Personalized Metabolic Analysis of Diseases.

Cakmak A., Celik M.

IEEE/ACM transactions on computational biology and bioinformatics, cilt.18, ss.1014-1025, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 18
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1109/tcbb.2020.3008196
  • Dergi Adı: IEEE/ACM transactions on computational biology and bioinformatics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, BIOSIS, Biotechnology Research Abstracts, Communication Abstracts, Compendex, EMBASE, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1014-1025
  • Anahtar Kelimeler: Diseases, Biochemistry, Metabolomics, Linear programming, Computational modeling, Predictive models, Biological system modeling, Systems biology, biomedical informatics, classification algorithms, metabolomics, supervised learning, GENE SET ANALYSIS, SYSTEMS BIOLOGY, CANCER, EXPRESSION, SELECTION, VARIABILITY, DATABASE, THERAPY, NETWORK, ACETATE
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.