Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data

Identification of informative subpathways and genes using improved differential expression analysis for pathways method

Pathway-based analysis is introduced to define useful biological knowledge by considering the whole pathway features. However, most of these analyses have several shortcomings, such as less sensitivity towards data that could lead to some important information being missed. Because of the deficiency, pathway-based analysis has been shifted to subpathway-based analysis, which is seen to be more relevant in understanding the biological reactions. This is strengthened by the fact that several studies have found abnormalities in pathways caused by certain regions that respond in the etiology of diseases. In addition, subpathway-based analysis has been found to be more effective and sensitive than the whole pathway. Due to this orientation, many tools have been developed to accomplish the inadequate interpretation in biology system. The Differential Expression Analysis for Pathway (DEAP) is one of the methods in subpathway-based analysis which identifies a local region perturbed by complex diseases in large pathway data. However, the method has shown low performance in identifying informative pathway and subpathway. Hence, this research proposes a modified DEAP method (termed iDEAP) for enhancing the identification of perturbed subpathways in pathway activities and aimed at achieving higher performance in the detection of differential expressed pathways. To this end, firstly, asearch algorithm adapted from DMSP algorithm was implemented to DEAP in search for informative subpathways. Secondly, the relation among subpathways was taken into account by averaging the maximum absolute value (termed DEAP score) to emphasize the reaction among subpathways so that efficient identification of informative pathways can be achieved. Three gene expression data sets were applied in this study (head and neck tumour, colorectal cancer and breast cancer). The results were obtained in terms of the number of differential expressed pathways (head and neck tumor-81 pathways, colorectal cancer-78 pathways, breast cancer-95 pathways) and they suggest that the proposed method yielded better performance as compared to previous work. In fact, when the selected genes from the results were evaluated using 10-fold CV in terms of accuracy, the proposed method showed higher accuracy for Colorectal (90%) and Breast cancer (94%). Finally, a biological validation was conducted on the top five (5) significant pathways and selected genes based on biological literatures

A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli

Ethanol is a chemical-colourless compound that widely used in pharmaceutical, medicines, food products, and industrial applications. As the demand for ethanol is rising recently, attention has been given on metabolic engineering of Escherichia coli (E.coli) to enhance its production through alteration of its genetic content. This research mainly aimed to optimize ethanol production in E.coli using a gene knockout strategy. Several gene knockout strategies like OptKnock and OptGene have been proposed previously. However, most of them suffer from premature convergence. Hence, a hybrid of Particle Swarm Optimization (PSO) and Minimization of Metabolic Adjustment (MOMA) algorithm is proposed to identify the list of gene knockouts in maximizing the ethanol production and growth rate of E.coli. Experiment results show that the hybrid method is comparable with two state-of-the-art methods in term of growth rate and production

Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli

The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment