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.

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 Baker's 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 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.