Chuii Khim Chong

Differential Bees Flux Balance Analysis with OptKnock for In Silico Microbial Strains Optimization

Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.

A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains.

Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy.

Identifying Gene Knockout Strategies Using a Hybrid of Bees Algorithm and Flux Balance Analysis for in Silico Optimization of Microbial Strains

Genome-scale metabolic networks reconstructions from different organisms have become popular in recent years. Genetic engineering is proven to be able to obtain the desirable phenotypes. Optimization algorithms are implemented in previous works to identify the effects of gene knockout on the results. However, the previous works face the problem of falling into local minima. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to solve the local minima problem and to predict optimal sets of gene deletion for maximizing the growth rate of certain metabolite. This paper involves two case studies that consider the production of succinate and lactate as targets, by using E.coli as model organism. The results from this experiment are the list of knockout genes and the growth rate after the deletion. BAFBA shows better results compared to the other methods. The identified list suggests gene modifications over several pathways and may be useful in solving challenging genetic engineering problems.

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.

Improved Differential Evolution Algorithm for Parameter Estimation to Improve the Production of Biochemical Pathway

This paper introduces an improved Differential Evolution algorithm (IDE) which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE) in this paper is a hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF). The outcome of IDE is proven to be superior than Genetic Algorithm (GA) and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms

Inferring Gene Regulatory Networks from Gene Expression Data by a Dynamic Bayesian Network-Based Model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships.

Prediction of Vanillin Production in Yeast Using a Hybrid of Continuous Bees Algorithm and Flux Balance Analysis (CBAFBA)

Most food and beverage is containing artificial flavor compound. Creation of artificial flavors is not an easy step and it is hardly ever completely effective. In this paper, we introduce an in silico method in optimization of microbial strains of flavor compound synthesis. Previously, there are several algorithms such as Genetic Algorithm, Evolutionary Algorithm, OptKnock tool and other related techniques are widely used to predict the yield of target compound by suggesting the gene knockouts. The used of these algorithms or tools is able to predict the yield of production instead of using try and error method for gene deletions. Nowadays, without using in silico method, the direct experiment methods are not cost effective and time consumed. As we know, the cost of chemical is expensive and not all flavorist able to afford the cost. However, the main limitations of previous algorithms are it failed to optimize the prediction of the yield and suggesting unrealistic flux distribution. Therefore, this paper proposed a hybrid of continuous Bees algorithm and Flux Balance Analysis. The target compound in this research is vanillin. The aim of study is to identify optimum gene knockouts. The results in this paper are the prediction of the yield and the growth rate values of the model. The predictive results showed that the improvement in term of yield which may help in food flavorings.

Gene Knockout Identification Using an Extension of Bees Hill Flux Balance Analysis

Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

A Review on Modelling Methods, Pathway Simulation Software and Recent Development on Differential Evolution Algorithms for Metabolic Pathways in Systems Biology

The process of analysing metabolic pathways mathematically in systems biology requires accurate parameters which represent the simulated in-vivo processes. Differential Evolution algorithm (DE) has been provenable to outperform other estimation algorithms, and has been used to achieve an accurate estimation of the required parameters. The total number of DE-variants has increased recently, which makes researchers face difficulties in obtaining complete overview of current DE-variants. To provide a holistic view to the area of mathematical modelling in System Biology, through this paper, we present an overview of modelling methods, parameter estimation by utilizing DE-variants to analyse metabolic pathways, and exiting pathway simulation software.