Limits...
Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.

Hernández Patiño CE, Jaime-Muñoz G, Resendis-Antonio O - Front Physiol (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Undergraduate Program for Genomic Sciences, Universidad Nacional Autónoma de México Cuernavaca, México.

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.

No MeSH data available.


Related in: MedlinePlus

Central metabolism in cancer cell lines. LAC represents lactate, G6P represents glucose-6phosphate, F6P represents fructose 6 phosphate, FDP represents fructose 1,6 biphospate, G3P represents glyceraldehydes 3 phosphate, 13DPG represents 1,3 biphosoglycerate, 3PG represent 3 phosphoglycerate, 2PG represents 2-phopho glycerate, PEP represents phosphenol pyruvate, PYR represents pyruvate, 6PGCL represents 6-phosphoglucono-δ-lactone, 6PGC represents 6-phosphogluconate, RU5Prepresents ribulose 5-phosphate, R5P represents Ribose 5 phosphate, X5P represents Xylulose 5 phosphate, S7P represents sedoheptulose 7-phosphate, E4P represents erythrose 4-phosphate, OAA represents oxaloacetate, SUC-COA represents succinyl-CoA, ACCOA represents acetyl CoA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3539652&req=5

Figure 2: Central metabolism in cancer cell lines. LAC represents lactate, G6P represents glucose-6phosphate, F6P represents fructose 6 phosphate, FDP represents fructose 1,6 biphospate, G3P represents glyceraldehydes 3 phosphate, 13DPG represents 1,3 biphosoglycerate, 3PG represent 3 phosphoglycerate, 2PG represents 2-phopho glycerate, PEP represents phosphenol pyruvate, PYR represents pyruvate, 6PGCL represents 6-phosphoglucono-δ-lactone, 6PGC represents 6-phosphogluconate, RU5Prepresents ribulose 5-phosphate, R5P represents Ribose 5 phosphate, X5P represents Xylulose 5 phosphate, S7P represents sedoheptulose 7-phosphate, E4P represents erythrose 4-phosphate, OAA represents oxaloacetate, SUC-COA represents succinyl-CoA, ACCOA represents acetyl CoA.

Mentions: As we described above, the Warburg effect is a fundamental metabolic process that contributes to the malignant transformation of most cancer cells. The fundamental nature of this effect makes it attractive to study, and consequently, the understanding of this effect could have conceptual and practical implications for new clinical treatments. With this aim in mind, in this section, we will briefly discuss how constraint-based modeling can be used as a computational tool to characterize the metabolic activity of cancer cells during aerobic glycolysis. Furthermore, we will focus on showing the practical implications of constraint-based modeling that may interest biomedical researchers in the field of cancer research. For the sake of simplicity, our results are constrained to a metabolic reconstruction whose set of reactions represents the central metabolism in a cell exposed to specific external conditions (see Figure 2). Although this metabolic network—containing only 89 reactions and 98 metabolites—is only a subset of the human metabolic reconstruction, we argue that it serves as a baseline to exemplify the method and, simultaneously, as a benchmark to assess the in silico predictions in terms of the experimental data currently found in the literature. In particular, we will show that our method can be used as an auxiliary computational tool for identifying a set of enzymes with an important role in supporting the metabolic phenotype in cancer cells. To explore the metabolic activity in cancer cells using the perspective of constraint-based modeling, three requirements should be met systematically: (1) the metabolic reconstruction of a human cell, (2) the definition of an OF associated with a physiological state, and (3) the completion of computational simulations and the experimental assessment of their outputs.


Systems biology of cancer: moving toward the integrative study of the metabolic alterations in cancer cells.

Hernández Patiño CE, Jaime-Muñoz G, Resendis-Antonio O - Front Physiol (2013)

Central metabolism in cancer cell lines. LAC represents lactate, G6P represents glucose-6phosphate, F6P represents fructose 6 phosphate, FDP represents fructose 1,6 biphospate, G3P represents glyceraldehydes 3 phosphate, 13DPG represents 1,3 biphosoglycerate, 3PG represent 3 phosphoglycerate, 2PG represents 2-phopho glycerate, PEP represents phosphenol pyruvate, PYR represents pyruvate, 6PGCL represents 6-phosphoglucono-δ-lactone, 6PGC represents 6-phosphogluconate, RU5Prepresents ribulose 5-phosphate, R5P represents Ribose 5 phosphate, X5P represents Xylulose 5 phosphate, S7P represents sedoheptulose 7-phosphate, E4P represents erythrose 4-phosphate, OAA represents oxaloacetate, SUC-COA represents succinyl-CoA, ACCOA represents acetyl CoA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3539652&req=5

Figure 2: Central metabolism in cancer cell lines. LAC represents lactate, G6P represents glucose-6phosphate, F6P represents fructose 6 phosphate, FDP represents fructose 1,6 biphospate, G3P represents glyceraldehydes 3 phosphate, 13DPG represents 1,3 biphosoglycerate, 3PG represent 3 phosphoglycerate, 2PG represents 2-phopho glycerate, PEP represents phosphenol pyruvate, PYR represents pyruvate, 6PGCL represents 6-phosphoglucono-δ-lactone, 6PGC represents 6-phosphogluconate, RU5Prepresents ribulose 5-phosphate, R5P represents Ribose 5 phosphate, X5P represents Xylulose 5 phosphate, S7P represents sedoheptulose 7-phosphate, E4P represents erythrose 4-phosphate, OAA represents oxaloacetate, SUC-COA represents succinyl-CoA, ACCOA represents acetyl CoA.
Mentions: As we described above, the Warburg effect is a fundamental metabolic process that contributes to the malignant transformation of most cancer cells. The fundamental nature of this effect makes it attractive to study, and consequently, the understanding of this effect could have conceptual and practical implications for new clinical treatments. With this aim in mind, in this section, we will briefly discuss how constraint-based modeling can be used as a computational tool to characterize the metabolic activity of cancer cells during aerobic glycolysis. Furthermore, we will focus on showing the practical implications of constraint-based modeling that may interest biomedical researchers in the field of cancer research. For the sake of simplicity, our results are constrained to a metabolic reconstruction whose set of reactions represents the central metabolism in a cell exposed to specific external conditions (see Figure 2). Although this metabolic network—containing only 89 reactions and 98 metabolites—is only a subset of the human metabolic reconstruction, we argue that it serves as a baseline to exemplify the method and, simultaneously, as a benchmark to assess the in silico predictions in terms of the experimental data currently found in the literature. In particular, we will show that our method can be used as an auxiliary computational tool for identifying a set of enzymes with an important role in supporting the metabolic phenotype in cancer cells. To explore the metabolic activity in cancer cells using the perspective of constraint-based modeling, three requirements should be met systematically: (1) the metabolic reconstruction of a human cell, (2) the definition of an OF associated with a physiological state, and (3) the completion of computational simulations and the experimental assessment of their outputs.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Undergraduate Program for Genomic Sciences, Universidad Nacional Autónoma de México Cuernavaca, México.

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.

No MeSH data available.


Related in: MedlinePlus