Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

Penas, David R.; Banga, Julio R.; González, Patricia; Doallo, Ramón

doi:10.1016/j.asoc.2015.04.025

Cited by 48 publications

(33 citation statements)

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“…Previous works in the literature have already pointed out that, for sequential implementations, eSS performs better than DE in those problems where local searches are instrumental in refining the solution [51]. Thus, to ensure a more fair comparison here, we chose a parallel DE implementation (asynPDE [43]) that performs a global search through an asynchronous parallel implementation based on a cooperative island-model, and that also improves the local search phase by means of several heuristics also used in the eSS (i.e. an efficient local solver, a tabu list and a logarithmic space search).…”

Section: Resultsmentioning

confidence: 99%

“…repeated local searches started from different initial guesses inside a bounded domain) also enjoys great popularity, but it has been shown to be rather inefficient, even when exploiting high-quality gradient information [35]. Parallel global optimization strategies have been considered in several system biology studies, including parallel variants of simulated annealing [36], evolution strategies [37–40], particle swarm optimization [41, 42] and differential evolution [43]. …”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

et al. 2017

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BackgroundThe development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times.Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies.ResultsThe performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network.The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used.ConclusionsThe new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1452-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

et al. 2017

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“…The same previous experiments were carried out with the implementation of the asynchronous parallel DE described in [16]. This implementation is coded in C and uses the OpenMPI library.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of Parallel Differential Evolution Implementations on MapReduce and Spark

Teijeiro

Pardo

Penas³

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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Abstract. Global optimization problems arise in many areas of science and engineering, computational and systems biology and bioinformatics among them. Many research efforts have focused on developing parallel metaheuristics to solve them in reasonable computation times. Recently, new programming models are being proposed to deal with large scale computations on commodity clusters and Cloud resources. In this paper we investigate how parallel metaheuristics deal with these new models by the parallelization of the popular Differential Evolution algorithm using MapReduce and Spark. The performance evaluation has been carried out both in a local cluster and in the Amazon Web Services public cloud. The results obtained can be particularly useful for those interested in the potential of new Cloud programming models for parallel metaheuristic methods in general and Differential Evolution in particular.

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“…A feature of many but not all of these algorithms is the maintenance of a population of good parameter sets, which are used to generate new trial parameter sets. Many modern descriptions of population-based metaheuristic algorithms (e.g [39,40,41]) allow for parallelized function evaluations within a single run of the algorithm, which enables these algorithms to take advantage of high-performance computing resources.…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

Parameter estimation and uncertainty quantification for systems biology models

Mitra

Hlavacek

2019

Current Opinion in Systems Biology

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Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena. Highlights• Models of immunoreceptor signaling often contain parameters that must be fit to data.• New tools including PyBioNetFit and AMICI support automated parameter estimation.• Optimization algorithms can be used to obtain point estimates of parameter values.• Parameter estimation can incorporate both quantitative and qualitative data.• Uncertainty quantification assesses confidence in parameter values and model predictions.

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Enhanced parallel Differential Evolution algorithm for problems in computational systems biology

Cited by 48 publications

References 50 publications

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

Evaluation of Parallel Differential Evolution Implementations on MapReduce and Spark

Parameter estimation and uncertainty quantification for systems biology models

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