Identifying cancer specific metabolic signatures using constraint-based models

A Schultz, S Mehta, C W Hu, F W Hoff, T M Horton, S M Kornblau, A A Qutub

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
43 Downloads (Pure)

Abstract

Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.

Original languageEnglish
Pages (from-to)485-496
Number of pages12
JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Volume22
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Algorithms
  • Cell Line, Tumor
  • Child
  • Computational Biology
  • Humans
  • Leukemia/metabolism
  • Metabolic Networks and Pathways
  • Models, Biological
  • Monte Carlo Method
  • Neoplasms/genetics
  • Proteomics

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