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

Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis

International audienc

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

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

International audienc