Abstract
BackgroundWe consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness.ResultsWe have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods.ConclusionRobust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.
Highlights
We consider the problem of parameter estimation in nonlinear dynamic models of biological systems
In order to evaluate the performance and reliability of the novel metaheuristic presented here, which we will denote SSm, we have considered three challenging benchmark problems of increasing order of complexity
Parameter estimation from experimental data remains a bottleneck for a major breakthrough in systems biology
Summary
We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. One starts from the definition of the purpose of the model and uses the a priori available knowledge (i.e. physical, chemical or biological laws, initial hypothesis and/or preliminary data) to choose a model framework and to propose a model structure This model contain parameters and we need to know whether is it possible to uniquely determine their values (identifiability analysis) and if so, to estimate them with maximum precision and accuracy. This leads to a first working model that must be validated with new experiments, revealing in most cases a number of deficiencies. This iterative process (i.e. the model building cycle) should contain other elements like optimal experimental design and model discrimination steps [9,10,11,12,13]
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