Safaai Deris

An improved gSVM-SCADL2 with firefly algorithm for identification of informative genes and pathways

Incorporation of pathway knowledge into microarray analysis has been favoured by researchers owing to the improved biological interpretation of the analysis outcome. However, most of the pathway data are manually curated without specific biological context. Inclusion of non-informative genes in the analysis of context specific microarray data could lead to classifier with poor discriminative power. Thus, one of the main challenges is how to effectively identify informative genes from the pathway data. This paper proposes a firefly optimised penalised support vector machine with SCADL2 penalty function SVM-SCADL2-FFA in optimising tuning parameters for each pathway for efficient identification of informative genes and pathways. Experiments are done on lung cancer and gender data sets. Tenfold CV is used to evaluate the performance in terms of accuracy, specificity, sensitivity and F-score. The identified informative genes are validated through online databases. Our proposed method shows consistent improvements compared to previous works.

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.

Gene knockout identification for metabolite production improvement using a hybrid of genetic ant colony optimization and flux balance analysis

The increasing demand of biochemical supply for various industries has spurred the development of metabolic engineering to find the optimal design of the microbial cell factories. Traditional method of chemical synthesis using the natural producer leads to the production far below their theoretical maximums. Gene knockout strategy is then introduced to improve the metabolite production. To aid the process, many computational algorithms have been developed to design the optimal microbial strain as cell factories to increase the production of the desired metabolite. However, due to the size of the genome scale model of the microbial strain, finding the optimal combination of genes to be knocked out is not an easy task. In this paper, we propose a hybrid of Genetic Ant Colony Optimization (GACO) and Flux Balance Analysis (FBA) namely GACOFBA to find the optimal gene knockout that increase the production of the target metabolite. Using E. coli and S. cerevisiae genome scale model, we test our proposed hybrid algorithm to increase the production of four different metabolites. By comparing with the results from existing method OptKnock as well as the conventional Ant Colony Optimization (ACO), the results show that our proposed hybrid algorithm able to identify the best set of genes and increase the production while maintaining the optimal growth rate.

Automated biological pathway knowledge retrieval based on semantic web services composition and AI Planning

This paper presents the experience gained on semantic web service composition technique applied to the bioinformatics domain. Specifically, the approach presented here consists of knowledge retrieval perspective in biological pathway. Semantic web services, annotated with domain ontology are used to describe services for pathway knowledge retrieval for Kyoto Encyclopedia of Gene and Genomes (KEGG) database. Retrieving knowledge can be seen as high level goals and the tasks involved can be decomposed into subtask to achieve the specified goals. We execute the composition of service by treating composition as planning problem using Hierarchical Task Network (HTN) planning system based on Simple Hierarchical Order Planner 2 (SHOP2). The approach for plan (task) decomposition using SHOP2 is implemented in automated way. We investigate the effectiveness of this approach by applying real world scenario in pathway information retrieval for Lactococcus Lactis (L. lactis) organism where biologists need to find out the pathway description from the given specific gene of interest.

An enhanced scatter search with combined opposition-based learning for parameter estimation in large-scale kinetic models of biochemical systems

An enhanced scatter search (eSS) with combined opposition-based learning algorithm is proposed to solve large-scale parameter estimation in kinetic models of biochemical systems. The proposed algorithm is an extension of eSS with three important improvements in terms of: reference set (RefSet) formation, RefSet combination, and RefSet intensification. Due to the difficulty in estimating kinetic parameter values in the presence of noise and large number of parameters (high-dimension), the aforementioned eSS mechanisms have been improved using combination of quasi-opposition and quasi-reflection, which were under the family of opposition-based learning scheme. The proposed algorithm is tested using one set of benchmark function each from large-scale global optimization (LSGO) problem as well as parameter estimation problem. The LSGO problem consists of 11 functions with 1000 dimensions. For parameter estimation, around 116 kinetic parameters in Chinese hamster ovary (CHO) cells and central carbon metabolism of E. coli are estimated. The results revealed that the proposed algorithm is superior to eSS and other competitive algorithms in terms of its efficiency in minimizing objective function value and having faster convergence rate. The proposed algorithm also required lower computational resources, especially number of function evaluations performed and computation time. In addition, the estimated kinetic parameter values obtained from the proposed algorithm produced the best fit to a set of experimental data.

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.

A Review of Gene Knockout Strategies for Microbial Cells

BACKGROUND Predicting the effects of genetic modification is difficult due to the complexity of metabolic net- works. Various gene knockout strategies have been utilised to deactivate specific genes in order to determine the effects of these genes on the function of microbes. Deactivation of genes can lead to deletion of certain proteins and functions. Through these strategies, the associated function of a deleted gene can be identified from the metabolic networks. METHODS The main aim of this paper is to review the available techniques in gene knockout strategies for microbial cells. The review is done in terms of their methodology, recent applications in microbial cells. In addition, the advantages and disadvantages of the techniques are compared and discuss and the related patents are also listed as well. RESULTS Traditionally, gene knockout is done through wet lab (in vivo) techniques, which were conducted through laboratory experiments. However, these techniques are costly and time consuming. Hence, various dry lab (in silico) techniques, where are conducted using computational approaches, have been developed to surmount these problem. CONCLUSION The development of numerous techniques for gene knockout in microbial cells has brought many advancements in the study of gene functions. Based on the literatures, we found that the gene knockout strategies currently used are sensibly implemented with regard to their benefits.

Analyzing and sense making of human factors in the Malaysian radiation and nuclear emergency planning framework

The evolution of current Radiation and Nuclear Emergency Planning Framework (RANEPF) simulator emphasizes on the human factors to be analyzed and interpreted according to the stakeholder’s tacit and explicit knowledge. These human factor criteria are analyzed and interpreted according to the “sense making theory” and Disaster Emergency Response Management Information System (DERMIS) design premises. These criteria are corroborated by the statistical criteria. In recent findings, there were no differences of distributions among the stakeholders according to gender and organizational expertise. These criteria are incrementally accepted and agreed the research elements indicated in the respective emergency planning frameworks and simulator (i.e. 78.18 to 84.32, p-value <0.05). This paper suggested these human factors criteria in the associated analyses and theoretical perspectives to be further acomodated in the future simulator development. This development is in conjunction with the proposed hypothesis building of the process factors and responses diagram. We proposed that future work which implies the additional functionality of the simulator, as strategized, condensed and concise, comprehensive public disaster preparedness and intervention guidelines, to be a useful and efficient computer simulation.

Metaheuristic Optimization for Parameter Estimation in Kinetic Models of Biological Systems - Recent Development and Future Direction

Background: Kinetic models with predictive ability are important to be used in industrial biotechnology. However, the most challenging task in kinetic modeling is parameter estimation, which can be addressed using metaheuristic optimization methods. The methods are utilized to minimize scalar distance between model output and experimental data. Due to highly nonlinear nature of biological systems and large number of kinetic parameters, parameter estimation becomes difficult and time consuming. Methods: This paper provides a review on recent development of parameter estimation methods, which has received increasing attention in the field of systems biology. The development of metaheuristic optimization methods is mostly focused in this review along with the development of large-scale kinetic models. Results: Although a plethora of methods have been applied to the problem of parameter estimation, recent results show that most of the successful approaches are those based on hybrid methods and parallel strategies. In addition, the current software used for parameter estimation and the sources of biological data for kinetic modeling are also described in this review. This review also presents future direction in parameter estimation to meet current industrial demands, especially in systems biology applications. Conclusion: The development of numerous optimization methods for parameter estimation in kinetic models has brought much advancement in the application of systems biology. Currently, it seems that there are highly demanded for further development of efficient optimization methods to address the expansion of systems biology applications.

Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems

Abstract Industrial bioprocesses development nowadays is concerned with producing chemicals using yeast, bacteria and therapeutic proteins in mammalian cells. This involves the utilization of microorganism cells as factories and re-engineering them in silico. The tools that could facilitate this process are known as the kinetic models. Kinetic models of cellular metabolism are important in assisting researchers to understand the rational design of biological systems, predicting metabolites production, and improving bio-products development. However, the most challenging task in model development is parameter estimation, which is the process of identifying an unknown value of model parameters which provides the best fit between the model output and a set of experimental data. Due to the increased complexity and high dimensionality of the models, which are extremely nonlinear and contain large numbers of kinetic parameters, parameter estimation is known to be difficult and time-consuming. This study proposes a cooperative enhanced scatter search with opposition-based learning schemes (CeSSOL) for parameter estimation in large-scale biology models. The method was executed in parallel with the proposed cooperative mechanism in order to exchange information (kinetic parameters) between individual threads. Each thread consists of different parameters settings that enhance the systemic properties in obtaining the global minimum. The performance of the proposed method was assessed against two large-scale microorganisms models using mammalian and bacteria cells. The results revealed that the proposed method recorded faster computation time compared to other methods. The study has also demonstrated that the proposed method can be used to provide more accurate and faster estimation of kinetic models, indicating the potential benefits of utilizing this method for expert systems of industrial biotechnology.