Abstract
BackgroundDynamic 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.ResultsHere 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.ConclusionsThis 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/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0144-4) contains supplementary material, which is available to authorized users.
Highlights
Dynamic modelling is one of the cornerstones of systems biology
Remarks on comparing optimization methods the objective of this paper is to present a set of ready-to-run benchmarks, we list below several guidelines on how to compare different optimizers with these problems
Envisioning that dynamic measurements of the 44 observed variables may be available in the near future, we show in this paper how they will be employed for re-estimating the parameter values
Summary
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. To describe the behaviour of complex systems, models with sufficient level of detail to provide mechanistic explanations are needed This leads to the use of large-scale dynamic models of cellular. For the models to encapsulate as accurately as possible our understanding of the system (i.e. reproducing the available data and, ideally, being capable of making predictions), these parameters have to be estimated This task, known as parameter estimation, model calibration, or data fitting [6,7,8,9,10], consists of finding the parameter values that give the best fit between the model output and a set of experimental data. One may choose a multistart strategy, where a local method is used repeatedly, starting from a number of different initial guesses for the parameters This approach is usually not efficient for realistic applications, and global optimization techniques need to be used instead [11,12]
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