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A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress.

Achcar F, Camadro JM, Mestivier D - BMC Syst Biol (2011)

Bottom Line: The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments).Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data.All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Modelling in Integrative Biology, Institut Jacques Monod - UMR7592 - CNRS - Univ. Paris-Diderot, Paris, France.

ABSTRACT

Background: In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.

Results: Using a methodology based on probabilistic Boolean modelling, we constructed the first model of yeast iron homeostasis including oxygen-related reactions in the frame of central metabolism. The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments). We removed every gene, thus generating in silico mutants. The simulations of the different mutants gave rise to a remarkably accurate qualitative description of most of the experimental phenotype (overall consistency > 91.5%). A second validation involved analysing the anaerobiosis to aerobiosis transition. Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data. The simulations reproducted accurately ten out of the eleven metabolic fluxes. We show here that our probabilistic Boolean modelling strategy provides a useful description of the dynamics of a complex biological system. A clustering analysis of the simulations of all in silico mutations led to the identification of clear phenotypic profiles, thus providing new insights into some metabolic response to stress conditions. Finally, the model was also used to explore several new hypothesis in order to better understand some unexpected phenotypes in given mutants.

Conclusions: All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

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Related in: MedlinePlus

Variations of the PoP, at steady state, of each element in the model (columns), in each in silico mutant (rows), with respect to WT simulations. For each mutant and for each element,  was calculated. Positive values (red) indicate that the PoP was higher in the mutant simulations and negative values (green) imply that the PoP was higher in the WT simulations. For a complete content of all clusters see additional le 2 (cdt file of this clustering). See methods for details.
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Figure 6: Variations of the PoP, at steady state, of each element in the model (columns), in each in silico mutant (rows), with respect to WT simulations. For each mutant and for each element, was calculated. Positive values (red) indicate that the PoP was higher in the mutant simulations and negative values (green) imply that the PoP was higher in the WT simulations. For a complete content of all clusters see additional le 2 (cdt file of this clustering). See methods for details.

Mentions: We evaluated the potential of the model to identify emerging properties through a global analysis of the simulations of the in silico mutants. In silico mutations were performed on 187 genes of the model. Indeed, it is impossible to predict the outputs of such a large number of simulations. We also included in this meta-analysis the simulations of models in which each of the 11 fixed elements was removed, independently, representing changes in the composition of the growth medium We evaluated whether it was possible to uncover from the simulations of the different models some specific properties of this complex system. A Bayesian classification algorithm (implemented at Autoclass@IJM, [49]) was then used to cluster the outputs of all simulations, defining classes of is-mutants with similar phenotypes and classes of elements varying in a similar manner in the simulations. Genes and elements were clustered into discrete sets (Figure 6 and Table 3). These mutations were grouped into seven classes that were consistent to some extent with the known biology of the system used (hem mutants, met mutants, cys mutants) and with additional mutations in genes with no straightforward relationship to the other members of the classes. This suggests that these mutations may define previously masked metabolic states, some of which it may be impossible to test experimentally, as several elements may be simultaneously missing, leading to cell death in vivo. A detailed analysis of each class showed that most simulations were correlated with biological data, but it was not always possible to predict the class to which a given gene would be assigned. Many is-mutations had only a limited impact on the steady-state PoP of most of the elements of the model when simulated with the predefined set of unlimited elements (Additional file 4). However, several classes of is-mutations had very pronounced phenotypes.


A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress.

Achcar F, Camadro JM, Mestivier D - BMC Syst Biol (2011)

Variations of the PoP, at steady state, of each element in the model (columns), in each in silico mutant (rows), with respect to WT simulations. For each mutant and for each element,  was calculated. Positive values (red) indicate that the PoP was higher in the mutant simulations and negative values (green) imply that the PoP was higher in the WT simulations. For a complete content of all clusters see additional le 2 (cdt file of this clustering). See methods for details.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094212&req=5

Figure 6: Variations of the PoP, at steady state, of each element in the model (columns), in each in silico mutant (rows), with respect to WT simulations. For each mutant and for each element, was calculated. Positive values (red) indicate that the PoP was higher in the mutant simulations and negative values (green) imply that the PoP was higher in the WT simulations. For a complete content of all clusters see additional le 2 (cdt file of this clustering). See methods for details.
Mentions: We evaluated the potential of the model to identify emerging properties through a global analysis of the simulations of the in silico mutants. In silico mutations were performed on 187 genes of the model. Indeed, it is impossible to predict the outputs of such a large number of simulations. We also included in this meta-analysis the simulations of models in which each of the 11 fixed elements was removed, independently, representing changes in the composition of the growth medium We evaluated whether it was possible to uncover from the simulations of the different models some specific properties of this complex system. A Bayesian classification algorithm (implemented at Autoclass@IJM, [49]) was then used to cluster the outputs of all simulations, defining classes of is-mutants with similar phenotypes and classes of elements varying in a similar manner in the simulations. Genes and elements were clustered into discrete sets (Figure 6 and Table 3). These mutations were grouped into seven classes that were consistent to some extent with the known biology of the system used (hem mutants, met mutants, cys mutants) and with additional mutations in genes with no straightforward relationship to the other members of the classes. This suggests that these mutations may define previously masked metabolic states, some of which it may be impossible to test experimentally, as several elements may be simultaneously missing, leading to cell death in vivo. A detailed analysis of each class showed that most simulations were correlated with biological data, but it was not always possible to predict the class to which a given gene would be assigned. Many is-mutations had only a limited impact on the steady-state PoP of most of the elements of the model when simulated with the predefined set of unlimited elements (Additional file 4). However, several classes of is-mutations had very pronounced phenotypes.

Bottom Line: The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments).Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data.All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

View Article: PubMed Central - HTML - PubMed

Affiliation: Modelling in Integrative Biology, Institut Jacques Monod - UMR7592 - CNRS - Univ. Paris-Diderot, Paris, France.

ABSTRACT

Background: In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.

Results: Using a methodology based on probabilistic Boolean modelling, we constructed the first model of yeast iron homeostasis including oxygen-related reactions in the frame of central metabolism. The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments). We removed every gene, thus generating in silico mutants. The simulations of the different mutants gave rise to a remarkably accurate qualitative description of most of the experimental phenotype (overall consistency > 91.5%). A second validation involved analysing the anaerobiosis to aerobiosis transition. Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data. The simulations reproducted accurately ten out of the eleven metabolic fluxes. We show here that our probabilistic Boolean modelling strategy provides a useful description of the dynamics of a complex biological system. A clustering analysis of the simulations of all in silico mutations led to the identification of clear phenotypic profiles, thus providing new insights into some metabolic response to stress conditions. Finally, the model was also used to explore several new hypothesis in order to better understand some unexpected phenotypes in given mutants.

Conclusions: All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.

Show MeSH
Related in: MedlinePlus