Abstract
One of the main objectives in systems biology is to understand the biological mechanisms that give rise to the phenotype of a microorganism by using high-throughput technologies (HTs) and genome-scale mathematical modeling. The computational modeling of genome-scale metabolic reconstructions is one systemic and quantitative strategy for characterizing the metabolic phenotype associated with human diseases and potentially for designing drugs with optimal clinical effects. The purpose of this short review is to describe how computational modeling, including the specific case of constraint-based modeling, can be used to explore, characterize, and predict the metabolic capacities that distinguish the metabolic phenotype of cancer cell lines. As we show herein, this computational framework is far from a pure theoretical description, and to ensure proper biological interpretation, it is necessary to integrate high-throughput data and generate predictions for later experimental assessment. Hence, genome-scale modeling serves as a platform for the following: (1) the integration of data from HTs, (2) the assessment of how metabolic activity is related to phenotype in cancer cell lines, and (3) the design of new experiments to evaluate the outcomes of the in silico analysis. By combining the functions described above, we show that computational modeling is a useful methodology to construct an integrative, systemic, and quantitative scheme for understanding the metabolic profiles of cancer cell lines, a first step to determine the metabolic mechanism by which cancer cells maintain and support their malignant phenotype in human tissues.
Highlights
Cancer is a complex disease that is characterized by uncontrolled cell growth
There are different schemes for the computational modeling of metabolic pathways, but we focus on Flux Balance Analysis (FBA), a paradigm in systems biology whose utility to understand and predict metabolic states has been proven in a variety of microorganisms (Resendis-Antonio et al, 2007, 2010, 2011, 2012; Orth et al, 2010; Schellenberger et al, 2011; Benedict et al, 2012; Kim and Reed, 2012; Martinez et al, 2012; Paglia et al, 2012; Pardelha et al, 2012; Tilghman et al, 2012)
We have demonstrated how a computational model, based on constraint-based modeling, can be useful to explore the metabolic profile associated with the cancer phenotype
Summary
Cancer is a complex disease that is characterized by uncontrolled cell growth. At the genetic level, cancer research has focused primarily on identifying oncogenes and tumor suppressors that initiate and promote the cancer phenotype (Bishop and Weinberg, 1996). Several processes associated with the progress of this disease have been identified as the hallmarks of cancer These hallmarks are sustained proliferative signaling, the evasion of growth suppressors, resistance to cell death, the induction of angiogenesis, the activation of invasion and metastasis, replicative immortality, tumor-promoting inflammation and the reprogramming of energy metabolism (Hanahan and Weinberg, 2011). Given the much higher efficiency of oxidative phosphorylation, a question arises: what is the metabolic advantage of using glycolysis to sustain the uncontrolled proliferation of cancer cells. To explain this apparent contradiction, Warburg proposed that cancer cells do not have functional mitochondria (Warburg, 1956); contrary to this hypothesis, there is evidence that the oxidative phosphorylation pathway is active in some human cancer cells (Weinhouse, 1976). There is evidence that the prolonged exposure of cells to hypoxia promotes activity that favors the cancer phenotype, including the activation of hypoxia-inducible factor (HIF-1α), which is an important regulator of the metabolic activity of glycolysis and the glucose www.frontiersin.org
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