José Egea

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

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.

Phenotypic diversity and relationships of fruit quality traits in apricot (Prunus armeniaca L.) germplasm

Fruit quality attributes were studied for two consecutive years in forty-three apricot cultivars and selections grown in a Mediterranean climate. Physical parameters (weight, size, flesh and skin colour, percentage of blush, firmness and percentage of dry matter), chemical parameters (total soluble solids content and acidity) and sensory parameters (attractiveness, taste, aroma and texture) were evaluated. A high variability was found in the set of the evaluated apricot genotypes and significant differences were found among them in all studied quality attributes. Year-by-year variations were observed for some pomological traits such as harvest date, flesh colour, fruit weight, firmness and soluble solids content. A high correlation was found among some apricot quality attributes. In addition, principal component analysis (PCA) made it possible to establish similar groups of genotypes depending on their quality characteristics as well as to study relationships among pomological traits in the set of apricot genotypes evaluated.

Extended ant colony optimization for non-convex mixed integer nonlinear programming

Two novel extensions for the well known ant colony optimization (ACO) framework are introduced here, which allow the solution of mixed integer nonlinear programs (MINLPs). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results. These extensions on the ACO framework have been developed to serve the needs of practitioners who face highly non-convex and computationally costly MINLPs. The performance of this new method is evaluated considering several non-convex MINLP benchmark problems and one real-world application. The results obtained by our implementation substantiate the success of this new approach.

An evolutionary method for complex-process optimization

In this paper we present a new evolutionary method for complex-process optimization. It is partially based on the principles of the scatter search methodology, but it makes use of innovative strategies to be more effective in the context of complex-process optimization using a small number of tuning parameters. In particular, we introduce a new combination method based on path relinking, which considers a broader area around the population members than previous combination methods. We also use a population-update method which improves the balance between intensification and diversification. New strategies to intensify the search and to escape from suboptimal solutions are also presented. The application of the proposed evolutionary algorithm to different sets of both state-of-the-art continuous global optimization and complex-process optimization problems reveals that it is robust and efficient for the type of problems intended to solve, outperforming the results obtained with other methods found in the literature.

A Tabu search-based algorithm for mixed-integer nonlinear problems and its application to integrated process and control system design

In this contribution, we consider mixed-integer nonlinear programming problems subject to differential-algebraic constraints. This class of problems arises frequently in process design, and the particular case of integrated process and control system design is considered. Since these problems are frequently non-convex, local optimization techniques usually fail to locate the global solution. Here, we propose a global optimization algorithm, based on extensions of the metaheuristic Tabu Search, in order to solve this challenging class of problems in an efficient and robust way. The ideas of the methodology are explained and, on the basis of two case studies, the performance of the approach is evaluated. The first benchmark problem is a Wastewater Treatment Plant model [Alex, J., Bteau, J. F., Copp, J. B., Hellinga, C., Jeppsson, U., Marsili-Libelli, S., et al. (1999). Benchmark for evaluating control strategies in wastewater treatment plants. InProceedings of the ECC’99 conference] for nitrogen removal and the second case study is the well-known Tennessee Eastman Process [Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers & Chemical Engineering, 17, 245-255]. Numerical experiments with our new method indicate that we can achieve an improved performance in both cases. Additionally, our method outperforms several other recent competitive solvers for the two challenging case studies considered.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

BackgroundOptimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools.ResultsWe present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods.ConclusionsMEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

A cooperative strategy for parameter estimation in large scale systems biology models

BackgroundMathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently.ResultsA new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs (“threads”) that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results.ConclusionsThe cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended to incorporate other global and local search solvers and specific structural information for particular classes of problems.

Effect of basal media and growth regulators on the in vitro propagation of apricot (Prunus armenica L.) cv. Canino.

Summary The effect of different basal media and cytokinin concentration on the proliferation of ‘Canino’ shoots were studied. Among the media where optimum proliferation was obtained, healthier and greener shoots were found when a modified WP medium was used. A BA concentration between 0.5 and 0.6 mg l–1 produced an optimum number of shoots of a good length to be transferred to further subcultures. IBA and NAA induced rooting in similar percentages and with the same number of roots per shoot. The best rooting percentages (92.8 ± 2.5%) were induced in NAA at 2 mg l–1 while the largest number of roots per shoot (5.3 ± 0.5) were obtained after induction of 6 mg l–1 of IBA.

Control of hyperhydricity in micropropagated apricot cultivars

SummaryThe effects of different factors on the control and reversion of hyperhydricity during the in vitro propagation of Prunus armeniaca were studied. Treatments that decreased the hyperhydricity but did not affect micropropagation rates were the use of the bottom cooling system for 1 or 2 wk and agargel as gelling agent in ‘Helena’, whereas the best results were obtained with the bottom cooling system for 2 wk and the use of 0.8% agar as gelling agent in ‘Lorna’. Hyperhydric shoots reverted to normal after keeping them for 3 wk in the bottom cooling system.

Apricot flower bud development and abscission related to chilling, irrigation and type of shoots

Abstract Flower bud drop dramatically affects productivity in some apricot cultivars. The influence of different chilling and irrigation conditions on flower bud development and drop of the apricot cv. ‘Guillermo’ was studied. Flower buds from trees grown in three places, with different winter conditions, were collected periodically to compare their developmental stage. Slight differences in the developmental pattern were observed between the two coldest places. Just before bloom, flower buds from the warmest place were found to be more delayed. Also, flower bud development was compared in three irrigation treatments. The lack of autumn irrigation induced a slow development, but when irrigation was reinstated, the flower bud development was hastened. The suppression of irrigation in winter did not have an apparent effect on flower bud development compared with the more irrigated treatment. In flower buds of short shoots no important abnormalities were found, but they were in the analysed flower buds of long shoots. In ‘Guillermo’ cultivar a serious drop was recorded in every location or treatment. However, no correlation was found between accumulation of chill unit or irrigation treatments and flower bud drop or fructification. In long shoots, the heavy flower bud drops (more than 90%), together with the high number of malformed flower buds, indicate that they do not produce an acceptable fruit set in this apricot cultivar.