Éditeur(s) : Université de la Rochelle
Résumé : In order to better understand the functioning of the Brouage intertidal mudflat (Marennes- Oléron Basin, France), its carbon-based trophic web has been modelled and analyzed. The foodweb building is based on the 4 step method of Inverse Analysis: 1) conceive an a priori model as the graph of compartments (nodes) between which exist fluxes of material (vertices). These vertices are the unknowns for the problem; 2) gather all the existing knowledge about the ecosystem and translate it into linear equations and inequalities involving the fluxes; 3) complete this set of data by common knowledge on the behaviour of the compartments and translate it into inequalities, 4) solve the obtained linear system under the parsimony principle to find a unique solution vector. The food-web analysis is based on. The first purpose of this work was a methodological one: inverse analysis method has been adapted to consider the seasonal and spatial variability of the temperate ecosystem: two seasons have been coupled in the computation and the area has been divided into three geographical zones along a cross-shore gradient. Hence, two types of physical exchanges are involved: the benthicpelagic ones and the advection ones. Statistical results can also be obtained by coupling Monte Carlo methods to Inverse Analysis. To study the obtained networks, new indices based on Markov Chains take delay into account in describing integrative transfers of material between compartments. This lets emerge the systems' properties that are not obvious at first sight and emphasizes the role of peculiar components. The second purpose was to apply such methods to the Brouage mudflat ecosystem in order to better understand its functioning. As the knowledge and methods evolve, various food webs were computed and analyzed with the help such indices and existing ones. Sensitivity analyses allow to point at the most crucial lacks of knowledge, which can orientate future field research. New data would improve the quality of the model in two ways: one the one hand, they would validate or invalidate the outcomes and on the other hand, they would give useful information to better constrain some of the black boxes and most uncertain fluxes, especially the advection and vertical flows. The main results of the study show a strongly seasonal system in which benthic and pelagic components are linked with intensity. The system is driven by its high local benthic primary production, but also depends on imports from the basin. Each of the 3 geographic zones has a peculiar functioning. The central zone corresponds to the classical description of European intertidal mudflats: very productive, it can feed the other regions. It is a production zone. The upper part is less productive but is fed by the middle one via the water column and can be exploited by shorebirds. It is a degradation zone (material is transformed for less qualitative). The lower part strongly depends on the basin imports for its functioning, as the cultivated bivalves act as a pump for pelagic material of which a great part is deposited under the cultivation structures (oyster racks or mussels "bouchots"). It is a transformation zone.
Un double objectif (méthodologique et appliqué) a été visé par une modélisation du réseau trophique de la vasière de Brouage (Bassin de Marennes Oléron, France) à l'aide de l'analyse inverse, et son étude à l'aide de l'analyse des réseaux. L'Analyse Inverse permet en effet de reconstruire l'ensemble du réseau à partir de données éparses, le problème du manque de données étant récurrent en Ecologie. Au plan méthodologique, l'analyse inverse a été adaptée à la prise en compte des caractères saisonnier et spatialement hétérogène d'un écosystème tempéré de zone intertidale : deux saisons ont ainsi été couplées et la vasière a été considérée le long d'un transect perpendiculaire au rivage et divisé en trois zones spatiales. Deux types de flux physiques interviennent donc : les flux verticaux de dépôt et remise en suspension, et les flux d'advection via la colonne d'eau. Des pistes d'amélioration et de nouvelles méthodes sont proposées. Un résultat sous forme statistique peut être obtenu en couplant l'utilisation de méthodes de Monte Carlo à l'analyse inverse pour la construction des réseaux trophiques. Les moyens d'étude des modèles ont été enrichis par la mise au point d'indices issus de la construction de chaînes de Markov et prenant en compte le caractère temporel des transferts de matière entre compartiments. Enfin, la transformation mathématique du modèle statique pour une étude dynamique de la stabilité de l'équilibre décrit est abordée. Plusieurs modèles ont été construits au cours de l'évolution des méthodes et de l'acquisition de connaissances sur le site. Des méthodes d'analyses existantes et d'autres mises au point dans le cadre de cet ouvrage ont été appliquées aux réseaux obtenus afin de faire émerger les propriétés du fonctionnement de la vasière et de souligner le rôle de certains compartiments. Des analyses de sensibilité permettent d'évaluer la robustesse des résultats et d'orienter les recherches futures en mettant en évidence les manques les plus cruciaux. Des données supplémentaires, notamment concernant les foraminifères benthiques, le zooplancton ou les mouvements verticaux du microphytobenthos permettront de fournir de meilleures contraintes et de valider (ou non) les résultats obtenus. Les principaux résultats montrent un système au fort caractère saisonnier dont les compartiments benthiques et pélagiques sont très liés. La production primaire benthique locale est un moteur important, mais qui ne suffirait pas à suppléer l'indispensable importation de matériel détritique. Le découpage de la vasière en trois zones fait apparaître les particularités de chacune. La zone centrale correspond à la description classique des « vasières intertidales européennes » : très productive, elle peut alimenter les autres régions. C'est une zone de production et d'exportation. La partie supérieure est moins productive et reçoit de la matière de la zone de milieu d'estran via la colonne d'eau. Sa production secondaire peut alors être exploitée par les limicoles, nombreux en hiver. C'est une zone de dégradation et d'exportation. Le bas d'estran dépend fortement des importations extérieures, « pompées » par les bivalves cultivés qui sont ainsi directement responsables d'une forte sédimentation sous les structures mytilicoles et ostréicoles. C'est une zone de transformation.

Droits : info:eu-repo/semantics/openAccess
http://archimer.ifremer.fr/doc/2005/these-2260.pdf
http://archimer.ifremer.fr/doc/00000/2260/

Éditeur(s) : HAL CCSD
Résumé : International audience
In many situations it is important to be able to propose N independent real- izations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains (MCMC) interact in order to get an approximation of an indepen- dent N-sample of a given target law. In this method each individual chain proposes can- didates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model.
International Conference on Applied Statistics for Development in Africa Sada'07
Cotonou, Benin

inria-00506398
https://hal.inria.fr/inria-00506398
https://hal.inria.fr/inria-00506398/document
https://hal.inria.fr/inria-00506398/file/campillo2007b.pdf

Éditeur(s) : HAL CCSD
Résumé : 15 pages
A Stochastic Differential Equation appearing in the statistical theory of turbulence is extended in random environment by assuming that its two parameters are switched by an unobserved continuoustime Markov chain whose states represent the states of the environment. A Dirichlet process is placed as a prior on the space of the sample paths of this chain, leading to a hierarchical Dirichlet model whose estimation is done both on simulated data and on real data of wind speed measured at the entrance of a mangrove ecosystem.
https://hal.archives-ouvertes.fr/hal-00455050

hal-00455050
https://hal.archives-ouvertes.fr/hal-00455050
https://hal.archives-ouvertes.fr/hal-00455050/document
https://hal.archives-ouvertes.fr/hal-00455050/file/CSDA06_Srik_Hal.pdf

Éditeur(s) : Elsevier Science Bv
Résumé : Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from satellite-based vessel monitoring systems (VMS). The analysis seeks to enhance the definition of the fishing pressure exerted on fish stocks, by discriminating between the different behavioural states of a fishing trip, and also by quantifying the relative importance of each of these states during a fishing trip. The HBM approach is tested to analyse the behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov chain with a regular discrete time step is used to model transitions between successive behavioural states (e.g., fishing, steaming, stopping (at Port or at sea)) of each vessel. The parameters of the movement process (speed and turning angles) are defined conditionally upon the behavioural states. Bayesian methods are used to integrate the available data (typically VMS position recorded at discrete time) and to draw inferences on any unknown parameters of the model. The model is first tested on simulated data with different parameters structures. Results provide insights on the potential of HBM with HMP to analyze VMS data. They show that if VMS positions are recorded synchronously with the instants at which the process switch from one behavioural state to another, the estimation method provides unbiased and precise inferences on behavioural states and on associated movement parameters. However, if the observations are not gathered with a sufficiently high frequency, the performance of the estimation method could be drastically impacted when the discrete observations are not synchronous with the switching instants. The model is then applied to real pathways to estimate variables of interest such as the number of operations per trip, time and distance spent fishing or travelling. (C) 2010 Elsevier B.V. All rights reserved.
Ecological Modelling (0304-3800) (Elsevier Science Bv), 2010-07 , Vol. 221 , N. 15 , P. 1757-1769

Droits : 2010 Elsevier B.V. All rights reserved.
http://archimer.ifremer.fr/doc/00009/11993/9342.pdf
DOI:10.1016/j.ecolmodel.2010.04.005
http://archimer.ifremer.fr/doc/00009/11993/

Éditeur(s) : HAL CCSD
Résumé : 27 pages
Cascade Stochastic Differential Equation (SDE), a continuous time model for energy dissipation in turbulence, is a generalization of the Yaglom discrete cascade model. We extend this SDE to a model in random environment by assuming that its two parameters are switched by a continuous time Markov chain whose states represent the states of the environment. Moreover, a Dirichlet process is placed as a prior on the space of sample paths of this chain. We propose a Bayesian estimation method of this model which is tested both on simulated data and on real data of wind speed measured at the entrance of the mangrove ecosystem in Guadeloupe.
https://hal.archives-ouvertes.fr/hal-00286131

hal-00286131
https://hal.archives-ouvertes.fr/hal-00286131
https://hal.archives-ouvertes.fr/hal-00286131/document
https://hal.archives-ouvertes.fr/hal-00286131/file/Srik11_PhysicaDDspace.pdf

Éditeur(s) : HAL CCSD IOS Press (Amsterdam)
Résumé : 6 pages, 7 embedded figures, LaTeX 2e using the ecai2006.cls document class and the algorithm2e.sty style file (+ standard packages like epsfig, amsmath, amssymb, amsfonts...). Extends the short version contained in the ECAI 2006 proceedings.
International audience
Without prior knowledge, distinguishing different languages may be a hard task, especially when their borders are permeable. We develop an extension of spectral clustering -- a powerful unsupervised classification toolbox -- that is shown to resolve accurately the task of soft language distinction. At the heart of our approach, we replace the usual hard membership assignment of spectral clustering by a soft, probabilistic assignment, which also presents the advantage to bypass a well-known complexity bottleneck of the method. Furthermore, our approach relies on a novel, convenient construction of a Markov chain out of a corpus. Extensive experiments with a readily available system clearly display the potential of the method, which brings a visually appealing soft distinction of languages that may define altogether a whole corpus.
ECAI 2006: 17th European Conference on Artificial Intelligence
Riva del Garda, Italy

hal-00327782
https://hal.archives-ouvertes.fr/hal-00327782
ARXIV : 0810.1261

Éditeur(s) : HAL CCSD INRIA
Résumé : International audience
Computational probabilistic modeling and Bayesian inference has met a great success over the past fifteen years through the development of Monte Carlo methods and the ever increasing performance of computers. Through methods such as Monte Carlo Markov chain and sequential Monte Carlo Bayesian inference effectively combines with Markovian modelling. This approach has been very successful in ecology and agronomy. We analyze the development of this approach applied to a few examples of natural resources management.
La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand succès depuis une quinzaine d'années grâce au développement des méthodes de Monte Carlo et aux performances toujours croissantes des moyens de calcul. Au travers d'outils comme les méthodes de Monte Carlo par chaîne de Markov et les méthodes de Monte Carlo séquentielles, l'inférence bayésienne se combine efficacement à la modélisation markovienne. Cette approche est également très répendue dans le domaine de l'écologie et l'agronomie. Nous faisons le point sur les développements de cette approche appliquée à quelques exemples de gestion de ressources naturelles.
ARIMA

inria-00506383
https://hal.inria.fr/inria-00506383
https://hal.inria.fr/inria-00506383/document

Éditeur(s) : HAL CCSD
Résumé : International audience
Bayesian modelling is fluently employed to assess natural ressources. It is associated with Monte Carlo Markov Chains (MCMC) to get an approximation of the distribution law of interest. Hence in such situations it is important to be able to propose N independent realiza- tions of this distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains interact in order to get an approximation of an independent N -sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through example that it possesses many advantages. This approach will be applied to a forest dynamic model.
International Conference on Approximation Methods and Numerical Modelling in Environment and Natural Resources
Granada, Spain

inria-00506591
https://hal.inria.fr/inria-00506591
https://hal.inria.fr/inria-00506591/document
https://hal.inria.fr/inria-00506591/file/campillo2007c.pdf

Éditeur(s) : HAL CCSD INRIA
Résumé : International audience
Markov chain Monte Carlo (MCMC) methods together with hidden Markov models are extensively used in the Bayesian inference for many scientific fields like environment and ecology. Through simulated examples we show that the speed of convergence of these methods can be very low. In order to improve the convergence properties, we propose a method to make parallel chains interact. We apply this method to a biomass evolution model for fisheries.
Les méthodes de Monte Carlo par chaînes de Markov (MCMC) couplées à des modèles de Markov cachés sont utilisées dans de nombreux domaines, notamment en environnement et en écologie. Sur des exemples simples, nous montrons que la vitesse de convergence de ces méthodes peut être très faible. Nous proposons de mettre en interaction plusieurs algorithmes MCMC pour accélérer cette convergence. Nous appliquons ces méthodes à un modèle d'évolution de la biomasse d'une pêcherie.
ISSN: 1638-5713

inria-00506386
https://hal.inria.fr/inria-00506386
https://hal.inria.fr/inria-00506386/document
https://hal.inria.fr/inria-00506386/file/campillo2008d.pdf

Éditeur(s) : HAL CCSD Presses Universitaires de Louvain (distribution I6DOC/CIACO)
Résumé : In French. 10 pages, 5 figures, LaTeX 2e using EPSF and custom package taln2006.sty (designed by Pierre Zweigenbaum, ATALA). Proceedings of the 13th annual French-speaking conference on Natural Language Processing: `Traitement Automatique des Langues Naturelles' (TALN 2006), Louvain (Leuven), Belgium, 10-13 April 2003
National audience
We propose a theoretical framework within which information on the vocabulary of a given corpus can be inferred on the basis of statistical information gathered on that corpus. Inferences can be made on the categories of the words in the vocabulary, and on their syntactical properties within particular languages. Based on the same statistical data, it is possible to build matrices of syntagmatic similarity (bigram transition matrices) or paradigmatic similarity (probability for any pair of words to share common contexts). When clustered with respect to their syntagmatic similarity, words tend to group into sublanguage vocabularies, and when clustered with respect to their paradigmatic similarity, into syntactic or semantic classes. Experiments have explored the first of these two possibilities. Their results are interpreted in the frame of a Markov chain modelling of the corpus' generative processe(s): we show that the results of a spectral analysis of the transition matrix can be interpreted as probability distributions of words within clusters. This method yields a soft clustering of the vocabulary into sublanguages which contribute to the generation of heterogeneous corpora. As an application, we show how multilingual texts can be visually segmented into linguistically homogeneous segments. Our method is specifically useful in the case of related languages which happened to be mixed in corpora.
Verbum ex machina : Actes de la 13ème conférence annuelle sur le Traitement Automatique des Langues Naturelles
Louvain (Leuven), Belgium

hal-00327607
https://hal.archives-ouvertes.fr/hal-00327607
ARXIV : 0810.1212

Éditeur(s) : HAL CCSD
Résumé : International audience
In this work, we have led an analysis of the error of different global solar radiation prediction models according to the global solar radiation variability. Different predictions models where performed such as machine learning techniques (Neural Networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this study a simple linear autoregressive (AR) model as well as two naive models based on persistence of the GHI and persistence of the clear sky index (denoted herein scaled persistence model). The models are calibrated and tested with data from three 3 French islands: Corsica (42.15°N ; 9.08°E), Guadeloupe (16.25°N ; 61.58°W) and Reunion (21.15°S ; 55.5°E). Guadeloupe and Reunion are located in a subtropical climatic zone whereas Corsica is in a tempered climatic zone. Hence the global solar radiation variation differs significantly. The output error of the different models was quantified by the nRSME. In order to quantify the influence of the global solar radiation variability on the forecasting models error we performed a classification of typical days according to different typical days. Each class of typical day is defined by a variation of global solar radiation rate. For each class and for each location, the selected forecasting models where performed and the error was quantified. With this analysis a global solar radiation forecasting models can be selected according to the location, the global solar radiation fluctuations and hence the meteorological conditions. INTRODUCTION Large and frequent variations of solar radiation can be observed in tropical climates with amplitudes reaching 800 W/m² and occurring within a short time interval, from few seconds to few minutes, according to the geographical location. Such fluctuations can be due for example to the dynamic of clouds which can be very complex and depend on cloud type, size, speed and spatial distribution and, more generally, due to some specific local meteorological conditions. Thus, the solar energy forecasting, a process used to predict the amount of solar energy available in the current and near terms, might be a difficult task. Some of the best predictors found in literature are Autoregressive and moving average (ARMA) [5,7,8], Bayesian inferences [9,10], Markov chains [11], k-Nearest-Neighbors predictors [12] or artificial intelligence techniques as the Artificial Neural Network (ANN) [9-11]. Although these methodologies are potentially good in many areas, we observed in our previous studies on global radiation prediction [9,13,14] that the simple model based on the persistence of the clear sky index gives often very good results with acceptable errors [15] for short term forecasting time horizon (<= 1 hour). The goal of this paper is to determinate the influence of solar radiation variability regarding different classes of days on the expected error provided by different forecasting methods that the modeller can possibly implement. The paper is organized as follow: Section 2 describes the data we have used. Section 3 exposes the classification methodology and the results obtained for the three studied locations. In the two following sections, the forecasting methods are exposed and then 3 the errors on the forecasting results for each location and for each class are exposed.
The Third Southern African Solar Energy Conference (SASEC2015)
Kruger National Park, South Africa

Droits : info:eu-repo/semantics/OpenAccess
hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487
https://hal.archives-ouvertes.fr/hal-01099487/document
https://hal.archives-ouvertes.fr/hal-01099487/file/article%20SASEC.pdf

Résumé : La forte variabilité de la vitesse du vent fait que l'énergie produite par un parc éolien n'est pas constante dans le temps. Le gestionnaire ne peut donc pas dimensionner son réseau électrique en prenant intégralement ce type de production en compte. L' une des solutions préconisées pour permettre le développement de l' éolien et son intégration avec une plus grande sureté aux réseaux, est de développer et d'améliorer les outils de prévisions. Le travail de thèse consiste à améliorer les performances d'un outil de prédiction basé sur les réseaux de neurones bayesiens, permettant la prédiction de la puissance à très court terme . Le prédicteur fonctionne notamment par J'ajustement de paramètres, certain se détermine « automatiquement » via le mécanisme des réseaux de neurones bayesiens d' autres, que nous nommerons paramètres temporels, sont à l' appréciation de l'utilisateur. Le travail mené consiste à établir un protocole pour la fixation de ces paramètres tout en améliorant les performances du prédicteur . Nous avons donc décidé de conditionner leurs valeurs en fonction de la variabilité des séquences de puissance précédent l'instant de prévision. Tout d'abord nous avons classifié des séquences de puissance en fonction de leurs coefficients de variation en appliquant la méthode des C-moyennes floues. Puis, chaque classe formée a été testée sur plusieurs valeurs de paramètres, les valeurs associées aux meilleures prédictions ont été retenues. Enfin, ces résultats couplés au formalisme des Chaines de Markov, par le biais de la matrice de transition , ont perm is d'obtenir des taux d'amélioration par rapport à la persistance allant de 7,73 à 23,22 % selon l'horizon de prédiction considéré
The high variability of the wind speed has for conse quences that the energy produced by a wind farm is not constant over time. Therefore, the manager can't size the electrical network by takin g into account this type of production. One solution advocated for the development of wind energy and its integrati on with greater security at network, is to develop and improve fore casting tools. The thesi s objective is to improve the performance of a predi ction tool based on Bayesian neural networks, allowing the predi ction of wind power for short timescales. The predictor works, in part icular by the adjustment of parameters, sorne is determined "automatically" through the mechan ism of neural networks Bayesian other , which we cali temporal parameters are at the discretion of the user. The work involves establishing a protocol for the determination of these parameters and improving the performance of the predictor. So, we decided to condition their values depending on the sequence variability of wind power previous the moment of the forecast. First we classified sequences of power according to their coefficients of variation using the method of fuzzy C-means. Then, each formed class was tested for several parameters values, the values associated with the best predictions were selected. Finally , these result s coupled with the formalism of Markov chains , through the transition matrix allowed to obtain rates of improvement over the persistence ranging from 7.73 to 23.22 % depending on the prediction horizon considered

http://www.theses.fr/2013AGUY0679/document

Éditeur(s) : HAL CCSD
Résumé : International audience
The segmentation of digital images of plants is a tricky operation. In the example of a plant image on a unhomogeneous background, i.e. taken in its environment, the colorimetric diversity of the elements of a scene or the large number of forms can amplify the phenomena of over-segmentation. Global segmentation methods such as Mean Shift are then in this case the ones which will give the best results. These methods take into account the totality of the pixels of an image before classifying a point. On the other hand, complexity is increased, because it is necessary to go through the whole image treated, in order to find the mode of the point which one wishes to classify. In this article, we plan to couple the global segmentation with a local method which would take over in the event of obvious classification of a given point. The Mean Shift method is used for this purpose in association with Markov's chains.
Applied Imagery Pattern Recognition (AIPR)
Washington DC, United States

hal-00637143
https://hal.archives-ouvertes.fr/hal-00637143
DOI : 10.1109/AIPR.2011.6176338

Éditeur(s) : HAL CCSD European Geosciences Union
Résumé : International audience
Natural disturbances are the dominant form of forest regeneration and dynamics in unmanaged tropical forests. Monitoring the size distribution of treefall gaps is important to better understand and predict the carbon budget in response to land use and other global changes. In this study, we model the size frequency distribution of natural canopy gaps with a discrete power law distribution. We use a Bayesian framework to introduce and test, using Monte Carlo Markov chain and Kuo–Mallick algorithms, the effect of local physical environment on gap size distribution. We apply our methodological framework to an original light detecting and ranging dataset in which natural forest gaps were delineated over 30 000 ha of unmanaged forest. We highlight strong links between gap size distribution and environment, primarily hydrological conditions and topography, with large gaps being more frequent on floodplains and in wind-exposed areas. In the future, we plan to apply our methodological framework on a larger scale using satellite data. Additionally , although gap size distribution variation is clearly under environmental control, variation in gap size distribution in time should be tested against climate variability.
ISSN: 1726-4170

hal-01590729
https://hal-univ-guyane.archives-ouvertes.fr/hal-01590729
https://hal-univ-guyane.archives-ouvertes.fr/hal-01590729/document
https://hal-univ-guyane.archives-ouvertes.fr/hal-01590729/file/bg-14-353-2017.pdf
DOI : 10.5194/bg-14-353-2017

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. These metastable regimes depend on the network topology and on the orders of magnitude of the kinetic parameters. Benchmarking of the method on a computational biology model repository suggests that the number of metastable regimes is sub-exponential in the number of variables and equations. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed con-nectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional * corresponding author capacities of signalling pathways, as well as parameters responsible for speci-ficity of the pathway response. In particular, for a model of TGFβ signalling, we find that the ratio of TGFBR2 to TGFBR1 receptors concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptors of the two types.
ISSN: 0303-2647

hal-01379033
https://hal.inria.fr/hal-01379033
https://hal.inria.fr/hal-01379033/document
https://hal.inria.fr/hal-01379033/file/Samal_et_al_Biosystems_final.pdf
DOI : 10.1016/j.biosystems.2016.07.004

Éditeur(s) : HAL CCSD Springer Verlag (Germany)
Résumé : International audience
The Michaelis-Menten enzymatic reaction is sufficient to perceive many subtleties of network modeling, including the concentration and time scales separations, the formal equivalence between bulk phase and single-molecule approaches, or the relationships between single-cycle transient probabilities and steady state rates. Twelve methods proposed by different authors and yielding the same famous Michaelis-Menten equation, are selected here to illustrate the kinetic and probabilistic use of rate constants and to review basic techniques for handling them. Finally, the general rate of an ordered multistep reaction, of which the Michaelis-Menten reaction is a particular case, is deduced from a Markovian approach.
ISSN: 0259-9791

hal-00920806
https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806
https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806v2/document
https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806/file/12MM.pdf
ARXIV : 1312.5468

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
Recently, a large amount of work has been devoted to the study of spectral clustering — a powerful unsupervised classification method. This paper brings contributions to both its foundations, and its applications to text classification. Departing from the mainstream, concerned with hard membership, we study the extension of spectral clustering to soft membership (probabilistic, EM style) assignments. One of its key features is to avoid the complexity gap of hard membership. We apply this theory to a challenging problem, text clustering for languages having permeable borders, via a novel construction of Markov chains from corpora. Experiments with a readily available code clearly display the potential of the method, which brings a visually appealing soft distinction of languages that may define altogether a whole corpus.
ISSN: 0031-3203

hal-00329991
https://hal.archives-ouvertes.fr/hal-00329991
DOI : 10.1016/j.patcog.2008.06.024

Éditeur(s) : HAL CCSD Springer Verlag (Germany)
Résumé : International audience
The Michaelis-Menten enzymatic reaction is sufficient to perceive many subtleties of network modeling, including the concentration and time scales separations, the formal equivalence between bulk phase and single-molecule approaches, or the relationships between single-cycle transient probabilities and steady state rates. Seven methods proposed by different authors and yielding the same famous Michaelis-Menten equation, are selected here to illustrate the kinetic and probabilistic use of rate constants and to review basic techniques for handling them. Finally, the general rate of an ordered multistep reaction, of which the Michaelis-Menten reaction is a particular case, is deduced from a Markovian approach.
Journal of Mathematical Chemistry

hal-00920806
https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806
https://hal-univ-rennes1.archives-ouvertes.fr/hal-00920806/document
ARXIV : 1312.5468
DOI : 10.1007/s10910-013-0237-5