The amino acid and hydrocarbon contents of the Paris meteorite: Insights into the most primitive CM chondrite

International audienc

A Mathematical Approach Estimating Source and Sink Functioning of Competing Organs

in pressPlant growth and development depend on both organogenesis and photosynthesis. Organogenesis sets in place various organs (leaves, internodes, fruits, roots) that have their own sinks. The sum of these sinks corresponds to the plant demand. Photosynthesis of the leaves provides the biomass supply (source) that is to be shared among the organs according to their sink strength. Here we present a mathematical model – GreenLab – that describes dynamically plant architecture in a resource-dependent way. The source and sink functions of the various organs control the biomass acquisition and partitioning during plant development and growth, giving the sizes and weights of organs according to their position in the plant architecture. Non-linear least-square method was used to estimate the numerical values of (hidden) parameters that control the organ sink variation and leaf functioning. Through simultaneous fitting of data from several developmental stages (multi-fitting), plant growth could be described satisfactorily with just a few parameters. Examples of application on cotton and maize are shown in this article

A Formal Approach for Tuning Stochastic Oscillators

International audiencePeriodic recurrence is a prominent behavioural of many biological phenomena, including cell cycle and circadian rhythms. Although deterministic models are commonly used to represent the dynamics of periodic phenomena, it is known that they are little appropriate in the case of systems in which stochastic noise induced by small population numbers is actually responsible for periodicity. Within the stochastic modelling settings automata-based model checking approaches have proven an effective means for the analysis of oscillatory dynamics, the main idea being that of coupling a period detector automaton with a continuous-time Markov chain model of an alleged oscillator. In this paper we address a complementary aspect, i.e. that of assessing the dependency of oscillation related measure (period and amplitude) against the parameters of a stochastic oscillator. To this aim we introduce a framework which, by combining an Approximate Bayesian Computation scheme with a hybrid automata capable of quantifying how distant an instance of a stochastic oscillator is from matching a desired (average) period, leads us to identify regions of the parameter space in which oscillation with given period are highly likely. The method is demonstrated through a couple of case studies, including a model of the popular Repressilator circuit

Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis

Plant growth modeling has attracted a lot of attention due to its potential applications. Many scientific disciplines are involved, and a lot of research effort and intensive computer methods were needed to understand better the complex mechanisms underlying plant evolution. Among the numerous challenges, one can cite mathematical modeling, parameterization, estimation and prediction. One of the most promising models that have been proposed in the literature is the GreenLab functional–structural plant growth model. In this study, we focus only on one of its versions, named GreenLab-1, particularly adapted to a certain class of plants with known organogenesis, such as sugar beet, maize, rapeseed and other crop plants. The parameters of the model are related to plant functioning, and the vector of observations consists of organ masses measured only once at a given observation time. Previous efforts for parameter estimation in GreenLab-1 include Kalman-type filters, stochastic variants of EM and/or ECM algorithms, and hybrid sequential importance sampling algorithms with Bayesian estimation only for the functional parameters of the model. In this paper, the first purely Bayesian approach for parameter estimation of the GreenLab-1 model is proposed. This approach has much more flexibility in handling complex structures, thus providing a useful tool for analyzing such types of models. In order to sample from the posterior distribution an MCMC algorithm is used and its implementation issues are also discussed. The performance of this method is illustrated on a simulated and a real dataset from the sugar beet plant, and a comparison is made with the MLE approach. © 2021, International Biometric Society

Full Bayesian inference in hidden Markov models of plant growth

Accurately modeling the growth process of plants in interaction with their environment is important for predicting their biophysical characteris-tics, referred to as phenotype prediction. Most models are described by dis-crete dynamic systems in general state-space representation with important domain-specific characteristics: First, plant model parameters have usually clear functional meanings and may be of genetic origins, thus necessitating a precise estimation. Second, critical growth variables, specifically biomass production and dynamic allocation to organs, are hidden variables not acces-sible to measure. Finally, the difficulty to assess the local plant environment may imply the introduction of process noises in models. Therefore, a precise understanding of the system’s behavior requires the joint estimation of functional parameters, hidden states, and noise parameters. In this paper we describe how a full Bayesian method of estimation can accurately estimate all these key model variables using Markov chain Monte Carlo (MCMC) tech-niques. In the presence of both process and observation noises, it requires to use adequate particle MCMC (PMCMC) algorithms to efficiently sample the hidden states which, consequently, allows for a precise estimation of all noise parameters involved. Thanks to the Bayesian framework, appropriate choices of prior distributions for the noise parameters have enabled analytical pos-terior distributions and only simple updates are required. Furthermore, this estimation strategy can be easily generalized and adapted to different types of plant growth models, such as organ-scale or compartmental, provided that they are formulated as hidden Markov models. Our estimation method im-proves on those classically used in plant growth modeling in several aspects: First, by building upon a general probabilistic framework the estimation results allow proper statistical analyses. It is useful in prediction, no only for uncertainty and risk analysis (e.g., for crop yield prediction) but also to an-alyze the results of experimental trials, for example, to compare genotypes in breeding. Moreover, the care taken in the estimation of hidden variables opens new perspectives in the understanding of inner growth processes, no-tably the balance and interaction between biomass production and allocation (referred to as source-sink dynamics). Applications of this estimation proce-dure are demonstrated on the GreenLab model for Arabidopsis thaliana and the Log-Normal Allocation and Senescence (LNAS) model for sugar beet, on both synthetic and real data

Modélisation mathématique de l'hématopoïèse et des hémopathies : développement, dynamique et traitement

National audienceL'étude de l'hématopoïèse normale et pathologique, de par la complexité du sujet, requiert une approche multi-disciplinaire. L'utilisation de modèles mathématiques, lorsqu'il s'agit de comprendre la dynamique de populations de cellules, le développement de cancers ou l'effet d'un traitement, est particulièrement appropriée. Les modèles mathématiques, calibrés à partir d'observations expérimentales, peuvent être des outils d'aide à la décision en clinique, permettant par exemple de prédire l'effet d'un traitement ou d'en optimiser le dosage. Dans cette revue, nous commencerons par présenter différents modèles et formalismes mathématiques qui se sont développés au cours des décennies pour modéliser l'hématopoïèse, et qui sont encore pour certains à la base des travaux les plus récents. Nous aborderons ensuite les enjeux méthodologiques liés à l'inférence mathématique, permettant de s'assurer de la validité et robustesse des résultats. Enfin, nous terminerons par illustrer l'utilisation de modèles mathématiques dans trois champs d'application : l'initiation et le développement des hémopathies malignes, la dérégulation de leur hématopoïèse et leurs traitements

Contrasted reaction norms of wheat yield in pure vs mixed stands explained by tillering plasticities and shade avoidance

International audienceContext: Mixing cultivars is an agroecological practice of crop diversification, increasingly used for cereals. The yield of such cereal mixtures is higher on average than the mean yield of their components in pure stands, but with a large variance. The drivers of this variance are plant-plant interactions leading to different plant phenotypes in pure and mixed stands, i.e phenotypic plasticity. Objectives: The objectives were (i) to quantify the magnitude of phenotypic plasticity for yield in pure versus mixed stands, (ii) to identify the yield components that contribute the most to yield plasticity, and (iii) to link such plasticities to differences in functional traits, i.e. plant height and flowering earliness. Methods: A new experimental design based on a precision sowing allowed phenotyping each cultivar in mixture, at the level of individual plants, for above-ground traits throughout growth. Eight commercial cultivars of Triticum aestivum L. were grown in pure and mixed stands in field plots repeated for two years (2019-2020, 2020-2021) with contrasted climatic conditions and with nitrogen fertilization, fungicide and weed removal management strategies. Two quaternary mixtures were assembled with cultivars contrasted either for height or earliness. Results: Compared to the average of cultivars in pure stands, the height mixture strongly underyielded over both years (-29%) while the earliness mixture overyielded the second year (+11%) and underyielded the first year (-8%). The second year, the magnitude of cultivar's grain weight plasticity, measured as the difference between pure and mixed stands, was significantly and positively associated with their relative yield differences in pure stands (R-2=0.51). When grain weight plasticity, measured as the log ratio of pure over mixed stands, was partitioned as the sum of plasticities in each yield component, its strongest contributor was the plasticity in spike number per plant (similar to 56% of the sum), driven by even stronger but opposed underlying plasticities in both tiller emission and regression. For both years, the plasticity in tiller emission was significantly, positively associated with the height differentials between cultivars in mixture (R-2=0.43 in 2019-2020 and 0.17 in 2020-2021). Conclusions: Plasticity in the early recognition of potential resource competitors is a major component of cultivar strategies in mixtures, as shown here for tillering dynamics. Our results also highlighted a link between plasticity in tiller emission and height differential in mixture. Both height and tillering dynamics displayed plasticities typical of the shade avoidance syndrome. Implications: Both the new experimental design and decomposition of plasticities developed in this study open avenues to better study plant-plant interactions in agronomically-realistic conditions. This study also contributed a unique, plant-level data set allowing the calibration of process-based plant models to explore the space of all possible mixtures