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2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE) (2012)
Larnaca, Cyprus Cyprus
Nov. 11, 2012 to Nov. 13, 2012
ISBN: 978-1-4673-4357-2
pp: 626-631
Eleftheria Tzamali , Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
Vangelis Sakkalis , Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
Konstantinos Marias , Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece
ABSTRACT
Cancer cells inefficiently produce energy through glycolysis even in ample oxygen, a phenomenon known as “aerobic glycolysis”. A characteristic of the rapid and incomplete catabolism of glucose is the secretion of lactate. Genome-scale metabolic models have been recently employed to describe the glycolytic phenotype of highly proliferating human cancer cells. Genome-scale models describe genotype-phenotype relations revealing the full extent of metabolic capabilities of genotypes under various environmental conditions. The importance of these approaches in understanding some aspects of cancer complexity, as well as in cancer diagnostics and individualized therapeutic schemes related to metabolism is evident. Based on previous metabolic models, we explore the metabolic capabilities and rerouting that occur in cancer metabolism when we apply a strategy that allows near optimal growth solution while maximizing lactate secretion. The simulations show that slight deviations around the optimal growth are sufficient for adequate lactate release and that glucose uptake and lactate secretion are correlated at high proliferation rates as it has been observed. Inhibition of lactate dehydrogenase-A, an enzyme involved in the conversion of pyruvate to lactate, substantially reduces lactate release. We also observe that activating specific reactions associated with the migration-related PLCγ enzyme, the proliferation rate decreases. Furthermore, we incorporate flux constraints related to differentially expressed genes in Glioblastoma Multiforme in an attempt to construct a Glioblastoma-specific metabolic model and investigate its metabolic capabilities across different glucose uptake bounds.
INDEX TERMS
Cancer, Biochemistry, Sugar, Bioinformatics, Genomics, Humans, Tumors, Glioblastoma Multiforme, cancer metabolism, optimal growth, genome-scale network, in-silico modeling
CITATION

E. Tzamali, V. Sakkalis and K. Marias, "The effects of near optimal growth solutions in genome-scale human cancer metabolic model," 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), Larnaca, Cyprus Cyprus, 2012, pp. 626-631.
doi:10.1109/BIBE.2012.6399774
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