Handling the heterogeneity of genomic and metabolic networks data within flexible workflows with the PADMet toolbox

National audienceA main challenge of the era of fast and massive genome sequencing is to transform sequences into biological knowledge. The reconstruction of metabolic networks that include all biochemical reactions of a cell is a way to understand physiology interactions from genomic data. In 2010, Thiele and Palsson described a general protocol enabling the reconstruction of high-quality metabolic networks. Since then several approaches have been implemented for this purpose. They all rely mainly on drafting a first metabolic network from genome annotations and orthology information followed by a gap-filling step. More precisely, in the case of exotic species the lack of good annotations and poor biological information result in incomplete networks. Reference databases of metabolic reactions guide the filling process in order to check whether adding reactions to a network allows compounds of interest to be produced from a given growth media. As a final objective, as soon as the network is considered to be complete enough, functional studies are undergone, often relying on the constraint-based paradigm derived from the Flux Balance Analysis (FBA) framework (Orth et al., 2010). The high diversity of input files and tools required to run any metabolic networks reconstruction protocol represents an important drawback. In addition, most approaches require reference metabolic networks of a template organism. Dictionaries mapping the reference metabolic databases to the gene identifiers corresponding to the studied organism may be required. As a main issue, it appears very difficult to ensure that input files agree among them. Such a heterogeneity produces loss of information during the use of the protocols and generates uncertainty in the final metabolic model. Here we introduce the PADMet-toolbox which allows conciliating genomic and metabolic network information. The toolbox centralizes all this information in a new graph-based format: PADMet (PortAble Database for Metabolism) and provides methods to import, update and export information. For the sake of illustration, the toolbox was used to create a workflow, named AuReMe, aiming to produce high-quality genome-scale metabolic networks and eventually input files to feed most platforms involved in metabolic network analyses. We applied this approach to two exotic organisms and our results evidenced the need of combining approaches and reconciling information to obtain a functional metabolic network to produce biomass

Structure de réseaux biologiques : rôle des nœuds internes vis-à-vis de la production de composés

In this thesis we are interested in metabolic networks and, in particular, their modelling with a weighted directed bipartite graph. This representation makes it possible to study the production of target metabolic elements, constituting a biomass, from components coming from the growth medium of the organism. We focused on the role of metabolites inside the network and the notion of essentiality for this elements for the production of a biomass whose definition we have refined in the case of a flow study (metabolite essential for biomass producibility and metabolite essential for biomass efficiency) and extended this notion in the case of a topological study (metabolite essential for biomass sustainability). We rely on the formalism of Flux Balance Analysis and its derivatives, and of network expansion, in order to define an essential metabolite (ME or crossroad), allowing us to develop a python package (Conquests) looking for crossraods in a metabolic network. We applied our concept to six metabolic networks, four of which came from model species (iJO1360, iAF1260 and iJR904 of E. coli and Synecchocystis) and the other two from more specific species (A. ferrooxidans and T. lutea). We have also defined the concept of cluster of ME-sustainability, related to the biomass components to which they are required and which we have applied over the six previous metabolic networks and over 3600 degraded networks of iJR904 of E. coli and reconstructed according to three methods of gapfilling (Gapfill, Fastgapfill and Meneco) to compare the results. These studies have allowed us to highlight the importance of internal metabolites in the production of target compounds.Durant cette thèse nous nous sommes intéressés aux réseaux métaboliques et notamment leur modélisation sous forme d’un graphe biparti orienté pondéré. Ce dernier permet d’étudier la production d’éléments cibles métaboliques regroupés dans une biomasse à partir de composants provenant du milieu de croissance de l’organisme. Nous nous sommes plus particulièrement penchés sur le rôle des métabolites internes au réseau et la notion d’essentialité de ces derniers pour la production d’une biomasse dont nous avons raffiné la définition dans le cas d’une étude de flux (métabolite essentiel du point de vue de la productibilité du réseau et métabolite essentiel du point de vue de l’efficacité du réseau) puis étendu cette dernière dans le cas d’une étude topologique (métabolite essentiel du point du vue de la persistance du réseau). Nous nous sommes pour cela reposés sur le formalisme d’un part de Flux Balance Analysis et ses dérivés, et d’autre part d’expansion de réseau, afin de définir un métabolite essentiel (ou carrefour), nous permettant de mettre au point un package python (Conquests) cherchant les carrefours dans un réseau métabolite. Nous avons appliqué ce dernier à six réseaux métaboliques dont quatre provenant d’espèces modèles (iJO1360, iAF1260 et iJR904 d’E. coli et Synecchocystis) et les deux autres d’espèces plus spécifiques (A. ferrooxidans et T. lutea). Nous avons aussi défini le concept de cluster de métabolites essentiels du point du vue de la persistance du réseau lié aux composants de la biomasse auxquels ils sont nécessaires et que nous avons appliqué sur les six réseaux métaboliques précédents et sur 3600 réseaux dégradés du réseau iJR904 de E. coli puis reconstruits selon trois méthodes de gapfilling (Gapfill, Fastgapfill et Meneco) afin de comparer ces dernières. Ces études nous ont permis de mettre en avant l’importance de métabolites internes dans la production de composés cibles

Structure of biological networks : role of internal nodes in the production of compounds

Durant cette thèse nous nous sommes intéressés aux réseaux métaboliques et notamment leur modélisation sous forme d'un graphe bipartite dirigé pondéré. Ce dernier permet d'étudier la production d'éléments cibles métaboliques regroupés dans une biomasse à partir de composants provenant du milieu de croissance de l'organisme. Nous nous sommes plus particulièrement penchés sur le rôle des métabolites internes au réseau et la notion d'essentialité de ces derniers pour la production d'une biomasse dont nous avons raffiné la définition dans le cas d'une étude de flux (métabolite essentiel du point de vue de la productibilité du réseau et métabolite essentiel du point de vue de l'efficacité du réseau) puis étendu cette dernière dans le cas d'une étude topologique (métabolite essentiel du point du vue de la persistance du réseau). Nous nous sommes pour cela reposés sur le formalisme d'un part de Flux Balance Analysis et ses dérivés, et d'autre part d'expansion de réseau, afin de définir un métabolite essentiel (ou carrefour), nous permettant de mettre au point un package python (Conquests) cherchant les carrefours dans un réseau métabolite. Nous avons appliqué ce dernier à six réseaux métaboliques dont quatre provenant d'espèces modèles (iJO1360, iAF1260et iJR904 d'E. coli et Synecchocystis) et les deux autres d'espèces plus spécifiques (A. ferrooxidans et T. lutea). Nous avons aussi défini le concept de cluster de métabolites essentiels du point du vue de la persistance du réseau lié aux composants de la biomasse auxquels ils sont nécessaires et que nous avons appliqué sur les six réseaux métaboliques précédents et sur 3600 réseaux dégradés du réseau iJR904de E. coli puis reconstruits selon trois méthodes de gapfilling (Gapfill, Fastgapfill et Meneco) afin de comparer ces dernières. Ces études nous ont permis de mette en avant l'importance de métabolites internes dans la production de composés cibles.In this thesis we are interested in metabolic networks and, in particular, their modelling with a weighted directed bipartite graph. This representation makes it possible to study the production of target metabolic elements, constituting a biomass, from components coming from the growth medium of the organism. We focused on the role of metabolites inside the network and the notion of essentiality for this elements for the production of a biomass whose definition we have refined in the case of a flow study (metabolite essential for biomass producibility and metabolite essential for biomass efficiency) and extended this notion in the case of a topological study (metabolite essential for biomass sustainability). We rely on the formalism of Flux Balance Analysis and its derivatives, and of network expansion, in order to define an essential metabolite (ME or crossroad), allowing us to develop a python package (Conquests) looking for crossroads in a metabolic network. We applied our concept to six metabolic networks, four of which came from model species (iJO1360, iAF1260 and iJR904 of E. coli and Synecchocystis) and the other two from more specific species (A. ferrooxidans and T. lutea). We have also defined the concept of cluster of ME-sustainability, related to the biomass components to which they are required and which we have applied over the six previous metabolic networks and over 3600 degraded networks of iJR904 of E. coli and reconstructed according to three methods of gapfilling (Gapfill, Fastgapfill and Meneco) to compare the results. These studies have allowed us to highlight the importance of internal metabolites in the production of target compounds

Combinatorial optimization methods to complete and analyse a metabolic network

Biological compartments have a important role with respect to the struc- turation of metabolic map and to the control of metabolic transformations within a cell. Such compartments highlight the role of speci?c compounds, namely those transported from a compartment to another, which can be view as metabolite crossroads. We will call them "intermediary metabo- lites". The goal of this talk will be to study the impact of such intermediary metabolite when one is reconstructing a metabolic map from genome scale data. More precisely, genome annotation allow to identify a set of metabolic reactions which are active in a given species. Although, very often, these reactions are not su?cient to explain the production of compounds which are experimentally characterized. In this case, we need to propose reac- tions to be added to the metabolic map in order to explain the production of the targeted metabolic compounds. Such gap-?lling algorithms rarely take into account the existence of internal crossroads in the metabolic map. To that goal, we will model the role of intermediary metabolites in terms of combinatorial constraints. Then we will study the role of intermediary metabolites on a gap?lling method based on ASP (answer set programming) technologies. The method will be illustrated on large-scale yeast data

Combinatorial optimization methods to complete and analyse a metabolic network

Biological compartments have a important role with respect to the struc- turation of metabolic map and to the control of metabolic transformations within a cell. Such compartments highlight the role of speci?c compounds, namely those transported from a compartment to another, which can be view as metabolite crossroads. We will call them "intermediary metabo- lites". The goal of this talk will be to study the impact of such intermediary metabolite when one is reconstructing a metabolic map from genome scale data. More precisely, genome annotation allow to identify a set of metabolic reactions which are active in a given species. Although, very often, these reactions are not su?cient to explain the production of compounds which are experimentally characterized. In this case, we need to propose reac- tions to be added to the metabolic map in order to explain the production of the targeted metabolic compounds. Such gap-?lling algorithms rarely take into account the existence of internal crossroads in the metabolic map. To that goal, we will model the role of intermediary metabolites in terms of combinatorial constraints. Then we will study the role of intermediary metabolites on a gap?lling method based on ASP (answer set programming) technologies. The method will be illustrated on large-scale yeast data

Motives underlying food consumption in the Western Balkans: consumers’ profiles and public health strategies

International audienceThis study aims to identify subgroups of consumers based on the health motives underlying their food choice in Western Balkan Countries.The survey (n = 2943) was based on the Food Choice Questionnaire (FCQ) and elicited information on socio-demographic characteristics, consumption frequency of healthy food products, nutrition knowledge and impulsiveness. Analysis of the FCQ data focused on items of "health and natural content" and "weight control" factors to identify clusters.The biggest group of the sample was weight control and health-concerned individuals (34 %), mainly urban women older than 50. The second group of respondents (31 %) was moderately motivated about health and weight. A third group was health concerned but paid less attention to weight control (21 %), mainly comprising men and people living with children. The last group consisted of unconcerned young men (14 %) eating less fruit and showing higher impulsiveness.Western Balkan consumers differ in the importance they attach to health and natural content and weight control. This insight is needed to target interventions

Motives underlying food consumption in the Western Balkans : consumers’ profiles and public health strategies

Objectives: This study aims to identify subgroups of consumers based on the health motives underlying their food choice in Western Balkan Countries. Methods: The survey (n = 2943) was based on the Food Choice Questionnaire (FCQ) and elicited information on socio-demographic characteristics, consumption frequency of healthy food products, nutrition knowledge and impulsiveness. Analysis of the FCQ data focused on items of “health and natural content” and “weight control” factors to identify clusters. Results: The biggest group of the sample was weight control and health-concerned individuals (34 %), mainly urban women older than 50. The second group of respondents (31 %) was moderately motivated about health and weight. A third group was health concerned but paid less attention to weight control (21 %), mainly comprising men and people living with children. The last group consisted of unconcerned young men (14 %) eating less fruit and showing higher impulsiveness. Conclusions: Western Balkan consumers differ in the importance they attach to health and natural content and weight control. This insight is needed to target interventions

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

Background The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network. Results We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach. Conclusion The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype

Handling the heterogeneity of genomic and metabolic networks data within flexible workflows with the PADMet toolbox

National audienceA main challenge of the era of fast and massive genome sequencing is to transform sequences into biological knowledge. The reconstruction of metabolic networks that include all biochemical reactions of a cell is a way to understand physiology interactions from genomic data. In 2010, Thiele and Palsson described a general protocol enabling the reconstruction of high-quality metabolic networks. Since then several approaches have been implemented for this purpose. They all rely mainly on drafting a first metabolic network from genome annotations and orthology information followed by a gap-filling step. More precisely, in the case of exotic species the lack of good annotations and poor biological information result in incomplete networks. Reference databases of metabolic reactions guide the filling process in order to check whether adding reactions to a network allows compounds of interest to be produced from a given growth media. As a final objective, as soon as the network is considered to be complete enough, functional studies are undergone, often relying on the constraint-based paradigm derived from the Flux Balance Analysis (FBA) framework (Orth et al., 2010). The high diversity of input files and tools required to run any metabolic networks reconstruction protocol represents an important drawback. In addition, most approaches require reference metabolic networks of a template organism. Dictionaries mapping the reference metabolic databases to the gene identifiers corresponding to the studied organism may be required. As a main issue, it appears very difficult to ensure that input files agree among them. Such a heterogeneity produces loss of information during the use of the protocols and generates uncertainty in the final metabolic model. Here we introduce the PADMet-toolbox which allows conciliating genomic and metabolic network information. The toolbox centralizes all this information in a new graph-based format: PADMet (PortAble Database for Metabolism) and provides methods to import, update and export information. For the sake of illustration, the toolbox was used to create a workflow, named AuReMe, aiming to produce high-quality genome-scale metabolic networks and eventually input files to feed most platforms involved in metabolic network analyses. We applied this approach to two exotic organisms and our results evidenced the need of combining approaches and reconciling information to obtain a functional metabolic network to produce biomass