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The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities, as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm, Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with an objective function which is dynamically built based on the biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds classification models for each disease to diagnose patients and predict their subgroups based on the computed metabolic network changes. We demonstrate the use of Metabolitics on three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully diagnose patients by f1-score of over 90% on the average. Besides, in addition to the confirmation of previously reported breast cancer associated pathways, we discovered that Butanoate metabolism experiences significantly decreased activity, while Arginine and Proline Metabolism is subject to significant increase in activity, which have not been reported before. Metabolitics is made available as a Python package in pypi.