Abstract
Prediction and reconstruction of metabolic pathways play significant roles in many fields such as genetic engineering, metabolic engineering, drug discovery, and are becoming the most active research topics in synthetic biology. With the increase of related data and with the development of machine learning techniques, there have many machine leaning based methods been proposed for prediction or reconstruction of metabolic pathways. Machine learning techniques are showing state-of-the-art performance to handle the rapidly increasing volume of data in synthetic biology. To support researchers in this field, we briefly review the research progress of metabolic pathway reconstruction and prediction based on machine learning. Some challenging issues in the reconstruction of metabolic pathways are also discussed in this paper.
Highlights
Metabolic pathways are a series of enzymatic reactions in a cell, where the products of reactions are the substrates for subsequent reactions
The purpose of evaluating candidates is to select the missing enzymes catalyzing the specific reactions from the candidates, and there have many approaches been proposed for the evaluation
Yamanishi et al (2007) made the prediction of the encoding genes of missing enzymes based on the scores of the candidates and the chemical reaction information encoded in the EC number
Summary
Metabolic pathways are a series of enzymatic reactions in a cell, where the products of reactions are the substrates for subsequent reactions. There are many metabolic pathways have been identified out and been stored and characterized in several public repositories according to their functions, including KEGG (Ogata et al, 1998; Ogata et al, 1999; Okuda et al, 2008; Kanehisa et al, 2019), MetaCyc (Karp 2002b; Caspi 2006; Caspi et al, 2008; Caspi et al, 2018), BioCyc (Karp et al, 2019). If some enzymes or reactions are missed in reference pathways, such reference-based methods may reconstruct incorrect metabolic pathways and lead to incorrect elucidation. Such kind of methods cannot predict new reactions
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