Recently, a bio-based chemical product such as xylitol has been in the spotlight and has caught attention of many researchers due to the advantages offered by xylitol. Xylitol has been highly demanded in food, odontological and pharmaceutical industries. Xylitol has been widely used in sugar free chewing gum as a sugar substitute and it is also has possibility of being used as a sweetener in tonics, syrups and vitamin formulations in the food industries [1]. Besides that, it is widely used in odontological industry due to its anticariogenecity, tooth rehardening and remineralization properties. Moreover, it is also believed that due to its tooth friendly nature, xylitol can prevent the tooth decay problem as well as the ear infection. Xylitol
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coli is by using gene knockout strategy. Gene knockout strategy is the strategy used to investigate effect of each gene in the metabolic pathway of an E. coli by deactivates certain genes function to observe their effect. Gene knockout strategy often used due to the complexity of the metabolic network in an organism which always makes the effect of genetic modification is hard to predict [4]. Gene knockout strategy is a very powerful tool in industry [5] to study the function of a gene in the most effective way and can be relatively easily achieved in most cell types by using either in vivo or in silico methods. However, using gene knockout strategy in wet lab (in vivo) often faces the issues of highly cost and time consuming. Therefore, in silico technique for metabolic engineering has been developed to overcome this problem. In silico technique usually has computing power which has grown exponentially …show more content…
coli metabolic pathway in order to investigate certain genes functions towards the production of xylitol in E. coli and to find the best way to produce more xylitol in E. coli. Therefore advanced computational technology such as Flux Balance Analysis (FBA) can be used as a simulation tool to observe and manipulate the metabolic pathway of E. coli. Meanwhile, deep learning method such as Deep Neural Network (DNN) can be used as an analysis tool to predict the production value of xylitol in E. coli. Despite the successfulness of DNN, this method still suffers from several limitations that may limit its prediction analysis result. One of the limitations that always occur in DNN is stuck at local minima which may result in lower performance and high computational time [7]. In order to solve this problem, global optimization technique such as Differential Search Algorithm (DSA) can be taken into consideration to help and assist DNN in order to get better prediction result.
Main aim of this paper is to do simulation on the E. coli metabolic pathway to observe function of genes in E. coli and manipulate the metabolic pathway of E. coli by adding and deleting certain genes (gene knockout strategy) that may led to the high production of xylitol in E. coli. DNN also will be used as an analysis tool to predict the best condition of gene knockout strategy that can help E. coli to produce
coli is by using gene knockout strategy. Gene knockout strategy is the strategy used to investigate effect of each gene in the metabolic pathway of an E. coli by deactivates certain genes function to observe their effect. Gene knockout strategy often used due to the complexity of the metabolic network in an organism which always makes the effect of genetic modification is hard to predict [4]. Gene knockout strategy is a very powerful tool in industry [5] to study the function of a gene in the most effective way and can be relatively easily achieved in most cell types by using either in vivo or in silico methods. However, using gene knockout strategy in wet lab (in vivo) often faces the issues of highly cost and time consuming. Therefore, in silico technique for metabolic engineering has been developed to overcome this problem. In silico technique usually has computing power which has grown exponentially …show more content…
coli metabolic pathway in order to investigate certain genes functions towards the production of xylitol in E. coli and to find the best way to produce more xylitol in E. coli. Therefore advanced computational technology such as Flux Balance Analysis (FBA) can be used as a simulation tool to observe and manipulate the metabolic pathway of E. coli. Meanwhile, deep learning method such as Deep Neural Network (DNN) can be used as an analysis tool to predict the production value of xylitol in E. coli. Despite the successfulness of DNN, this method still suffers from several limitations that may limit its prediction analysis result. One of the limitations that always occur in DNN is stuck at local minima which may result in lower performance and high computational time [7]. In order to solve this problem, global optimization technique such as Differential Search Algorithm (DSA) can be taken into consideration to help and assist DNN in order to get better prediction result.
Main aim of this paper is to do simulation on the E. coli metabolic pathway to observe function of genes in E. coli and manipulate the metabolic pathway of E. coli by adding and deleting certain genes (gene knockout strategy) that may led to the high production of xylitol in E. coli. DNN also will be used as an analysis tool to predict the best condition of gene knockout strategy that can help E. coli to produce