Using gene expression programming to infer gene regulatory networks from time-series data

Gene regulatory networks inference is currently a topic under heavy research in the systems biology field. In this paper, gene regulatory networks are inferred via evolutionary model based on time-series microarray data. A non-linear differential equation model is adopted. Gene expression programming (GEP) is applied to identify the structure of the model and least mean square (LMS) is used to optimize the parameters in ordinary differential equations (ODEs). The proposed work has been first verified by synthetic data with noise-free and noisy time-series data, respectively, and then its effectiveness is confirmed by three real time-series expression datasets. Finally, a gene regulatory network was constructed with 12 Yeast genes. Experimental results demonstrate that our model can improve the prediction accuracy of microarray time-series data effectively.     

Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other’s state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.     

Optimization strategy based on genetic algorithms and neural networks applied to a polymerization process

The article presents a simple and general methodology, especially destined to the optimization of complex, strongly nonlinear systems, for which no extensive knowledge or precise models are available. The optimization problem is solved by means of a simple genetic algorithm, and the results are interpreted both from the mathematical point of view (the minimization of the objective function) and technological (the estimation of the achievement of individual objectives in multiobjective optimization). The use of a scalar objective function is supported by the fact that the genetic algorithm also computes the weights attached to the individual objectives along with the optimal values of the decision variables. The optimization strategy is accomplished in three stages: (1) the design and training of the neural model by a new method based on a genetic algorithm where information about the network is coded into the chromosomes; (2) the actual optimization based on genetic algorithms, which implies testing different values for parameters and different variants of the algorithm, computing the weights of the individual objectives and determining the optimal values for the decision variables; (3) the user's decision, who chooses a solution based on technological criteria. © 2007 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

Geometry optimizations of benzene clusters using a modified genetic algorithm

A modified genetic algorithm with real-number coding, non-uniform mutation and arithmetical crossover operators was described in this paper. A local minimization was used to improve the final solution obtained by the genetic algorithm. Using the exp-6-1 interatomic energy function, the modified genetic algorithm with local minimization (MGALM) was applied to the geometry optimization problem of small benzene clusters (C6H6)N(N = 2-7) to obtain the global minimum energy structures. MGALM is simple but the structures optimized are comparable to the published results obtained by parallel genetic algorithms.     

Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters

An improved genetic algorithm (GA) is described that has been developed to increase the efficiency of finding the global minimum energy isomers for nanoalloy clusters. The GA is optimized for the example Pt12Pd12, with specific investigation of: the effect of biasing the initial population by seeding; the effect of removing specified clusters from the population ("predation"); and the effect of varying the type of mutation operator applied. These changes are found to significantly enhance the efficiency of the GA, which is subsequently demonstrated by the application of the best strategy to a new cluster, namely Pt19Pd19.     

Development of a multipopulation parallel genetic algorithm for structure solution from powder diffraction data

Previously, the genetic algorithm (GA) approach for direct-space crystal structure solution from powder diffraction data has been applied successfully in the structure determination of a range of organic molecular materials. In this article, we present a further development of our approach, namely a multipopulation parallel GA (PGA), which is shown to give rise to increased speed, efficiency, and reliability of structure solution calculations, as well as providing new opportunities for further optimizing our GA methodology. The multipopulation PGA is based on the independent evolution of different subpopulations, with occasional interaction (e.g., transfer of structures) allowed to occur between the different subpopulations. Different strategies for carrying out this interpopulation communication are considered in this article, and comparisons are made to the conventional single-population GA. The increased power offered by the PGA approach creates the opportunity for structure determination of molecular crystals of increasing complexity.     

Design of pseudo-simulated moving bed process with multi-objective optimization for the separation of a ternary mixture: Linear isotherms

Pseudo-SMB, often called “J-O process”, is a modified SMB process to completely separate a ternary mixture with two discrete steps per one cycle. For improved separation, two new design parameters, the position of step 1 (χ_S1) and the number of port switches during step 2 (n_SMB), were introduced. A multi-objective optimization method was used to optimize the operating conditions of the pseudo-SMB process with four average zone flow-rate ratios for one cycle. Nadolol isomers were selected for the model solutes and the global objective for the design of the pseudo-SMB was to collect 99% of the intermediate retained solute. The separation was optimized for 8-column pseudo-SMB system with three column lengths (2.5, 5.0, and 10 cm) and three feed composition ratios (1/1/1, 1/2/1, and 2/1/2). The simulation results showed that productivity was increased 4.3 times (n_SMB = 20, χ_S1 = 0.5, 1/1/1) and desorbent to feed ratio D/F was decreased 45% (n_SMB = 16, χ_S1 = 0.5, 1/1/1) compared to normal operation (n_SMB = 8, χ_S1 = 0.5, 1/1/1). Productivity and D/F were significantly improved when short columns were used in the pseudo-SMB process. The pseudo-SMB was compared with recycle chromatography and SMB cascades for the same total amount of adsorbent. Recycle chromatography and 8-column SMB cascades using 20 cm and 40 cm of total column lengths were not able to separate the intermediate component with the target purity and the same feed rate of the pseudo-SMB process.     

Energy minimization of mixed argon–xenon microclusters using a genetic algorithm

This article describes a parallel real-coded genetic algorithm implemented to find global minimum energy structures of microclusters of non-bonded argon and xenon atoms. Using appropriate genetic operators, the genetic algorithm was able to find minimum energy structures for microclusters of two to twenty atoms, in all possible combinations of argon and xenon. © 1997 John Wiley & Sons, Inc. J Comput Chem 18 :1096–1111, 1997     

运用模糊神经网络表达和预测链烷烃pVT性质

采用一种基于遗传算法的新型模糊神经网络方法研究链烷烃类化合物的pVT性质。该方法综合神经网络、遗传算法与模糊系统三种柔性智能计算技术的优点,具有良好的学习能力,不易陷入局部最小区域,学习速度较快,网络知识以模糊语言变量的形式加以表达,易于理解。用分子连接性指数对24种链烷烃化合物结构和pVT数据进行学习,进而预测另外14种未知化合物的pVT性质,较好地揭示出化合物分子结构与pVT性质之间的关系,并给出了良好的关联与预测结果。     

Global cluster geometry optimization by a phenotype algorithm with Niches: Location of elusive minima,and low-order scaling with cluster size

The problem of global geometry optimization of clusters is addressed with a phenotype variant of the method of genetic algorithms, with several novel performance enhancements. The resulting algorithm is applied to Lennard–Jones clusters as benchmark system, with up to 150 atoms. The well-known, difficult cases involving nonicosahedral global minima can be treated reliably using the concept of niches. The scaling of computer time with cluster size is approximately cubic, which is crucial for future applications to much larger clusters. © 1999 John Wiley & Sons, Inc. J Comput Chem 20: 1752–1759, 1999     

A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification

In the analysis of gene expression profiles, the number of tissue samples with genes expression levels available is usually small compared with the number of genes. This can lead either to possible overfitting or even to a complete failure in analysis of microarray data. The selection of genes that are really indicative of the tissue classification concerned is becoming one of the key steps in microarray studies. In the present paper, we have combined the modified discrete particle swarm optimization (PSO) and support vector machines (SVM) for tumor classification. The modified discrete PSO is applied to select genes, while SVM is used as the classifier or the evaluator. The proposed approach is used to the microarray data of 22 normal and 40 colon tumor tissues and showed good prediction performance. It has been demonstrated that the modified PSO is a useful tool for gene selection and mining high dimension data.