Deep neural network method for the prediction of xylitol production

Bio-based chemical products such as xylitol have achieved remarkable attentions both in pharmaceutical and food industries due to their several advantages such as sugar substitute that can help diabetic patients and help in preventing tooth decay problem. To produce xylitol, recently, microbial host such as E. Coli often used as it is predicted that E. Coli can produce high level of xylitol. Therefore, metabolic engineering need to be done towards E. Coli and powerful tools are needed to manipulate, simulate and analyse the E. Coli metabolic pathway. Artificial intelligence methods such as deep neural network offer an efficient and powerful approach to be used to analyse the xylitol production value and at the same time to predict which genes and pathway that give biggest effect in the process to produce xylitol in E. Coli. Results show that, with an absence of genes pgi, tkt and tala, xylitol production can be boosted up to the higher level

Prediction of bioprocess production using deep neural network method

Deep learning enhanced the state-of-the-art methods in genomics allows it to be used in analysing the biological data with high prediction. The training process of neural network with several hidden layers which has been facilitated by deep learning has been subjected into increased interest in achieving remarkable results in various fields. Thus, the extraction of bioprocess production can be implemented by pathway prediction in genomic metabolic network in eschericia coli. As metabolic engineering involves the manipulation of genes which have the potential to increase the yield of metabolite production. A mathematical model of this network is the foundation for the development of computational procedure that directs genetic manipulations that would eventually lead to optimized bioprocess production. Due to the ability of deep learning to be well suited in terms of genomics, modelling for biological network can be implemented. Each layer reveal the insight of biological network which enable pathway analysis to be implemented in order to extract the target bioprocess production. In this study, deep neural network has been to identify any set of gene deletion models that offers optimal results in xylitol production and its growth yield