Mapping the Follicle-Stimulating Hormone-Induced Signaling Networks

Follicle-stimulating hormone (FSH) is a central regulator of male and female reproductive function. Over the last decade, there has been a growing perception of the complexity associated with FSH-induced cellular signaling. It is now clear that the canonical Gs/cAMP/PKA pathway is not the sole mechanism that must be considered in FSH biological actions. In parallel, consistent with the emerging concept of biased agonism, several examples of ligand-mediated selective signaling pathway activation by gonadotropin receptors have been reported. In this context, it is important to gain an integrative view of the signaling pathways induced by FSH and how they interconnect to form a network. In this review, we propose a first attempt at building topological maps of various pathways known to be involved in the FSH-induced signaling network. We discuss the multiple facets of FSH-induced signaling and how they converge to the hormone integrated biological response. Despite of their incompleteness, these maps of the FSH-induced signaling network represent a first step toward gaining a system-level comprehension of this hormone’s actions, which may ultimately facilitate the discovery of novel regulatory processes and therapeutic strategies for infertility and non-steroidal contraception

Towards a logic-based method to infer provenance-aware molecular networks

International audienceProviding techniques to automatically infer molecular networks is particularly important to understand complex relationships between biological objects. We present a logic-based method to infer such networks and show how it allows inferring signalling networks from the design of a knowledge base. Provenance of inferred data has been carefully collected, allowing quality evaluation. More precisely, our method (i) takes into account various kinds of biological experiments and their origin; (ii) mimics the scientist's reasoning within a first-order logic setting; (iii) specifies precisely the kind of interaction between the molecules; (iv) provides the user with the provenance of each interaction; (v) automatically builds and draws the inferred network

Towards a logic-based method to infer provenance-aware molecular networks

International audienceProviding techniques to automatically infer molecular networks is particularly important to understand complex relationships between biological objects. We present a logic-based method to infer such networks and show how it allows inferring signalling networks from the design of a knowledge base. Provenance of inferred data has been carefully collected, allowing quality evaluation. More precisely, our method (i) takes into account various kinds of biological experiments and their origin; (ii) mimics the scientist's reasoning within a first-order logic setting; (iii) specifies precisely the kind of interaction between the molecules; (iv) provides the user with the provenance of each interaction; (v) automatically builds and draws the inferred network

Automatical inference of signalling pathway's models from experimental

Les réseaux biologiques, notamment les réseaux de signalisation déclenchés par les hormones, sont extrêmement complexes. Les méthodes expérimentales à haut débit permettent d’aborder cette complexité, mais la prise en compte de l’ensemble des données générées requiert la mise au point de méthodes automatiques pour la construction des réseaux. Nous avons développé une nouvelle méthode d’inférence reposant sur la formalisation, sous forme de règles logiques, du raisonnement de l’expert sur les données expérimentales. Cela nécessite la constitution d’une base de connaissances, ensuite exploitée par un moteur d’inférence afin de déduire les conclusions permettant de construire les réseaux. Notre méthode a été élaborée grâce au réseau de signalisation induit par l’hormone folliculo-stimulante dont le récepteur fait partie de la grande famille des récepteurs couplés aux protéines G. Ce réseau a également été construit manuellement pour évaluer notre méthode. Un contrôle a ensuite été réalisé sur réseau induit par le facteur de croissance épidermique, se liant à un récepteur tyrosine kinase, de façon à montrer que notre méthode est capable de déduire différents types de réseaux de signalisation.Biological networks, including signalling networks induced by hormones, are very complex. High-throughput experimental methods permit to approach this complexity, but to be able to use all generated data, it is necessary to create automatical inference methods to build networks. We have developped a new inference method based on the formalization of the expert’s reasoning on experimental data. This reasoning is converted into logical rules. This work requires the creation of a knowledge base which is used by an inference engine to deduce conclusions to build networks. Our method has been elaborated by the construction of the signalling network induced by the follicle stimulating hormone whose receptor belongs to the G protein-coupled receptors family. This network has also been built manually to assess our method. Then, a test has been done on the network induced by the epidermal growth factor, which binds to a tyrosine kinase receptor, to demonstrate the ability of our method to deduce differents types of signaling networks

Automatical inference of signalling pathway's models from experimental

Les réseaux biologiques, notamment les réseaux de signalisation déclenchés par les hormones, sont extrêmement complexes. Les méthodes expérimentales à haut débit permettent d’aborder cette complexité, mais la prise en compte de l’ensemble des données générées requiert la mise au point de méthodes automatiques pour la construction des réseaux. Nous avons développé une nouvelle méthode d’inférence reposant sur la formalisation, sous forme de règles logiques, du raisonnement de l’expert sur les données expérimentales. Cela nécessite la constitution d’une base de connaissances, ensuite exploitée par un moteur d’inférence afin de déduire les conclusions permettant de construire les réseaux. Notre méthode a été élaborée grâce au réseau de signalisation induit par l’hormone folliculo-stimulante dont le récepteur fait partie de la grande famille des récepteurs couplés aux protéines G. Ce réseau a également été construit manuellement pour évaluer notre méthode. Un contrôle a ensuite été réalisé sur réseau induit par le facteur de croissance épidermique, se liant à un récepteur tyrosine kinase, de façon à montrer que notre méthode est capable de déduire différents types de réseaux de signalisation.Biological networks, including signalling networks induced by hormones, are very complex. High-throughput experimental methods permit to approach this complexity, but to be able to use all generated data, it is necessary to create automatical inference methods to build networks. We have developped a new inference method based on the formalization of the expert’s reasoning on experimental data. This reasoning is converted into logical rules. This work requires the creation of a knowledge base which is used by an inference engine to deduce conclusions to build networks. Our method has been elaborated by the construction of the signalling network induced by the follicle stimulating hormone whose receptor belongs to the G protein-coupled receptors family. This network has also been built manually to assess our method. Then, a test has been done on the network induced by the epidermal growth factor, which binds to a tyrosine kinase receptor, to demonstrate the ability of our method to deduce differents types of signaling networks

ChloroKB, a cell metabolism reconstruction of the model plant Arabidopsis thaliana

International audienceCan we understand how plant cell metabolism really works? An integrated large-scale modelling of plant metabolism predictive model would make possible to analyse the impact of disturbances in environmental conditions on cellular functioning and diversity of plant-made molecules of interest. ChloroKB, a Web application initially developed for exploration of Arabidopsis chloroplast metabolic network now covers Arabidopsis mesophyll cell metabolism. Interconnected metabolic maps show subcellular compartments, metabolites, proteins, complexes, reactions, and transport. Data in ChloroKB have been structured to allow for mathematical modelling and will be used as a reference for modelling work dedicated to a particular issue.Peut-on comprendre comment fonctionne réellement le métabolisme des cellules végétales ? Un modèle prédictif intégré à grande échelle du métabolisme des plantes permettrait d’analyser l’impact des perturbations des conditions environnementales sur le fonctionnement cellulaire et la diversité des molécules d’intérêt fabriquées par les plantes. ChloroKB, une application Web initialement développée pour l’exploration du réseau métabolique du chloroplaste d’Arabidopsis, couvre désormais le métabolisme des cellules du mésophylle d’Arabidopsis. Des cartes métaboliques interconnectées décrivent les compartiments subcellulaires, les métabolites, les protéines, les complexes, les réactions et le transport. Les données de ChloroKB ont été structurées pour permettre la modélisation mathématique et seront utilisées comme référence pour les travaux de modélisation consacrés à une question particulière

A logic-based method to build signaling networks and propose experimental plans

With the dramatic increase of the diversity and the sheer quantity of biological data generated, the construction of comprehensive signaling networks that include precise mechanisms cannot be carried out manually anymore. In this context, we propose a logic-based method that allows building large signaling networks automatically. Our method is based on a set of expert rules that make explicit the reasoning made by biologists when interpreting experimental results coming from a wide variety of experiment types. These rules allow formulating all the conclusions that can be inferred from a set of experimental results, and thus building all the possible networks that explain these results. Moreover, given an hypothesis, our system proposes experimental plans to carry out in order to validate or invalidate it. To evaluate the performance of our method, we applied our framework to the reconstruction of the FSHR-induced and the EGFR-induced signaling networks. The FSHR is known to induce the transactivation of the EGFR, but very little is known on the resulting FSH- and EGF-dependent network. We built a single network using data underlying both networks. This leads to a new hypothesis on the activation of MEK by p38MAPK, which we validate experimentally. These preliminary results represent a first step in the demonstration of a cross-talk between these two major MAP kinases pathways