are currently being invested in the development and exploitation of large-scale kinetic models. The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging. The community has already developed many methods and software packages which aim to facilitate these tasks. However, there is a lack of suitable benchmark problems which allow a fair and systematic evaluation and comparison of these contributions.

Results: Here we present BioPreDyn-bench, a set of challenging parameter estimation problems which aspire to serve as reference test cases in this area. This set comprises six problems including medium and large-scale kinetic models of the bacterium E. coli, baker's yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The level of description includes metabolism, transcription, signal transduction, and development. For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation.

Conclusions: This suite of benchmark problems can be readily used to evaluate and compare parameter estimation methods. Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods. The suite, including codes and documentation, can be freely downloaded from the BioPreDyn-bench website, https://sites.google.com/site/biopredynbenchmarks/ .

Show MeSH