Contributions des entrées sur les sorties pour les réseaux métaboliques sur génomes entiers: performances et utilisation pour des études en nutrition humaine

International audienceMetabolic networks are composed by all the biochemical reactions that can take place within an organism, as well as the exchanges of metabolites between the organism and its environment. The metabolic network of many organisms has been reconstructed, either manually or automatically, including at the scale of the entire genome. These models are stored in biological reference databases. Diets can be applied to these networks to model the behavior of organisms, when subjected to a specific diet. In this work, we automate an approach that allows, for each input nutrient in the network, to determine the percentages that are distributed in the different outputs when the organism is forced to evolve in a given diet. We name this approach nAIO (normalized Allocation of inputs on outputs. The nAIO is computed thanks to the inversion of a large-scale matrix and is combined with linear optimization problems. We apply this calculation to all known bacterial networks from studies of the gut microbiota and stored in the Virtual Metabolic Human database. The calculation of nAIOs shows that computation times do not depend on the size of the network but rather on the selected diet. The nAIO calculation also shows that for some bacteria the nAIOsare independent of diet. For these bacteria the nAIOs can be used to make predictions that resultin a linear relationship between the inputs of the system and its outputs

Étude de la variabilité des contributions de nutriments à un réseau métabolique (modélisation, optimisation et application en nutrition)

Nous développons une approche générique pour comprendre comment différents régimes alimentaires peuvent influencer la qualité et la composition du lait. Cette question s'intègre dans le cadre du Flux Balance Analysis (FBA), qui consiste à analyser un réseau métabolique en optimisant un système de contraintes linéaires. Nous avons proposé une extension du FBA pour analyser la transformation des nutriments en intégrant des hypothèses biologiques utilisées par différents modèles numériques dans un modèle générique de la glande mammaire. Notre méthode permet de quantifier les précurseurs qui interviennent dans la composition des sorties du système, en calculant des contributions des entrées dans les sorties [AIO]. A l'aide de cette approche, nous avons montré que la transformation des nutriments du lait ne peut pas être modélisée par l'optimisation d'une combinaison linéaire des flux des réactions sur un modèle du métabolisme mammaire. Pour étudier plus précisément la flexibilité d'un réseau métabolique, nous avons proposé un algorithme efficace de recherche locale pour calculer les valeurs extrémales des coefficients des AIOs. Cette approche permet de discriminer les traitements sans formuler d'hypothèses sur le comportement interne du système.This thesis proposes a generic approach to understanding how different diets affect the quality and composition of milk. This question is addressed in the framework of Flux Balance Analysis (FBA), which considers metabolic network analysis as an optimization issue on a system of linear constraints. In this work, we extended FBA to take into account nutrients transformation by incorporating general assumptions made by various numerical methods in a generic stoichiometric model of the mammary gland. Our method tries to quantify the precursor composition of each system output and to discuss the biological relevance of a set of flux in a given metabolic network. The composition is called contribution of inputs over outputs [AIO]. Using this method on the mammary metabolism, we could show that nutrients transformation cannot be properly modelled by optimizing a linear combination of reactions fluxes in the mammary gland model. In order to further investigate metabolic network flexibility, we have proposed an efficient local search algorithm computing the extremal values of AIO coefficients. This approach enables to discriminate diets without making any assumption on the internal behaviour of the system.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

Étude de la variabilité des contributions de nutriments à un réseau métabolique : modélisation, optimisation et application en nutrition

This thesis proposes a generic approach to understanding how different diets affect the quality and composition of milk. This question is addressed in the framework of Flux Balance Analysis (FBA), which considers metabolic network analysis as an optimization issue on a system of linear constraints. In this work, we extended FBA to take into account nutrients transformation by incorporating general assumptions made by various numerical methods in a generic stoichiometric model of the mammary gland. Our method tries to quantify the precursor composition of each system output and to discuss the biological relevance of a set of flux in a given metabolic network. The composition is called contribution of inputs over outputs [AIO]. Using this method on the mammary metabolism, we could show that nutrients transformation cannot be properly modelled by optimizing a linear combination of reactions fluxes in the mammary gland model. In order to further investigate metabolic network flexibility, we have proposed an efficient local search algorithm computing the extremal values of AIO coefficients. This approach enables to discriminate diets without making any assumption on the internal behaviour of the system.Nous développons une approche générique pour comprendre comment différents régimes alimentaires peuvent influencer la qualité et la composition du lait. Cette question s'intègre dans le cadre du Flux Balance Analysis (FBA), qui consiste à analyser un réseau métabolique en optimisant un système de contraintes linéaires. Nous avons proposé une extension du FBA pour analyser la transformation des nutriments en intégrant des hypothèses biologiques utilisées par différents modèles numériques dans un modèle générique de la glande mammaire. Notre méthode permet de quantifier les précurseurs qui interviennent dans la composition des sorties du système, en calculant des contributions des entrées dans les sorties [AIO]. A l'aide de cette approche, nous avons montré que la transformation des nutriments du lait ne peut pas être modélisée par l'optimisation d'une combinaison linéaire des flux des réactions sur un modèle du métabolisme mammaire. Pour étudier plus précisément la flexibilité d'un réseau métabolique, nous avons proposé un algorithme efficace de recherche locale pour calculer les valeurs extrémales des coefficients des AIOs. Cette approche permet de discriminer les traitements sans formuler d'hypothèses sur le comportement interne du système

Study of the variability of nutrient contributions to a metabolic network : modelling, optimization and application to nutrition

Nous développons une approche générique pour comprendre comment différents régimes alimentaires peuvent influencer la qualité et la composition du lait. Cette question s'intègre dans le cadre du Flux Balance Analysis (FBA), qui consiste à analyser un réseau métabolique en optimisant un système de contraintes linéaires. Nous avons proposé une extension du FBA pour analyser la transformation des nutriments en intégrant des hypothèses biologiques utilisées par différents modèles numériques dans un modèle générique de la glande mammaire. Notre méthode permet de quantifier les précurseurs qui interviennent dans la composition des sorties du système, en calculant des contributions des entrées dans les sorties [AIO]. A l'aide de cette approche, nous avons montré que la transformation des nutriments du lait ne peut pas être modélisée par l'optimisation d'une combinaison linéaire des flux des réactions sur un modèle du métabolisme mammaire. Pour étudier plus précisément la flexibilité d'un réseau métabolique, nous avons proposé un algorithme efficace de recherche locale pour calculer les valeurs extrémales des coefficients des AIOs. Cette approche permet de discriminer les traitements sans formuler d'hypothèses sur le comportement interne du système.This thesis proposes a generic approach to understanding how different diets affect the quality and composition of milk. This question is addressed in the framework of Flux Balance Analysis (FBA), which considers metabolic network analysis as an optimization issue on a system of linear constraints. In this work, we extended FBA to take into account nutrients transformation by incorporating general assumptions made by various numerical methods in a generic stoichiometric model of the mammary gland. Our method tries to quantify the precursor composition of each system output and to discuss the biological relevance of a set of flux in a given metabolic network. The composition is called contribution of inputs over outputs [AIO]. Using this method on the mammary metabolism, we could show that nutrients transformation cannot be properly modelled by optimizing a linear combination of reactions fluxes in the mammary gland model. In order to further investigate metabolic network flexibility, we have proposed an efficient local search algorithm computing the extremal values of AIO coefficients. This approach enables to discriminate diets without making any assumption on the internal behaviour of the system

Contributions des entrées sur les sorties pour les réseaux métaboliques sur génomes entiers: performances et utilisation pour des études en nutrition humaine

International audienceMetabolic networks are composed by all the biochemical reactions that can take place within an organism, as well as the exchanges of metabolites between the organism and its environment. The metabolic network of many organisms has been reconstructed, either manually or automatically, including at the scale of the entire genome. These models are stored in biological reference databases. Diets can be applied to these networks to model the behavior of organisms, when subjected to a specific diet. In this work, we automate an approach that allows, for each input nutrient in the network, to determine the percentages that are distributed in the different outputs when the organism is forced to evolve in a given diet. We name this approach nAIO (normalized Allocation of inputs on outputs. The nAIO is computed thanks to the inversion of a large-scale matrix and is combined with linear optimization problems. We apply this calculation to all known bacterial networks from studies of the gut microbiota and stored in the Virtual Metabolic Human database. The calculation of nAIOs shows that computation times do not depend on the size of the network but rather on the selected diet. The nAIO calculation also shows that for some bacteria the nAIOsare independent of diet. For these bacteria the nAIOs can be used to make predictions that resultin a linear relationship between the inputs of the system and its outputs

Contributions des entrées sur les sorties pour les réseaux métaboliques sur génomes entiers: performances et utilisation pour des études en nutrition humaine

International audienceMetabolic networks are composed by all the biochemical reactions that can take place within an organism, as well as the exchanges of metabolites between the organism and its environment. The metabolic network of many organisms has been reconstructed, either manually or automatically, including at the scale of the entire genome. These models are stored in biological reference databases. Diets can be applied to these networks to model the behavior of organisms, when subjected to a specific diet. In this work, we automate an approach that allows, for each input nutrient in the network, to determine the percentages that are distributed in the different outputs when the organism is forced to evolve in a given diet. We name this approach nAIO (normalized Allocation of inputs on outputs. The nAIO is computed thanks to the inversion of a large-scale matrix and is combined with linear optimization problems. We apply this calculation to all known bacterial networks from studies of the gut microbiota and stored in the Virtual Metabolic Human database. The calculation of nAIOs shows that computation times do not depend on the size of the network but rather on the selected diet. The nAIO calculation also shows that for some bacteria the nAIOsare independent of diet. For these bacteria the nAIOs can be used to make predictions that resultin a linear relationship between the inputs of the system and its outputs

Contributions des entrées sur les sorties pour les réseaux métaboliques sur génomes entiers: performances et utilisation pour des études en nutrition humaine

International audienceMetabolic networks are composed by all the biochemical reactions that can take place within an organism, as well as the exchanges of metabolites between the organism and its environment. The metabolic network of many organisms has been reconstructed, either manually or automatically, including at the scale of the entire genome. These models are stored in biological reference databases. Diets can be applied to these networks to model the behavior of organisms, when subjected to a specific diet. In this work, we automate an approach that allows, for each input nutrient in the network, to determine the percentages that are distributed in the different outputs when the organism is forced to evolve in a given diet. We name this approach nAIO (normalized Allocation of inputs on outputs. The nAIO is computed thanks to the inversion of a large-scale matrix and is combined with linear optimization problems. We apply this calculation to all known bacterial networks from studies of the gut microbiota and stored in the Virtual Metabolic Human database. The calculation of nAIOs shows that computation times do not depend on the size of the network but rather on the selected diet. The nAIO calculation also shows that for some bacteria the nAIOsare independent of diet. For these bacteria the nAIOs can be used to make predictions that resultin a linear relationship between the inputs of the system and its outputs

Contributions des entrées sur les sorties pour les réseaux métaboliques sur génomes entiers: performances et utilisation pour des études en nutrition humaine

International audienceMetabolic networks are composed by all the biochemical reactions that can take place within an organism, as well as the exchanges of metabolites between the organism and its environment. The metabolic network of many organisms has been reconstructed, either manually or automatically, including at the scale of the entire genome. These models are stored in biological reference databases. Diets can be applied to these networks to model the behavior of organisms, when subjected to a specific diet. In this work, we automate an approach that allows, for each input nutrient in the network, to determine the percentages that are distributed in the different outputs when the organism is forced to evolve in a given diet. We name this approach nAIO (normalized Allocation of inputs on outputs. The nAIO is computed thanks to the inversion of a large-scale matrix and is combined with linear optimization problems. We apply this calculation to all known bacterial networks from studies of the gut microbiota and stored in the Virtual Metabolic Human database. The calculation of nAIOs shows that computation times do not depend on the size of the network but rather on the selected diet. The nAIO calculation also shows that for some bacteria the nAIOsare independent of diet. For these bacteria the nAIOs can be used to make predictions that resultin a linear relationship between the inputs of the system and its outputs

Exploring metabolism flexibility in complex organisms through quantitative study of precursor sets for system outputs

BackgroundWhen studying metabolism at the organ level, a major challenge is to understand the matter exchanges between the input and output components of the system. For example, in nutrition, biochemical models have been developed to study the metabolism of the mammary gland in relation to the synthesis of milk components. These models were designed to account for the quantitative constraints observed on inputs and outputs of the system. In these models, a compatible flux distribution is first selected. Alternatively, an infinite family of compatible set of flux rates may have to be studied when the constraints raised by observations are insufficient to identify a single flux distribution. The precursors of output nutrients are traced back with analyses similar to the computation of yield rates. However, the computation of the quantitative contributions of precursors may lack precision, mainly because some precursors are involved in the composition of several nutrients and because some metabolites are cycled in loops. Results We formally modeled the quantitative allocation of input nutrients among the branches of the metabolic network (AIO). It corresponds to yield information which, if standardized across all the outputs of the system, allows a precise quantitative understanding of their precursors. By solving nonlinear optimization problems, we introduced a method to study the variability of AIO coefficients when parsing the space of flux distributions that are compatible with both model stoichiometry and experimental data. Applied to a model of the metabolism of the mammary gland, our method made it possible to distinguish the effects of different nutritional treatments, although it cannot be proved that the mammary gland optimizes a specific linear combination of flux variables, including those based on energy. Altogether, our study indicated that the mammary gland possesses considerable metabolic flexibility. Conclusion Our method enables to study the variability of a metabolic network with respect to efficiency (i.e. yield rates). It allows a quantitative comparison of the respective contributions of precursors to the production of a set of nutrients by a metabolic network, regardless of the choice of the flux distribution within the different branches of the network

A generic stoichiometric model to analyse the metabolic flexibility of the mammary gland in lactating dairy cows

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