A multi-objective differential evolutionary approach toward more stable gene regulatory networks

Esmaeili, Afshin; Jacob, Christian

doi:10.1016/j.biosystems.2009.09.002

Cited by 13 publications

(7 citation statements)

References 28 publications

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“…Handl et al published in 2007 an exhaustive review [11] about the application of multi-objective optimization in fields such as supervised and unsupervised classification of biological data, gene regulatory networks inference, sequence and structure alignment, protein structure prediction or optimization of biochemical processes among others. Several authors have performed preliminary research on the application of multi-objective optimization methods to reverse-engineering gene networks [12] , [13] , [14] . More specifically, this kind of optimization has also been used to search patterns or unique optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Optimization of Regulation in Metabolic Networks

et al. 2012

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Determining the regulation of metabolic networks at genome scale is a hard task. It has been hypothesized that biochemical pathways and metabolic networks might have undergone an evolutionary process of optimization with respect to several criteria over time. In this contribution, a multi-criteria approach has been used to optimize parameters for the allosteric regulation of enzymes in a model of a metabolic substrate-cycle. This has been carried out by calculating the Pareto set of optimal solutions according to two objectives: the proper direction of flux in a metabolic cycle and the energetic cost of applying the set of parameters. Different Pareto fronts have been calculated for eight different “environments” (specific time courses of end product concentrations). For each resulting front the so-called knee point is identified, which can be considered a preferred trade-off solution. Interestingly, the optimal control parameters corresponding to each of these points also lead to optimal behaviour in all the other environments. By calculating the average of the different parameter sets for the knee solutions more frequently found, a final and optimal consensus set of parameters can be obtained, which is an indication on the existence of a universal regulation mechanism for this system.The implications from such a universal regulatory switch are discussed in the framework of large metabolic networks.

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Section: Introductionmentioning

confidence: 99%

Multi-Criteria Optimization of Regulation in Metabolic Networks

et al. 2012

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“…The first area is synthesis of GRNs using evolutionary algorithms. Esmaeili and Jacob 25 employed evolutionary algorithms to discover regulatory networks that optimize multiple stability indicators, including network sensitivity, cyclic length of the attractors, and number of attractors. Nicolau and Schoenauer 26 used evolution to find regulatory networks with specific patterns of connectivity, such as scale-free networks.…”

Section: Related Workmentioning

confidence: 99%

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

García-Calvo

Guisado

Díaz-del-Río

et al. 2018

Evol Bioinform Online

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Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

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“…With the IR defined above, an evaluation function defined below was used to investigate the inconsistency between the network generated and the experimental data:

\begin{matrix} ϕ = \frac{1}{1 + ({false\sum}_{i = normal1}^{N} {IR}_{i} / (N \times 0.5)) + (NP / N^{2})}, \end{matrix}

where N × 0.5 denotes the maximum inconsistency to be generated by the network while (NP/ N 2 ) is a penalty factor. With this evaluation function, the differential evolution (DE) approach was used to identify the optimal network structure [50]. …”

Section: Molecular Network/pathway Reconstructionmentioning

confidence: 99%

A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

Kang

Zhao

2014

BioMed Research International

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With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

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A multi-objective differential evolutionary approach toward more stable gene regulatory networks

Cited by 13 publications

References 28 publications

Multi-Criteria Optimization of Regulation in Metabolic Networks

Multi-Criteria Optimization of Regulation in Metabolic Networks

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

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