Modelling metabolic network dynamics in a cheese bacterial community

International audienc

Metabolic modelling deciphers interactions in a cheese bacterial community

National audienc

Modelling the dynamics of Salmonella infection in the gut at the bacterial and host levels

International audienc

Modelling the dynamics of Salmonella infection in the gut at the bacterial and the host level

International audienc

A multi-scale epidemic model of Salmonella infection with heterogeneous shedding∗

Salmonella strains colonize the digestive tract of farm livestock, such as chickens or pigs, without affecting them, and potentially infect food products, representing a threat for human health ranging from food poisoning to typhoid fever. It has been shown that the ability to excrete the pathogen in the environment and contaminate other animals is variable. This heterogeneity in pathogen carriage and shedding results from interactions between the host’s immune response, the pathogen and the commensal intestinal microbiota. In this paper we propose a novel generic multiscale modeling framework of heterogeneous pathogen transmission in an animal population. At the intra-host level, the model describes the interaction between the commensal microbiota, the pathogen and the inflammatory response. Random fluctuations in the ecological dynamics of the individual microbiota and transmission at between-host scale are added to obtain a drift-diffusion PDE model of the pathogen distribution at the population level. The model is further extended to represent transmission between several populations. The asymptotic behavior as well as the impact of control strategies including cleaning and antimicrobial administration are investigated through numerical simulation

Accelerating metabolic models evaluation with statistical metamodels: application to Salmonella infection models

Mathematical and numerical models are increasingly used in microbial ecology to model the fate of microbial communities in their ecosystem. These models allow to connect in a mechanistic framework species-level informations, such as the microbial genomes, with macro-scale features, such as species spatial distributions or metabolite gradients. Numerous models are built upon species-level metabolic models that predict the metabolic behaviour of a microbe by solving an optimization problem knowing its genome and its nutritional environment. However, screening the community dynamics with these metabolic models implies to solve such an optimization problem by species at each time step, leading to a significant computational load further increased by several orders of magnitude when spatial dimensions are added. In this paper, we propose a statistical framework based on Reproducing Kernel Hilbert Space (RKHS) metamodels that are used to provide fast approximations of the original metabolic model. The metamodel can replace the optimization step in the system dynamics, providing comparable outputs at a much lower computational cost. We will first build a system dynamics model of a simplified gut microbiota composed of a unique commensal bacterial strain in interaction with the host and challenged by a Salmonella infection. Then, the machine learning method will be introduced, and particularly the ANOVA-RKHS that will be exploited to achieve variable selection and model parsimony. A training dataset will be constructed with the original system dynamics model and hyper-parameters will be carefully chosen to provide fast and accurate approximations of the original model. Finally, the accuracy of the trained metamodels will be assessed, in particular by comparing the system dynamics outputs when the original model is replaced by its metamodel. The metamodel allows an overall relative error of 4.71% but reducing the computational load by a speed-up factor higher than 45, while correctly reproducing the complex behaviour occurring during Salmonella infection. These results provide a proof-of-concept of the potentiality of machine learning methods to give fast approximations of metabolic model outputs and pave the way towards PDE-based spatio-temporal models of microbial communities including microbial metabolism and host-microbiota-pathogen interactions

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved

Psychometric Properties and Correlates of Precarious Manhood Beliefs in 62 Nations

Precarious manhood beliefs portray manhood, relative to womanhood, as a social status that is hard to earn, easy to lose, and proven via public action. Here, we present cross-cultural data on a brief measure of precarious manhood beliefs (the Precarious Manhood Beliefs scale [PMB]) that covaries meaningfully with other cross-culturally validated gender ideologies and with country-level indices of gender equality and human development. Using data from university samples in 62 countries across 13 world regions (N = 33,417), we demonstrate: (1) the psychometric isomorphism of the PMB (i.e., its comparability in meaning and statistical properties across the individual and country levels); (2) the PMB’s distinctness from, and associations with, ambivalent sexism and ambivalence toward men; and (3) associations of the PMB with nation-level gender equality and human development. Findings are discussed in terms of their statistical and theoretical implications for understanding widely-held beliefs about the precariousness of the male gender role

Numerical modeling of the electrical activity of the atria and the pulmonary veins

Le travail présenté dans ce manuscrit s’articule en trois axes distincts. Dérivation de modèles mathématiques de phénomènes électrophysiologiques en cardiologie Nous utilisons des méthodes d'analyse asymptotique pour dériver un modèle simplifié à partir d'un modèle de tissu auriculaire tridimensionnel, tout en contrôlant l'erreur d'approximation. Ces méthodes ont permis de dériver un modèle bisurfacique qui permet de simuler des comportements tridimensionnels dans les oreillettes pour un coût numérique bidimensionnel afin d'étudier des phénomènes entrant en jeu lors d'arythmies auriculaires, tels que la dissociation électrique ou des hétérogénéités transmurales. La preuve de la convergence du modèle bisurfacique est apportée, et une stratégie d'optimisation du modèle en dehors du régime asymptotique est formalisée. Une méthode d’homogénéisation est également utilisée pour construire un modèle continu homogénéisé de l'activité des myocytes incluant le comportement non linéaire des gap junctions. Processus déclencheurs d'arythmie Des preuves de concepts de mécanismes arythmogènes sont apportées à l'aide de modèles numériques des veines pulmonaires. Le premier mécanisme repose sur un bloc de conduction unidirectionnel engendré par une discontinuité dans la structure fibreuse. Le second est basé sur une dynamique différente lors de la dépolarisation et de la repolarisation lorsque deux couches de fibres de directions différentes sont superposées. Perpétuation des arythmies auriculaires Un modèle bicouche des oreillettes est construit à partir d'une méthode semi-automatique de construction des fibres que nous avons développées. Nous étudions avec l'influence d'hétérogénéités transmurales de fibrose sur la perpétuation des arythmies. Plusieurs protocoles d'ablation sont ensuite testés. Enfin, une méthode de personnalisation du modèle auriculaire est formalisée.Three axes are explored.Derivation of mathematical models of electrophysiological phenomena applied to cardiology Asymptotic analysis methods allow to derive simplified models from three-dimensional complex atrial ones, while controlling approximation errors. We construct a bilayer surface model that allows to simulate three-dimensional phenomena for a bi-dimensional computational load, and to investigate 3D atrial patterns involved in atrial arrhythmia such as electrical dissociation or transmural heterogeneities. We prove the convergence of the bilayer model, and an optimization strategy to improve the model outside the asymptotic regime is formalised. Homogeneisation methods are also used to construct a homogenized continuous model of the electrical activity of the myocytes that includes the non linear behavior of gap junctions. Triggers of atrial arrhythmia Proofs of concept of arrhythmogenic mechanisms are given by using numerical models of the pulmonary veins. The first mechanism is based on a unidirectional conduction block triggered by a discontinuity of the fibre distribution. The second one comes from a different propagation pattern during the depolarization and the repolarization when two layer of fibres are superimposed. Atrial arrhythmia perpetuation A bilayer model of the atria is constructed from a semi automatic method that we developed. We investigate the influence of transmural heterogeneities of the distribution of fibrosis on the perpetuation of atrial arrhythmia. Several ablation protocols are assessed. Finally, a method of personalization of the model is given

Mathematical models in microbial ecology

In this presentation at the MathSV seminary (Université Paris-Saclay), a model of microbial ecology of the gut microbiota is introduced and linked to biological questions regarding gut microbial ecology and different links with the host