  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.
