Abdul Hakim Mohamed Salleh

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A hybrid of ant colony optimization and minimization of metabolic adjustment to improve the production of succinic acid in Escherichia coli.

This paper presents a study on gene knockout strategies to identify candidate genes to be knocked out for improving the production of succinic acid in Escherichia coli. Succinic acid is widely used as a precursor for many chemicals, for example production of antibiotics, therapeutic proteins and food. However, the chemical syntheses of succinic acid using the traditional methods usually result in the production that is far below their theoretical maximums. In silico gene knockout strategies are commonly implemented to delete the gene in E. coli to overcome this problem. In this paper, a hybrid of Ant Colony Optimization (ACO) and Minimization of Metabolic Adjustment (MoMA) is proposed to identify gene knockout strategies to improve the production of succinic acid in E. coli. As a result, the hybrid algorithm generated a list of knockout genes, succinic acid production rate and growth rate for E. coli after gene knockout. The results of the hybrid algorithm were compared with the previous methods, OptKnock and MOMAKnock. It was found that the hybrid algorithm performed better than OptKnock and MOMAKnock in terms of the production rate. The information from the results produced from the hybrid algorithm can be used in wet laboratory experiments to increase the production of succinic acid in E. coli.

Database and tools for metabolic network analysis

Metabolic network analysis has attracted much attention in the area of systems biology. It has a profound role in understanding the key features of organism metabolic networks and has been successfully applied in several fields of systems biology, including in silico gene knockouts, production yield improvement using engineered microbial strains, drug target identification, and phenotype prediction. A variety of metabolic network databases and tools have been developed in order to assist research in these fields. Databases that comprise biochemical data are normally integrated with the use of metabolic network analysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as well as twenty one recent tools. The aim of this review is to study the different types of tools in terms of the features and usability, as well as the databases in terms of the scope and data provided. These tools can be categorised into three main types: standalone tools; toolbox-based tools; and web-based tools. Furthermore, comparisons of the databases as well as the tools are also provided to help software developers and users gain a clearer insight and a better understanding of metabolic network analysis. Additionally, this review also helps to provide useful information that can be used as guidance in choosing tools and databases for a particular research interest.

Identifying a gene knockout strategy using a hybrid of the bat algorithm and flux balance analysis to enhance the production of succinate and lactate in Escherichia coli

The current problem for metabolic engineering is how to identify a suitable set of genes for knockout that can improve the production of certain metabolites and sustain the growth rate from the thousands of metabolic networks which are complex and combinatorial. Some approaches, such as OptKnock and OptGene, are developed to enhance the production of desired metabolites. However, the performances of these approaches are suboptimal and the obtained results are unsatisfactory because of computational limitations such as local minima. In this paper, we propose a hybrid of Bat Algorithm and Flux Balance Analysis (BATFBA) to enhance succinate and lactate production by identifying a set of genes for knock out. The Bat Algorithm is an optimisation algorithm, whereas Flux Balance Analysis (FBA) is a mathematical approach to analyse the flow of metabolites through a metabolic network. The Escherichia coli iJR904 dataset was used to determine optimal knockout genes, production rate, and growth rate. By applying this hybrid method to the iJR904 dataset, we found that BATFBA yielded better results than existing methods, such as OptKnock and a hybrid of Artificial Bee Colony algorithms and Flux Balance Analysis (ABCFBA), at predicting succinate and lactate production.

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.

Identifying minimal genomes and essential genes in metabolic model using flux balance analysis

With the advancement in metabolic engineering technologies, reconstruction the genome of a host organism to achieve desired phenotypes for example, to optimize the production of metabolites can be made. However, due to the complexity and size of the genome scale metabolic network, significant components tend to be invisible. This research utilizes Flux Balance Analysis (FBA) to search the essential genes and obtain minimal functional genome. Different from traditional approaches, we identify essential genes by using single gene deletions and then we identify the significant pathway for the metabolite production using gene expression data. The experiment is conducted using genome scale metabolic model of Saccharomyces Cerevisiae for L-phenylalanine production. The result has shown the reliability of this approach to find essential genes for metabolites productions, reduce genome size and identify production pathway that can further optimize the production yield and can be applied in solving other genetic engineering problems.

Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains.

Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Bakers yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms.

A Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis for Enhancing Lactate and Succinate in Escherichia Coli.

In the past decades, metabolic engineering has received great attention from different sectors of science due to its important role in enhancing the over expression of the target phenotype by manipulating the metabolic pathway. The advent of metabolic engineering has further laid the foundation for computational biology, leading to the development of computational approaches for suggesting genetic manipulation. Previously, conventional methods have been used to enhance the production of lactate and succinate in E. coli. However, these products are always far below their theoretical maxima. In this research, a hybrid algorithm is developed to seek optimal solutions in order to increase the overproduction of lactate and succinate by gene knockout in E. coli. The hybrid algorithm employed the Simple Constrained Artificial Bee Colony (SCABC) algorithm, using swarm intelligence as an optimization algorithm to optimize the objective function, where lactate and succinate productions are maximized by simulating gene knockout in E. coli. In addition, Flux Balance Analysis (FBA) is used as a fitness function in the SCABC algorithm to assess the growth rate of E. coli and the productivity of lactate and succinate. As a result of the research, the gene knockout list which induced the highest production of lactate and succinate is obtained.

A Hybrid of Harmony Search and Minimization of Metabolic Adjustment for Optimization of Succinic Acid Production

Succinic acid has been favored by researchers due to its industrial multi-uses. However, the production of succinic acid is far below cell theoretical maximum. The goal of this research is to identify the optimal set of gene knockouts for obtaining high production of succinic acid in microorganisms. Gene knockout is a widely used genetic engineering technique. Hence, a hybrid of Harmony Search (HS) and Minimization of Metabolic Adjustment (MOMA) is proposed. The dataset applied is a core Escherichia coli metabolic network model. Harmony Search is a meta-heuristic algorithm inspired by musicians’ improvisation process. Minimization of Metabolic Adjustment is used to calculate fitness closest to the wild-type, after mutant gene knockout. The result obtained from the proposed hybrid technique are knockout genes list and production rate after the deletion. This proposed technique is possible to be applied in wet laboratory experiment to increase the production of succinic acid in E. coli.

Metabolites production improvement by identifying minimal genomes and essential genes using flux balance analysis

With the advancement in metabolic engineering technologies, reconstruction of the genome of host organisms to achieve desired phenotypes can be made. However, due to the complexity and size of the genome scale metabolic network, significant components tend to be invisible. We proposed an approach to improve metabolite production that consists of two steps. First, we find the essential genes and identify the minimal genome by a single gene deletion process using Flux Balance Analysis (FBA) and second by identifying the significant pathway for the metabolite production using gene expression data. A genome scale model of Saccharomyces cerevisiae for production of vanillin and acetate is used to test this approach. The result has shown the reliability of this approach to find essential genes, reduce genome size and identify production pathway that can further optimise the production yield. The identified genes and pathways can be extendable to other applications especially in strain optimisation.