Towards an automatic conversion from SBML core to SBML qual

Présentation PosterNational audienceThe SBML format is the de facto standard to encode biological model in different formalisms. It was first developed to encode quantitative models like Differentials Equations (DEs), but the current release allows the definition of packages extending the core format. One of these packages, named qual, encodes qualitative models like Boolean Networks (BNs). To the best of our knowledge, there is no automatic pipeline to convert a quantitative model encoded in SBML core into a qualitative model encoded with the qual package. Here, we explore such a pipeline on a relatively simple system: the cell division of fission yeast, which has been studied both with a set DEs and with a BN. Our approach consists in extracting the model topology from the set of DEs and in solving them numerically in order to retrieve the time course data of species' concentrations on which we apply a discretization. Then we extract from these data a scarce state transition table. We are currently investigating ways to synthesize a BN fitting both topology knowledge and state transitions

An integrated way to design FD/FTC modules via parity space and model following

International audienceThis paper deals with the problem of integration of fault diagnosis and fault tolerant control modules. The main objective is to ensure a good behavior of the closed-loop system in the presence of faults and disturbances. To this aim, a global active methodology is defined in order to synthesize an additive optimal control input from fault detection and isolation results. More precisely, a robust residual is firstly generated by means of usual parity relations to detect and isolate faults on the system. Next, a fault accommodation procedure, based model following control scheme, is used to generate an additive control input according to the residual characteristics. The efficiency of this methodology is illustrated through an heating system benchmark

A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology

We propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. Simulations study shows the interest of the decorrelation inside the different clusters of covariates. We first apply our method to transcriptomic data of 37 patients with advanced non-small-cell lung cancer who have received chemotherapy, to select the transcriptomic covariates that explain the survival outcome of the treatment. Secondly, we apply our method to 79 breast tumor samples to define patient profiles for a new metastatic biomarker and associated gene network in order to personalize the treatments

A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology

International audienceWe propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. Simulations study shows the interest of the decorrelation inside the different clusters of covariates. We first apply our method to transcriptomic data of 37 patients with advanced non-small-cell lung cancer who have received chemotherapy, to select the transcriptomic covariates that explain the survival outcome of the treatment. Secondly, we apply our method to 79 breast tumor samples to define patient profiles for a new metastatic biomarker and associated gene network in order to personalize the treatments

Une exposition à de faibles doses d'alkylphénols entraine des altérations de épithélium mammaires et des défauts transgénérationnels mais n'augmente pas le potentiel tumorigénique des cellules cancéreuses mammaires

International audienceFetal and neonatal exposure to long chain alkylphenols has been suspected to promote breast developmental disorders and consequently to increase breast cancer risk. However, disease predisposition from developmental exposures remains unclear. In this work, human MCF-10A mammary epithelial cells were exposed in vitro to a low dose of a realistic [4-nonylphenol+4-tert-octylphenol] mixture. Transcriptome and cell phenotype analyses combined to functional and signaling network modeling indicated that long chain alkylphenols triggered enhanced proliferation, migration ability and apoptosis resistance and shed light on the underlying molecular mechanisms which involved the human estrogen receptor variant ERα36. A male mouse inherited transgenerational model of exposure to 3 environmentally relevant doses of the alkylphenol mix was set up in order to determine whether and how it would impact on mammary gland architecture. Mammary glands from F3 progeny obtained after intrabuccal chronic exposure of C57BL/6J P0 pregnant mice followed by F1 to F3 male inheritance displayed an altered histology which correlated with the phenotypes observed in vitro in human mammary epithelial cells. Since cellular phenotypes are similar in vivo and in vitro and involve the unique ERα36 human variant, such consequences of alkylphenol exposure could be extrapolated from mouse model to human. However, transient alkylphenol treatment combined to ERα36 overexpression in mammary epithelial cells were not sufficient to trigger tumorigenesis in xenografted Nude mice. Therefore, it remains to be determined if low dose alkylphenol transgenerational exposure and subsequent abnormal mammary gland development could account for an increased breast cancer susceptibility

Transgenerational effects of ERalpha36 over-expression on mammary gland development and molecular phenotype: clinical perspective for breast cancer risk and therapy.

International audienceGrowing source of evidence suggests that exposure to estrogen mimicking agents is a risk factor for breast cancer onset and progression. Long chain alkylphenols are man made compounds still present in household products, industrial and agricultural processes, leading to a global environmental and human contamination. These molecules are known to exert estrogen -like activities through binding to classical estrogen receptors. Recently, we have demonstrated that a realistic mixture of 4 tert - octylphenol and 4 - nonylphenol can stimulate proliferation and modulate epigenetic status of testicular cancer germ cells through a rapid, Estrogen Receptor alpha 36 (ERα36) -dependent non genomic pathway (Ajj et al, 2013; doi: 10.1371/journal.pone.0061758). In a retrospective study of breast tumor samples, we also validated ERα36 expression as a reliable prognostic factor for cancer progression from an estrogen dependent prolifera tive tumor toward an estrogen dispensable metastatic disease (Chamard - Jovenin et al, 2015; doi: 10.1186/s12918 - 015 - 0178 - 7). Since high ERα36 expression enhances expression of migration/invasion markers in breast tumors, we addressed the question of its involvement in response to alkylphenol exposure in vitro (MCF -10A mammary epithelial cell line and MCF -7 estrogen -sensitive cancer cells) and in vivo ( C57BL mice). A male inherited transgenerational model of exposure to environmentally relevant doses of an alkylphenol mix was set up in C57BL/6J mice to determine whether and how it impacts on mammary gland morphogenesis. Human mammary epithelial MCF -10A cells were exposed to similar doses to decipher the molecular mechanisms involved by a combination of transcriptomic study, cell phenotype analyses, functional and signaling network modeling. The relevance of mouse phenotype extrapolation to human risk is discussed. Mouse mammary gland exposed transgenerationally to the alkylphenol mix displayed a neoplastic -like histology. This phenotype was correlated with the enhanced proliferation, migration ability and apoptosis resistance observed in vitro on human mammary epithelial cells and mediated by the estrogen receptor variant ERα36. Since cellular phenotypes are similar in vivo and in vitro and involve the unique ERα36 human variant , such consequences of alkylphenol exposure could be extrapolated from mouse model to human. Low dose alkylphenol transgenerational exposure could promote abnormal mammary gland development and subsequently increase the risk of breast cancer at ageing

Analyse structurelle des propriétés d'observabilité et de diagnosticabilité des systèmes linéaires et bilinéaires <br />-- Approche graphique --

Les travaux présentés concernent le développement d'outils d'analyse par approche graphique de propriétés structurelles liées à l'observabilité et à la problématique du diagnostic pour les systèmes linéaires et bilinéaires.L'originalité des approches graphiques est de fournir des résultats de faible ordre de complexité et très aisément interprétables. Ainsi, les méthodes proposées sont applicables à des systèmes de grande taille et ce dès la phase de conception car ne nécessitant pas la connaissance exacte des paramètres physiques caractérisant le modèle du système considéré. En effet, seule la connaissance de la structure des systèmes est nécessaire pour la mise en œuvre des outils d'analyse proposés.Ainsi diverses propriétés liées à l'observabilité et à la détection et localisation de défauts ont été caractérisées graphiquement pour des systèmes linéaires standards ou singuliers ainsi que pour des systèmes bilinéaires. Des algorithmes de placement de capteurs pour le recouvrement de la propriété d'observabilité totale ou partielle ont aussi été proposés. Par ailleurs, la majorité de ces résultats et certains autres concernant les systèmes linéaires ont été implémentés et regroupés dans un logiciel ouvert développé sur plateforme libre.Quelques idées pour l'extension de ces résultats à des structures de systèmes plus complexes sont proposées comme perspectives à ces travaux