Toward fully characterized knowledge gaps in metabolic networks: Discovery of missing biochemistry in Escherichia coli

Conference Dates

July 16-20, 2017


Advances in medicine and biotechnology rely on the further understanding of biological processes. Despite the technological advances and increasing available types and amounts of omics data, significant biochemical knowledge gaps remain uncharacterised. Therefore, we need methods that enable analysing the growing sets of data and identifying the knowledge gaps in a systematic way. In this study, we develop an approach to classify and characterise the knowledge gaps in metabolic networks. We use the recently developed ATLAS of Biochemistry as an upper bound on the missing biochemistry and a guide to fill the gaps since it suggests more than 130,000 possible enzymatic reactions between known biological compounds. We identify alternative metabolic reactions from the ATLAS of Biochemistry1 and the KEGG database that can fill the gaps present in the metabolic network and we rank the alternative solutions based on a set of criteria, such as the thermodynamic feasibility of the reactions in the intracellular conditions. We further used a cheminformatics tool to compare the sequence similarity of the alternative gap-filled enzymes with the ORFs of closely related organisms. We apply our approach to the latest genome-scale model of Escherichia coli (iJO1366) and develop a database of top suggested biochemistry that can fill its knowledge gaps. Interestingly, some gaps cannot be filled with the ATLAS of Biochemistry, and represent biochemical bottlenecks for further analysis. Overall, our approach is a valuable tool for the reconstruction and further refinement of metabolic networks, and our results will accelerate experimental studies toward fully annotated ORFs. 1Hadadi, N., Hafner, J., Shajkofci, A., Zisaki, A., & Hatzimanikatis, V. (2016). ATLAS of Biochemistry: A repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies. ACS Synthetic Biology, 2016.

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