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

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

Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis

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Structured State Space Models for Multiple Instance Learning in Digital Pathology

Multiple instance learning is an ideal mode of analysis for histopathology data, where vast whole slide images are typically annotated with a single global label. In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified. Common models for performing this classification include recurrent neural networks and transformers. Although powerful compression algorithms, such as deep pre-trained neural networks, are used to reduce the dimensionality of each patch, the sequences arising from whole slide images remain excessively long, routinely containing tens of thousands of patches. Structured state space models are an emerging alternative for sequence modelling, specifically designed for the efficient modelling of long sequences. These models invoke an optimal projection of an input sequence into memory units that compress the entire sequence. In this paper, we propose the use of state space models as a multiple instance learner to a variety of problems in digital pathology. Across experiments in metastasis detection, cancer subtyping, mutation classification, and multitask learning, we demonstrate the competitiveness of this new class of models with existing state of the art approaches. Our code is available at https://github.com/MICS-Lab/s4 digital pathology

Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer

International audienceTumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor heterogeneity and, through a deeper molecular understanding of the disease, to personalize therapeutic strategies. In the last years, technological advances have widely improved the understanding of breast cancer biology and several trials have been developed to translate these new insights into clinical practice, with the ultimate aim of improving patients' outcomes. In the era of molecular oncology, genomics analyses and other methodologies are shaping a new treatment algorithm in breast cancer care. In this manuscript, we review the main steps of precision medicine to predict drug sensitivity in breast cancer from a translational point of view. Genomic developments and their clinical implications are discussed, along with technological advancements that could broaden precision medicine applications. Current achievements are put into perspective to provide an overview of the state-of-art of breast cancer precision oncology as well as to identify future research directions

Calibration of a probabilistic model of oilseed rape fertility to analyze the inter-variety variability in number of seeds

With the objective of using plant models as predictive tools scaling from genotype to phenotype, model parameters should have a strong genetic determinant. For this purpose, the modeling process involves assessing the differences in model parameters between varieties. In this study, a model of flower fertility is used to explain the observed behaviors and to identify the variety related parameters relevant to seed production. The model simulates the steps of seed production: ovule formation, landing of pollen grains on a flower, fertilization of ovule by pollen grains, possible abortion of the fertilized ovules. The aims of this study are to assess the differences of estimated parameters and identify the factors that can explain observed differences among varieties in the number of seeds per pod. Four varieties of oilseed rape (Mendel, Gamin, Exocet and Pollen) were grown at the experimental station of Grignon, France. Ten plants were marked and 15 pods from rank 11 to 40 (to eliminate the effect of position) on the main stem were collected on each plant for each variety. The numbers of seeds and aborted seeds were recorded. The total seed dry weight for each plant was measured. The maximum number of ovules per flower is different among the four varieties with the range of 36-45. The landing of pollen grains on a flower was significantly different among the varieties, although they were grown in the same field. The estimation result of model allows us to conclude that pollination and resource competition have the similar impact on the ovule and seed abortion. However, for the variety Gamin, the probability of fertilized ovules to abort was quite large in agreement with the smaller number of seeds measured but mean seed weight was higher than others (P<0.001, ANOVA). The data analysis indicated that the small number of seeds per pod was compensated by a higher seed weight. The abortion of seeds could result from insufficient pollination and resource competition. Current work aims at quantifying more precisely the roles of these factors and investigating other ones. We intend to use the model to distinguish the effects of different factors on seed production, such as the plant architecture. One way is to compare the behaviors of the main stem, the ramifications and the plants

Moving toward precision oncology centers V2.0

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