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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

Analysis with constraint-based modeling. (A) Having reconstructed the metabolic network, in silico gene deletion allows to identify the phenotype behavior in cancer cell lines. In this case, phenotype is defined in terms of biomass production. Three effects can occur in this situation: (1) genes whose activity is dispensable, (2) genes whose expression reduce the biomass, and (3) genes whose activity is essential to biomass production. Based on in silico analysis, we conclude that lactate dehydrogenase (LDH) has a pivotal metabolic control on cancer cell growth (Resendis-Antonio et al., 2010). Supporting this finding, panel (B) shows the effects that variations on the enzymatic activity of LDH have on some enzymes participating in glycolysis, TCA cycle and pentose phosphate. As the figure shows according the metabolic activity of LDH decrease, we note a reduced activity on all the enzymes integrating these pathways. Regions in red and blue represent a higher (H) and lower (L) metabolic flux activity, respectively. (C) Phenotype phase plane considering the activity of pyruvate dehydrogenase (PDH) and glucose uptake rate.
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Figure 3: Analysis with constraint-based modeling. (A) Having reconstructed the metabolic network, in silico gene deletion allows to identify the phenotype behavior in cancer cell lines. In this case, phenotype is defined in terms of biomass production. Three effects can occur in this situation: (1) genes whose activity is dispensable, (2) genes whose expression reduce the biomass, and (3) genes whose activity is essential to biomass production. Based on in silico analysis, we conclude that lactate dehydrogenase (LDH) has a pivotal metabolic control on cancer cell growth (Resendis-Antonio et al., 2010). Supporting this finding, panel (B) shows the effects that variations on the enzymatic activity of LDH have on some enzymes participating in glycolysis, TCA cycle and pentose phosphate. As the figure shows according the metabolic activity of LDH decrease, we note a reduced activity on all the enzymes integrating these pathways. Regions in red and blue represent a higher (H) and lower (L) metabolic flux activity, respectively. (C) Phenotype phase plane considering the activity of pyruvate dehydrogenase (PDH) and glucose uptake rate.

Mentions: Having defined the uncontrolled cell proliferation in cancer cells using the OF, we can characterize the feasible space of metabolic responses using FBA and eventually identify and explore those metabolic pathways that support the cancer phenotype. Hence, under this assumption, we can identify the metabolic state or states that maximize the OF and, in turn, elucidate the entire metabolic mechanism required to support the cancer phenotype (see Figure 3). Some advances have been reported recently in this type of scheme for studying metabolism in cancer cells. For instance, it has been reported that the constraint-based modeling of central metabolism was able to qualitatively reproduce an experimental growth curve for the HeLa cancer cell line (Resendis-Antonio et al., 2010). Notably, this study provides a computational methodology that contributes to identifying the metabolic reactions whose activities could be crucial to support the cancer phenotype. This latter issue is a worthy goal to optimize the design of drugs targeting human cancer (see Figure 3).


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)

Analysis with constraint-based modeling. (A) Having reconstructed the metabolic network, in silico gene deletion allows to identify the phenotype behavior in cancer cell lines. In this case, phenotype is defined in terms of biomass production. Three effects can occur in this situation: (1) genes whose activity is dispensable, (2) genes whose expression reduce the biomass, and (3) genes whose activity is essential to biomass production. Based on in silico analysis, we conclude that lactate dehydrogenase (LDH) has a pivotal metabolic control on cancer cell growth (Resendis-Antonio et al., 2010). Supporting this finding, panel (B) shows the effects that variations on the enzymatic activity of LDH have on some enzymes participating in glycolysis, TCA cycle and pentose phosphate. As the figure shows according the metabolic activity of LDH decrease, we note a reduced activity on all the enzymes integrating these pathways. Regions in red and blue represent a higher (H) and lower (L) metabolic flux activity, respectively. (C) Phenotype phase plane considering the activity of pyruvate dehydrogenase (PDH) and glucose uptake rate.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Analysis with constraint-based modeling. (A) Having reconstructed the metabolic network, in silico gene deletion allows to identify the phenotype behavior in cancer cell lines. In this case, phenotype is defined in terms of biomass production. Three effects can occur in this situation: (1) genes whose activity is dispensable, (2) genes whose expression reduce the biomass, and (3) genes whose activity is essential to biomass production. Based on in silico analysis, we conclude that lactate dehydrogenase (LDH) has a pivotal metabolic control on cancer cell growth (Resendis-Antonio et al., 2010). Supporting this finding, panel (B) shows the effects that variations on the enzymatic activity of LDH have on some enzymes participating in glycolysis, TCA cycle and pentose phosphate. As the figure shows according the metabolic activity of LDH decrease, we note a reduced activity on all the enzymes integrating these pathways. Regions in red and blue represent a higher (H) and lower (L) metabolic flux activity, respectively. (C) Phenotype phase plane considering the activity of pyruvate dehydrogenase (PDH) and glucose uptake rate.
Mentions: Having defined the uncontrolled cell proliferation in cancer cells using the OF, we can characterize the feasible space of metabolic responses using FBA and eventually identify and explore those metabolic pathways that support the cancer phenotype. Hence, under this assumption, we can identify the metabolic state or states that maximize the OF and, in turn, elucidate the entire metabolic mechanism required to support the cancer phenotype (see Figure 3). Some advances have been reported recently in this type of scheme for studying metabolism in cancer cells. For instance, it has been reported that the constraint-based modeling of central metabolism was able to qualitatively reproduce an experimental growth curve for the HeLa cancer cell line (Resendis-Antonio et al., 2010). Notably, this study provides a computational methodology that contributes to identifying the metabolic reactions whose activities could be crucial to support the cancer phenotype. This latter issue is a worthy goal to optimize the design of drugs targeting human cancer (see Figure 3).

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