Metabolic Process

Abstract: Improvements in bioprocess performance can be achieved by genetic modifications of metabolic control structures. A novel optimization problem helps quantitative understanding and rational metabolic engineering of metabolic reaction pathways. Maximizing the performance of a metabolic reaction pathway is treated as a mixed-integer linear programming formulation to identify changes in regulatory structure and strength and in cellular content of pertinent enzymes which should be implemented to optimize a particular metabolic process. A regulatory superstructure proposed contains all alternative regulatory structures that can be considered for a given pathway. This approach is followed to find the optimal regulatory structure for maximization of phenylalanine selectivity in the microbial aromatic amino acid synthesis pathway. The solution suggests that from the eight feedback inhibitory loops in the original regulatory structure of this pathway, inactivation of at least three loops and overexpression of three enzymes will increase phenylalanine selectivity by 42%. Moreover, novel regulatory structures with only two loops, none of which exists in the original pathway, could result in a selectivity up to 95%.

Abstract: Metabolic pathways refer to the continuous chemical reactions in the metabolic process in vivo. Compounds are the major participant for most metabolic pathways. It is essential to determine which compounds can constitute a metabolic pathway. This problem can be converted to the identification of the metabolic pathways of compounds. Although traditional experiments can provide solid results, they are always of low efficiency and high cost. To date, several machine leaning models have been proposed to address this problem. However, almost all models only identified metabolic pathway types of compounds rather than actual metabolic pathways. This study proposed a novel model for predicting actual metabolic pathways for given compounds. The pairs of compounds and metabolic pathways were termed as samples, thereby modeling a binary classification problem. With the concept of “similarity”, each sample was represented by seven features, extracted from seven associations of compounds, which measure compound linkages from different aspects. The model adopted random forest as the classification algorithm. Two types of ten-fold cross-validation were adopted to evaluate the performance of the model, indicating its utility. A feature analysis was also performed to determine which compound association was highly related to the identification of metabolic pathways of compounds.