Personalized Metabolic Analysis of Diseases

Ali Cakmak*, M. Hasan Celik

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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 called 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 a detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with a dynamically built objective function based on biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds supervised classification models to discriminate between patients and healthy subjects based on the computed metabolic network changes. The use of Metabolitics is demonstrated for three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully differentiate patients from healthy individuals by an average f1-score of 88 percent. Besides, in addition to the confirmation of previously reported breast cancer-associated pathways, we discovered that Biotin Metabolism along with Arginine and Proline Metabolism is subject to a significant increase in flux capacity, which have not been reported before.

Original languageEnglish
Article number9137659
Pages (from-to)1014-1025
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number3
Publication statusPublished - 1 May 2021

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.


This work was supported by the Scientific and Technological Research Council of Turkey (TUB€ İTAK) Career Grant to Ali Cakmak [grant number: 114E115]. Availability: https:// metabolitics.

FundersFunder number
TUB€ İTAK114E115
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu


    • biomedical informatics
    • classification algorithms
    • metabolomics
    • supervised learning
    • Systems biology


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