Keywords
Computer Science and Digital Science
 A3.4. Machine learning and statistics
 A6.1.1. Continuous Modeling (PDE, ODE)
 A6.1.2. Stochastic Modeling
 A6.1.4. Multiscale modeling
 A6.2.1. Numerical analysis of PDE and ODE
 A6.2.3. Probabilistic methods
 A6.3.1. Inverse problems
 A6.3.4. Model reduction
Other Research Topics and Application Domains
 B1.1.2. Molecular and cellular biology
 B1.1.3. Developmental biology
 B1.1.7. Bioinformatics
 B1.1.8. Mathematical biology
 B1.1.10. Systems and synthetic biology
 B2.2. Physiology and diseases
 B2.3. Epidemiology
 B3.6. Ecology
1 Team members, visitors, external collaborators
Research Scientists
 Frédérique Clément [Team leader, Inria, Senior Researcher, HDR]
 Pascale Crépieux [CNRS, Senior Researcher, HDR]
 Frédéric JeanAlphonse [CNRS, Researcher]
 Béatrice Laroche [INRAE, Senior Researcher, HDR]
 Anne Poupon [CNRS, Senior Researcher, HDR]
 Eric Reiter [INRAE, Senior Researcher, HDR]
 Romain Yvinec [INRAE, Researcher, HDR]
PhD Students
 Guillaume Ballif [Inria]
 Marie Haghebaert [INRAE]
 Léo Meyer [Université d'Orléans]
Administrative Assistant
 Bahar Carabetta [Inria]
2 Overall objectives
MUSCA is intrinsically interdisciplinary and brings together applied mathematicians and experimental biologists. We address crucial questions arising from biological processes from a mathematical perspective. Our main research line is grounded on deterministic and stochastic population dynamics, in finite or infinite dimension. We study open methodological issues raised by the modeling, analysis and simulation of multiscale in time and/or space dynamics in the field of physiology, with a special focus on developmental and reproductive biology, and digestive ecophysiology.
3 Research program
3.1 General scientific positioning
The formalism at the heart of our research program is that of structured population dynamics, both in a deterministic and stochastic version. Such a formalism can be used to design multiscale representations (say at the meso and macro levels), possibly embedding twoway (bottomup and topdown) interactions from one level to another. We intend to couple structured population dynamics with dynamics operating on the microscopic level typically large biochemical networks (signaling, metabolism, gene expression), whose outputs can be fed into the higher level models (see section 3.4). To do so, model reduction approaches have to be designed and implemented to properly formulate the “entry points” of the micro dynamics into the meso/macro formalism (e.g. formulation of velocity terms in transport equations, choice of intensities for stochastic processes) and to enable one to traceback as much as possible the variables and parameters from one scale to another. This approach is common to EPC MUSCA's two main applications in reproductive/developmental biology on one side, and microbiota/holobiont biology on the other side, while being applied to different levels of living organisms. Schematically, the meso level corresponds to the cells of a multicellular organism in the former case, and to the individual actors of a microbial community for the latter case.
Our general multiscale framework will be deployed on the study of direct problems as well as inverse problems. In some situations these studies will be accompanied with a postprocessing layer of experimental data, which may be necessary to make the observations compatible with the model state variables, and will be based on dedicated statistical tools. Even if our approach may use classical modeling bricks, it is worth highlighting that the design of de novo models, specifically suited for addressing dedicated physiological questions, is a central part of our activity. Due to their intrinsic multiscale nature (in time and/or space), infinite dimensional formulation (PDE and/or measurevalued stochastic processes) and nonlinear interactions (across scales), such models raise most of the time open questions as far as their mathematical analysis, numerical simulation, and/or parameter calibration. We intend to cope with the resulting methodological issues, possibly in collaboration with external experts when needed to tackle open questions.
3.2 Design, analysis and reduction of networkbased dynamic models
We will deal with models representing dynamic networks, whether in a biochemical or ecological context. The mathematical formulation of these models involve Ordinary Differential Equations (ODE), Piecewise Deterministic Markov Processes (PDMP), or Continuous Time Markov Chains (CTMC). A prototypical example is the (massaction) Chemical Reaction Network (CRN) 58, defined by a set of $d$ species and a directed graph $\mathcal{R}$ on a finite set of stoichiometric vectors $\{y\in {\mathbb{N}}^{d}\}$ (the linear combination of reactant and product species). A subclass of CRN corresponds to a standard interaction network model in ecology, the generalized LotkaVolterra (gLV) model, that lately raised a lot of interest in the analysis of complex microbial communities 80, 53. The model describes the dynamics of interacting (microbial) species through an intrinsic $d$dimensional growth rate vector $\mu $ and a directed weighted interaction graph given by its $d\times d$ matrix $A$. The stochastic versions of these models correspond respectively to a Continuous Time Markov Chain (CTMC) in the discrete statespace ${\mathbb{N}}^{d}$, and a birthdeath jump process. This general class of models is relatively standard in biomathematics 58, 52, yet their theoretical analysis can be challenging due to the need to consider high dimensional models for realistic applications. The curse of dimensionality (state space dimension and number of unknown parameters) makes also very challenging the development of efficient statistical inference strategies.
Most of EPC MUSCA's models based on CRNs deal with (unstructured) population dynamics (complex microbial communities, neutral models in ecology, cell dynamics in developmental processes, macromolecule assemblies), biochemical kinetics and chemical reaction networks (signaling, gene, and metabolic networks), coagulationfragmentation models (in particular BeckerDöring model). Notwithstanding the diversity of our modeling applications, we have to face common methodological issues to study such models, ranging from the theoretical analysis of model behavior to parameter inference.
Network behavior
In the case of autonomous systems (with no explicit dependency on time), the main theoretical challenge is the prediction of the long time dynamics, given the algebraic complexity associated with putative stationary states in high dimension. In physiological systems, the intracellular reaction networks are not under a static or constant input stimulation but rather subject to complex and highly dynamic signals such as (neuro)hormones 19 or metabolites. These systems are thus nonautonomous in nature. Understanding to what extent reaction network motifs are able to encode or decode the dynamic properties of a timedependent signal is a particularly challenging theoretical question, which has yet been scarcely addressed, either in simplified casestudies 74,10 or in the framework of “pulsemodulated systems” 56.
Network reduction
The high dimension of realistic networks calls for methods enabling to perform model reduction. Our strategy for model reduction combines several tools, that can be applied separately or sequentially to the initial model. Both in stochastic biochemical systems and population dynamics, large species abundance calls in general for the functional law of large number and central limit theorems, for which powerful results are now established in standard settings of finite dimension models 63. However, in more and more biological applications, the very large spectrum of orders of magnitude in reaction rates (or birth and death rates) leads naturally to consider simultaneously large species abundance with timescale separation, which generally results in either algebraicdifferential reduced models, or to hybrid reduced models with both deterministic and stochastic dynamics. We will apply the generic methodology provided by the singular perturbation theory of FenichelTikhonov in deterministic systems, and Kurtz's averaging results in stochastic systems, which, in the context of high dimensional reaction networks or population dynamics, are still the matter of active research both in the deterministic 64, 57 and stochastic context 47, 62, 73.
Other reduction approaches of deterministic systems will consist in combining regular perturbation expansion with standard linear model order reduction (MOR) techniques. We will continue our previous work 13, 12 on the derivation of convergence and truncation error bounds for the regular perturbation series expansion (also known as Volterra series expansion) of trajectories of a wide class of weakly nonlinear systems, in the neighborhood of stable hyperbolic equilibria. The challenge will be to obtain biologically interpretable reduced models with appropriate features such as for instance positivity and stability. Finding a general approach for the reduction of strongly nonlinear systems is still an open question, yet it is sometimes possible to propose adhoc reduced models in specific cases, using graphbased decomposition of the model 77, combined with the reduction of weakly nonlinear subsystems.
Statistical Inference, Datafitting
Once again, a key challenge in parameter estimation is due to the high dimension of the state space and/or parameter space. We will develop several strategies to face this challenge. Efficient Maximum likelihood or pseudolikelihood methods will be developed and put in practice 1126, using either existing stateofthe art deterministic derivativebased optimization 78 or global stochastic optimization 54. In any case, we pay particular attention to model predictivity (quantification of the model ability to reproduce experimental data that were not used for the model calibration) and parameter identifiability (statistical assessment of the uncertainty on parameter values). A particularly challenging and stimulating research direction of interest concerning both model reduction and statistical inference is given by identifiability and inferencebased model reduction 66. Another strategy for parameter inference in complex, nonlinear models with fully observed state, but scarce and noisy observations, is to couple curve clustering, which allows reducing the system state dimension, with robust network structure and parameter estimation. We are currently investigating this option, by combining curve clustering 60 based on similarity criteria adapted to the problem under consideration, and an original inference method inspired by the Generalized Smoothing (GS) method proposed in 76, which we call Modified Generalized Smoothing (MGS). MGS is performed using a penalized criterion, where the loglikelihood of the measurement error (noisy data) is penalized by a model error for which no statistical model is given. Moreover, the system state is projected onto a functional basis (we mainly use spline basis), and the inference simultaneously estimates the model parameters and the spline coefficients.
3.3 Design, analysis and simulation of stochastic and deterministic models for structured populations
The mathematical formulation of structured population models involves Partial Differential Equation (PDE) and measurevalued stochastic processes (sometimes referred as IndividualBased Models–IBM). A typical deterministic instance is the McKendrickVon Foerster model, a paragon of (nonlinear) conservation laws. Such a formalism rules the changes in a population density structured in time and (possibly abstract) space variable(s). The transport velocity represents the time evolution of the structured variable for each “individual” in the population, and might depend on the whole population (or a part of it) in the case of nonlinear interactions (for instance by introducing nonlocal terms through moment integrals or convolutions). The source term models the demographic evolution of the population, controlled by birth or death events. One originality of our multiscale approach is that the formulation of velocities and/or source terms may arise, directly or indirectly, from an underlying finitedimension model as presented in section 3.2. According to the nature of the structuring variable, diffusion operators may arise and lead to consider secondorder parabolic PDEs. For finite population dynamics, the stochastic version of these models can be represented using the formalism of Poisson Measuredriven stochastic differential equations.
From the modeling viewpoint, the first challenge to be faced with this class of models yields in the model formulation itself. Obtaining a wellposed and mathematically tractable formulation, that yet faithfully accounts for the “behavioral law” underlying the multiscale dynamics, is not an obvious task.
On one side, stochastic models are suited for situations where relatively few individuals are involved, and they are often easier to formulate intuitively. On the other side, the theoretical analysis of deterministic models is generally more tractable, and provides one with more immediate insight into the population behavior. Hence, the ideal situation is when one can benefit from both the representation richness allowed by stochastic models and the power of analysis applicable to their deterministic counterparts. Such a situation is actually quite rare, due to the technical difficulties associated with obtaining the deterministic limit (except in some linear or weakly nonlinear cases), hence compromises have to be found. The mathematical framework exposed above is directly amenable to multiscale modeling. As such, it is central to the biomathematical bases of MUSCA and transverse to its biological pillars. We develop and/or analyze models for structured cell population dynamics involved in developmental or tissuehomeostasis processes, structured microbial populations involved in ecophysiological systems and molecule assemblies.
As in the case of finite dimension models, the study of these various models involve common methodological issues.
Model behavior
The theoretical challenges associated with the analysis of structured population models are numerous, due to the lack of a unified methodological framework. The analysis of the wellposedness 17 and longtime behavior 7, and the design of appropriate numerical schemes 1, 3 often rely on more or less generic techniques 72, 68 that we need to adapt in a casebycase, modeldependent way: general relative entropy 69, 51, measure solution framework 61, 48, 55, martingale techniques 49, finitevolume numerical schemes 65, just to name a few.
Due to their strong biological anchorage, the formulation of our models often leads to new mathematical objects, which raises open mathematical questions. Specific difficulties generally arise, for instance from the introduction of nonlocal terms at an “unusual place” (namely in the velocities rather than boundary conditions 17), or the formulation of particularly tricky boundary conditions 8. When needed, we call to external collaborators to try to overcome these difficulties.
Model reduction
Even if the use of a structured population formalism leads to models that can be considered as compact, compared to the highdimensional ODE systems introduced in section 3.2, it can be useful to derive reduced versions of the models, for sake of computational costs, and also and above all, for parameter calibration purposes.
To proceed to such a reduction, we intend to combine several techniques, including moment equations 71, dimensional reduction 6, timescale reduction 4, spatial homogenization 459, discrete to continuous reduction 8 and stochastic to deterministic limit theorems 14.
Once again, all these techniques need to be applied on a casebycase basis, and they should be handled carefully to obtain rigorous results (appropriate choice of metric topology, a priori estimates).
Statistical inference, Datafitting
The calibration of structured population models is challenging, due to both the infinitedimensional setting and the difficulty to obtain rich enough data in our application domains. Our strategy is rather empirical. We proceed to a sequence of preliminary studies before using the experimental available data. Sensitivity analyses 59, 50, and theoretical studies of the inverse problems associated with the models 5 intend to preclude unidentifiable situations and illposed optimization problems. The generation and use of synthetic data (possibly noised simulation outputs) allow us to test the efficiency of optimization algorithms and to delimit an initial guess for the parameters. When reduced or simplified versions of the models are available (or derived specifically for calibration purposes) 2, these steps are implemented on the increasingly complex versions of the model. In situations where PDEs are or can be interpreted as limits of stochastic processes, it is sometimes possible to estimate parameters on the stochastic process trajectories, or to switch from one formalism to the other.
3.4 Coupling biochemical networks with cell and population dynamics
A major challenge for multiscale systems biology is to rigorously couple intracellular biochemical networks with physiological models (tissue and organic functions) 75, 46, 79, 67. Meeting this challenge requires reconciling very different mathematical formalisms and integrating heterogeneous biological knowledge in order to represent in a common framework biological processes described on very contrasting spatial and temporal scales. On a generic ground, there are numerous methodological challenges associated with this issue (such as model or graph reduction, theoretical and computational connection between different modeling formalisms, integration of heterogenous data, or exploration of the whole parameter space), which are far from being overcome at the moment.
Our strategy is not to face frontally these bottlenecks, but rather to investigate in parallel the two facets of the question, through (i) the modeling of the topology and dynamics of infraindividual networks or dynamics, accounting for individual variability and local spatialization or compartmentalization at the individual level, as encountered for instance in cell signaling; and (ii) the stochastic and/or deterministic multiscale modeling of populations, establishing rigorous link between the individual and population levels. To bridge the gap, the key point is to understand how intracellular (resp. infraindividual) networks produce outputs which can then be fed up in a multicellular (resp. microbial population) framework, in the formulation of terms entering the multiscale master equations. A typical example of such outputs in individual cell modeling is the translation of different (hormonal or metabolic) signaling cues into biological outcomes (such as proliferation, differentiation, apoptosis, or migration). In turn, the dynamics emerging on the whole cell population level feedback onto the individual cell level by tuning the signal inputs qualitatively and quantitatively.
4 Application domains
The multiscale modeling approach described in section 3 is deployed on biological questions arising from developmental and reproductive biology, as well as digestive ecophysiology.
Our main developmental and reproductive thematics are related to gametogenesis, and gonad differentiation and physiology. In females, the gametogenic process of oogenesis (production and maturation of egg cells) is intrinsically coupled with the growth and development of somatic structures called ovarian follicles. Ovarian folliculogenesis is a longlasting developmental and reproductive process characterized by well documented anatomical and functional stages. The proper morphogenesis sequence, as well as the transit times from one stage to another, are finely tuned by signaling cues emanating from the ovaries (especially during early folliculogenesis) and from the hypothalamopituitary axis (especially during late folliculogenesis). The ovarian follicles themselves are involved in either the production or regulation of these signals, so that follicle development is controlled by direct or indirect interactions within the follicle population. We have been having a longstanding interest in the multiscale modeling of follicle development, which we have tackled from a “middleout”, cell dynamicsbased viewpoint 2, completed progressively with morphogenesis processes 16.
On the intracellular level, we are interested in understanding the endocrine dialogue within the hypothalamopituitarygonadal (HPG) axis controling the ovarian function. In multicellular organisms, communication between cells is critical to ensure the proper coordination needed for each physiological function. Cells of glandular organs are able to secrete hormones, which are messengers conveying information through circulatory systems to specific, possibly remote target cells endowed with the proper decoders (hormone receptors). We have settled a systems biology approach combining experimental and computational studies, to study signaling networks, and especially GPCR (GProtein Coupled Receptor) signaling networks 11. In the HPG axis, we focus on the pituitary hormones FSH (FollicleStimulating Hormone) and LH (Luteinizing Hormone) – also called gonadotropins, which support the double, gametogenic and endocrine functions of the gonads (testes and ovaries). FSH and LH signal onto gonadal cells through GPCRs, FSHR and LHR, anchored in the membrane of their target cells, and trigger intracellular biochemical cascades tuning the cell enzymatic activity, and ultimately controlling gene expression and mRNA translation. Any of these steps can be targeted by pharmacological agents, so that the mechanistic understanding of signaling networks is useful for new drug development.
Our main thematics in digestive ecophysiology are related to the interactions between the host and its microbiota. The gut microbiota, mainly located in the colon, is engaged in a complex dialogue with the large intestinal epithelium of its host, through which important regulatory processes for the host's health and wellbeing take place. Through successive projects, we have developed an integrative model of the gut microbiota at the organ scale, based on the explicit coupling of a population dynamics model of microbial populations involved in fiber degradation with a fluid dynamics model of the luminal content. This modeling framework accounts for the main drivers of the spatial structure of the microbiota, specially focusing on the dietary fiber flow, the epithelial motility, the microbial active swimming and viscosity gradients in the digestive track 15.
Beyond its scientific interest, the ambitious objective of understanding mechanistically the multiscale functioning of physiological systems could also help on the long term to take up societal challenges.
In digestive ecophysiology, microbial communities are fundamental for human and animal wellbeing and ecologic equilibrium. In the gut, robust interactions generate a barrier against pathogens and equilibrated microbiota are crucial for immune balance. Imbalances in the gut microbial populations are associated with chronic inflammation and diseases such as inflammatory bowel disease or obesity. Emergent properties of the interaction network are likely determinant drivers for health and microbiome equilibrium. To use the microbiota as a control lever, we require causal multiscale models to understand how microbial interactions translate into productive, healthy dynamics 18.
In reproductive physiology, there is currently a spectacular revival of experimental investigations (see e.g. 70, 81), which are driven by the major societal challenges associated with maintaining the reproductive capital of individuals, and especially female individuals, whether in a clinical (early ovarian failure of idiopathic or iatrogenic origin in connection with anticancer drugs in young adults and children), breeding (recovery of reproductive longevity and dissemination of genetic progress by the female route), or ecological (conservation of germinal or somatic tissues of endangered species or strains) context. Understanding the intricate (possibly long range and long term) interactions brought to play between the main cell types involved in the gonadal function (germ cells, somatic cells in the gonads, pituitary gland and hypothalamus) also requires a multiscale modeling approach.
5 New software and platforms
5.1 New software
5.1.1 pyDynPeak

Keywords:
Data processing, Endocrinology

Scientific Description:
Analysis of time series taking into account the inherent properties of secretion events (form and pulse halflife, regularity of changes in rhythm)

Functional Description:
Detection of LH pulses (luteinizing hormone) and analysis of their rhythm. Visualisation, diagnostic and interactive correction of the detections.
 URL:

Authors:
Frederique Clement, Hande Gozukan, Christian Poli

Contact:
Frederique Clement
6 New results
6.1 Stochastic modeling and stochastic processes
6.1.1 Averaging of a stochastic, multiple timescale model : Application to ovarian follicle populations
Participants: Guillaume Ballif, Frédérique Clément, Romain Yvinec.
We have analyzed a birth, migration and death stochastic process modeling the dynamics of a finite population, in which individuals transit unidirectionally across successive compartments 20. The model is formulated as a continuoustime Markov chain, whose transition matrix involves multiscale effects; the whole (or part of the) population affects the rates of individual birth, migration and death events. Using the slowfast property of the model, we have proved the existence and uniqueness of the limit model in the framework of stochastic singular perturbations. The derivation of the limit model is based on compactness and coupling arguments. The uniqueness is handled by applying the ergodicity theory and studying a dedicated Poisson equation. The limit model consists of an ordinarydifferential equation ruling the dynamics of the first (slow) compartment, coupled with a quasistationary distribution in the remaining (fast) compartments, which averages the contribution of the fast component of the Markov chain on the slow one. We have illustrated numerically the convergence, and highlighted the relevance of dealing with nonlinear event rates for our application in reproductive biology. The numerical simulations involve a simple integration scheme for the deterministic part, coupled with the nested algorithm to sample the quasistationary distribution.
6.1.2 Modeling the populations of ovarian follicles on a whole life scale
Participants: Guillaume Ballif, Frédérique Clément, Romain Yvinec.
We have designed a datadriven compartmental model of ovarian follicle development all along lifespan, in which the number of compartments is dictated by the developmental stages considered in the available experimental datasets on follicle numbers according to age. Following the framework introduced in 20, we account for the interactions between follicle stages by means of a nonlinear activation rate. We then settle a parameter estimation strategy using complementary information from KO mice in which the activation of quiescent follicles, corresponding to the first compartment, is unregulated. This model version is particularly suited for describing the follicle population after puberty in a compact way. In a next step, we have completed the original model to investigate more deeply follicle development in the prenatal period, at a time when the population of follicles is composed of two coexisting different subpopulations. We end up with an augmented state variable space with unchanged observable variables. We have performed a theoretical identifiability analysis and estimated the new parameter sets from a multiobjective criterion, including supplemental experimental information discriminating the two subpopulations.6.1.3 Quasistationary distribution and metastability for the stochastic BeckerDöring model
Participants: Erwan Hingant, Romain Yvinec.
We have studied a stochastic version of the classical BeckerDöring model, a wellknown kinetic model for cluster formation that predicts the existence of a longlived metastable state before a thermodynamically unfavorable nucleation occurs, leading to a phase transition phenomena 30. This continuoustime Markov chain model has received little attention, compared to its deterministic differential equations counterpart. We have shown that the stochastic formulation leads to a precise and quantitative description of stochastic nucleation events thanks to an exponentially ergodic quasistationary distribution for the process conditionally on nucleation has not yet occurred.
6.1.4 A PDMP model of the epithelial cell turnover in the intestinal crypt including microbiotaderived regulations
Participants: Léo Darrigade, Marie Haghebaert, Claire Cherbuy, Simon Labarthe, Béatrice Laroche.
Human health and physiology are strongly influenced by interactions between human cells and intestinal microbiota in the gut. In mammals, the hostmicrobiota crosstalk is mainly mediated by regulations at the intestinal crypt level: the epithelial cell turnover in the crypts is directly influenced by metabolites produced by the microbiota. Conversely, the colonocytes maintain hypoxia in the gut, favorable to anaerobic bacteria which dominate the gut microbiota. We have constructed an individualbased model of epithelial cells interacting with the microbiotaderived chemicals diffusing in the crypt lumen 43. This model is formalized as a piecewise deterministic Markov process (PDMP). It accounts for local interactions due to cell contact (among which are mechanical interactions), for cell proliferation, differentiation and extrusion which are regulated spatially or by chemicals concentrations. It also includes chemicals diffusing and reacting with cells. A deterministic approximated model is also introduced.
6.1.5 Discrete to continuous models of coarsening: second order approximation
Participants: Léo Meyer, Magali Ribot, Romain Yvinec.
Motivated by a model of adipocyte cell size dynamics, we study the scaling limit from a discrete size coarsening model to a continuous one, namely from the BeckerDöring model to the LifshitzSlyozov. Specifically, we intend to derive a second order approximation that leads to a non linear FokkerPlanck equation. Our strategy to obtain a convergence result is to use a probabilistic interpretation of both models, using a suitable nonlinear Markov process whose law is given by the coarsening dynamics.
6.2 Deterministic modeling and model reduction
6.2.1 Input/output reduced model of a damped nonlinear beam based on Volterra series and modal decomposition with convergence results
Participants: Thomas Hélie, Béatrice Laroche.
We have dealt with the model reduction and the simulation of a damped EulerBernoullivon Karman pinned beam excited by a distributed force 29. This nonlinear problem is formulated as a PDE and reformulated as a wellposed statespace system. The model order reduction and simulation have been derived by combining two approaches: a Volterra series expansion and truncation and a pseudomodal truncation defined from the eigenbasis of the linearized problem. The interest of this approach lies in the large class of input waveshapes that can be considered and in the simplicity of the simulation structure. This structure only involves cascades of finitedimensional decoupled linear systems and multilinear functions. Closedform bounds depending on the model coefficients and the truncation orders are provided for the Volterra convergence domain and the approximation error. These theoretical results have been generalized to a large class of nonlinear models, and refinement of bounds have also been proposed for a large subclass. Numerical experiments confirm that the beam model is well approximated by the very first Volterra terms inside the convergence domain.6.2.2 Modeling the growth of Staphylococcus aureus on cooked broccoli under isothermal conditions
Participants: Béatrice Laroche, and collaborators.
We have developed predictive models describing the growth of Staphylococcus aureus on cooked broccoli florets 31. A pool of 3.5 log CFU/g of five S. aureus strains were inoculated on 10 g broccoli portions. The samples were then stored at 10, 20, 30 and 37 degrees C, and colonies were enumerated at different time intervals. Baranyi and Roberts model was fitted to the data using a Bayesian Adaptive Markov Chain Monte Carlo for estimation of the growth parameters. S. aureus showed low growth at 10 degrees C on broccoli samples and at 2037 degrees C interval, Baranyi and Roberts model fitted well to the experimental data (R2>0.97). Estimated growth parameters were correlated with the possibility of toxin production and indicate the potential presence of these biological hazards on contaminated broccoli after heat treatment. Additionally, linear regression was performed for growth rate as storage temperature function. This secondary model followed a linear tendency with R2=0.997 and was compared with two tertiary models (ComBase Predictor and Pathogen Modeling Program) and literature data, demonstrating similar growth rate values of both. These results can be helpful for food services and managers to establish food safety standards for S. aureus growth on cooked broccoli.6.3 Exploration of signaling networks
6.3.1 Pharmacological characterization of low molecular weight biased agonists at the follicle stimulating hormone receptor
Participants: Pascale Crépieux, Frédéric JeanAlphonse, Anne Poupon, Eric Reiter, Romain Yvinec, and collaborators.
Folliclestimulating hormone receptor (FSHR) plays a key role in reproduction through the activation of multiple signaling pathways. Low molecular weight (LMW) ligands composed of biased agonist properties are highly valuable tools to decipher complex signaling mechanisms as they allow selective activation of discrete signaling cascades. However, available LMW FSHR ligands have not been fully characterized yet. In this context, we have explored the pharmacological diversity of three benzamide and two thiazolidinone derivatives compared to FSH 27. Concentration/activity curves were generated for G$\alpha $s, G$\alpha $q, G$\alpha $i, $\beta $arrestin 2 recruitment, and cAMP production, using BRET assays in living cells. ERK phosphorylation was analyzed by Western blotting, and CREdependent transcription was assessed using a luciferase reporter assay. All assays were done in either wildtype, G$\alpha $s or $\beta $arrestin 1/2 CRISPR knockout HEK293 cells. Bias factors were calculated for each pair of readouts by using the operational model. Our results have shown that each ligand presented a discrete pharmacological efficacy compared to FSH, ranging from superagonist for $\beta $arrestin 2 recruitment to pure G$\alpha $s bias. Interestingly, LMW ligands generated kinetic profiles distinct from FSH (i.e., faster, slower or transient, depending on the ligand) and correlated with CREdependent transcription. In addition, clear system biases were observed in cells depleted of either G$\alpha $s or $\beta $arrestin genes. Such LMW properties are useful pharmacological tools to better dissect the multiple signaling pathways activated by FSHR and assess their relative contributions at the cellular and physiopathological levels.6.3.2 Spatial bias in cAMP generation determines biological responses to PTH type 1 receptor activation
Participants: Frédéric JeanAlphonse, and collaborators.
The parathyroid hormone (PTH) type 1 receptor (PTHR) is a class B G protein–coupled receptor (GPCR) that regulates mineral ion, vitamin D, and bone homeostasis. Activation of the PTHR by PTH induces both transient cell surface and sustained endosomal cAMP production. To address whether the spatial (location) or temporal (duration) dimension of PTHRinduced cAMP encodes distinct biological outcomes, we have engineered a biased PTHR ligand (PTH7d) that elicits cAMP production at the plasma membrane but not at endosomes 37. PTH7d stabilized a unique active PTHR conformation that mediated sustained cAMP signaling at the plasma membrane due to impaired $\beta $arrestin coupling to the receptor. Experiments in cells and mice have revealed that sustained cAMP production by cell surface PTHR failed to mimic the pharmacological effects of sustained endosomal cAMP production on the abundance of the ratelimiting hydroxylase catalyzing the formation of active vitamin D, as well as increases in circulating active vitamin D and Ca2+ and in bone formation in mice. Thus, similar amounts of cAMP generated by PTHR for similar lengths of time in different cellular locations, plasma membrane and endosomes, mediate distinct physiological responses. These results unveil subcellular signaling location as a means to achieve specificity in PTHRmediated biological outcomes and raise the prospect of rational drug design based upon spatiotemporal manipulation of GPCR signaling.6.3.3 In vitro effects of the endocrine disruptor p,p' DDT on human choriogonadotropin/luteinizing hormone receptor signalling
Participants: Eric Reiter, and collaborators.
Dichlorodiphenyltrichloroethane (p,p'DDT) is an endocrinedisrupting chemical (EDC). Several studies have shown an association between p,p' DDT exposure and reprotoxic effects. We have shown that p,p' DDT is a positive allosteric modulator of human follitropin receptor (FSHR). In contrast, we have demonstrated that p,p' DDT decreases the cyclic AMP (cAMP) production induced by human choriogonadotropin (hCG). We have evaluated further the effects of p,p' DDT on Gs, $\beta $arrestin 2 and steroidogenesis pathways induced by hCG or luteinizing hormone (LH) 33. We used Chinese hamster ovary cells line stably expressing hCG/LHR. The effects of 10100 $\mu $M p,p' DDT on cAMP production and on $\beta $arrestin 2 recruitment were measured using bioluminescence and timeresolved resonance energy transfer technology. The impact of 100 $\mu $M of p,p' DDT on steroid secretion was analysed in murine Leydig tumor cell line (mLTC1). In cAMP assays, 100 $\mu $M p,p' DDT increased the EC50 by more than 300% and reduced the maximum response of the hCG/LHR to hCG and hLH by 30%. This inhibitory effect was also found in human granulosa cells line and in mLTC1 cells. Likewise, 100 $\mu $M p,p' DDT decreased the hCG and hLHpromoted $\beta $arrestin 2 recruitment down to 14.2 and 26.6%, respectively. Moreover, 100 $\mu $M p,p' DDT decreased by 30 and 47% the progesterone secretion induced by hCG or hLH, respectively, without affecting testosterone secretion. This negative effect of p,p' DDT was independent of cytotoxicity. p,p' DDT acted as a negative allosteric modulator of the hCG/LHR signalling. This emphasizes the importance of analyzing all receptordownstream pathways to fully understand the deleterious effects of EDC on human health.6.4 Computational modeling
6.4.1 Accurate determination of epitope for antibodies with unknown 3D structures
Participants: Pascale Crépieux, Anne Poupon, Eric Reiter, and collaborators.
MAbTope is a dockingbased method for the determination of epitopes. It has been used to successfully determine the epitopes of antibodies with known 3D structures. However, during the antibody discovery process, this structural information is rarely available. Although we already have evidence that homology models of antibodies could be used instead of their 3D structure, the choice of the template, the methodology for homology modeling and the resulting performance still have to be clarified. We have shown that MAbTope has the same performance when working with homology models of the antibodies as compared to crystallographic structures 35. Moreover, we have shown that even lowquality models can be used. We applied MAbTope to determine the epitope of dupilumab, an antiinterleukin 4 receptor alpha subunit therapeutic antibody of unknown 3D structure, that we validated experimentally. Finally, we have shown how the MAbTopedetermined epitopes for a series of antibodies targeting the same protein can be used to predict competitions, and demonstrated the accuracy with an experimentally validated example.6.4.2 The RanBP2/RanGAP1SUMO complex gates betaarrestin2 nuclear entry to regulate the Mdm2p53 signaling axis
Participants: Anne Poupon, and collaborators.
Mdm2 antagonizes the tumour suppressor p53. Targeting the Mdm2p53 interaction represents an attractive approach for the treatment of cancers with functional p53. Investigating mechanisms underlying Mdm2p53 regulation is therefore important. The scaffold protein $\beta $arrestin2 ($\beta $arr2) regulates tumour suppressor p53 by counteracting Mdm2. $\beta $arr2 nucleocytoplasmic shuttling displaces Mdm2 from the nucleus to the cytoplasm resulting in enhanced p53 signalling. $\beta $arr2 is constitutively exported from the nucleus, via a nuclear export signal, but mechanisms regulating its nuclear entry are not completely elucidated. $\beta $arr2 can be SUMOylated, but no information is available on how SUMO may regulate $\beta $arr2 nucleocytoplasmic shuttling. We have found $\beta $arr2 SUMOylation to be dispensable for nuclear import, and identified a noncovalent interaction between SUMO and $\beta $arr2, via a SUMO interaction motif (SIM), that is required for $\beta $arr2 cytonuclear trafficking 21. This SIM promotes association of $\beta $arr2 with the multimolecular RanBP2/RanGAP1SUMO nucleocytoplasmic transport hub that resides on the cytoplasmic filaments of the nuclear pore complex. Depletion of RanBP2/RanGAP1 SUMO levels result in defective $\beta $arr2 nuclear entry. Mutation of the SIM inhibits $\beta $arr2 nuclear import, its ability to delocalize Mdm2 from the nucleus to the cytoplasm and enhanced p53 signalling in lung and breast tumour cell lines. Thus, a $\beta $arr2SIM nuclear entry checkpoint, coupled with active $\beta $arr2 nuclear export, regulate its cytonuclear trafficking function to control the Mdm2p53 signalling axis.6.4.3 Agonist antiChemR23 mAb reduces tissue neutrophil accumulation and triggers chronic inflammation resolution
Participants: Anne Poupon, and collaborators.
We have discovered an antibody targeting the ChemR23 receptor and performed preclinical tests to assess its effects 36. This antibody is under development for the treatment of chronic inflammation. In the context of this study, we have predicted, and then experimentally validated the epitope of the antibody.6.5 Bibliographic reviews
6.5.1 Physiologically Based Modeling of Food Digestion and Intestinal Microbiota: State of the Art and Future Challenges. An INFOGEST Review
Participants: Béatrice Laroche, and collaborators.
We have reviewed modeling methodologies of the gastrointestinal tract during digestion that have adopted a systemsview approach and, more particularly, on physiologically based compartmental models of food digestion and hostdietmicrobiota interactions 32. This type of modeling appears very promising for integrating the complex stream of mechanisms that must be considered and retrieving a full picture of the digestion process from mouth to colon. We may expect these approaches to become more and more accurate in the future and to serve as a useful means of understanding the physicochemical processes occurring in the gastrointestinal tract, interpreting postprandial in vivo data, making relevant predictions, and designing healthier foods. This review intends to provide a scientific and historical background of this field of research, before discussing the future challenges and potential benefits of the establishment of such a model to study and predict food digestion and absorption in humans.6.5.2 Betaarrestins, their mechanism of action and multiple roles in the biology of G proteincoupled receptors
Participants: Eric Reiter.
The stimulation of G proteincoupled receptors (GPCRs) induces biological responses to a wide range of extracellular cues. The heterotrimeric G proteins, which are recruited to the active conformation of GPCRs, lead to the generation of various diffusible second messengers. Only two other families of proteins exhibit the remarkable characteristic of recognizing and binding to the active conformation of most GPCRs: GPCR kinases (GRKs) and $\beta $arrestins. These two families of proteins were initially identified as key players in the desensitization of G protein activation by GPCRs. Over the years, $\beta $arrestins have been implicated in an increasing number of interactions with nonreceptor proteins, expanding the range of cellular functions in which they are involved. It is now well established that $\beta $arrestins, by scaffolding and recruiting protein complexes in an agonistdependent manner, directly regulate the trafficking and signaling of GPCRs. In 34, we have reviewed the remarkable advances made in recent years, which have made it possible to i) identify biased ligands capable, by stabilizing particular conformations of a growing number of GPCRs, of activating or blocking the action of $\beta $arrestins independently of that of G proteins, some of these ligands holding great therapeutic interest; ii) to demonstrate $\beta $arrestins’ role in the compartmentalization of GPCR signaling within the cell and iii) to understand the molecular details of their interaction with GPCRs and of their activation through structural and biophysical approaches.6.5.3 Betaarrestins and endocrinerelated GPCRs
Participants: Pascale Crépieux, Frédéric JeanAlphonse, Anne Poupon, Eric Reiter, Romain Yvinec, and collaborators.
G proteincoupled receptors (GPCRs) allow target cells to respond to a wide array of hormones. Mounting evidences point to GPCRs being functionally coupled to multiple transduction mechanisms, some of them not involving heterotrimeric G proteins. Among these emerging mechanisms, it has been well established that $\beta $arrestins recruited to active GPCRs control not only their desensitization and internalization, but also assemble and activate signaling modules in different intracellular compartments. Importantly, $\beta $arrestindependent transduction applies to most GPCRs, including those involved in endocrine mechanisms. This concept, in conjunction with remarkable advances made over the last decade in structural biology and biophysics of GPCRs, supports the notion of ligandselective signaling also known as pharmacological bias. We have reviewed the role of $\beta $arrestin recruitment in the signaling and trafficking of endocrinerelated GPCRs 40. We have also focused on biased ligands capable of selectively activating intracellular pathways downstream of endocrinerelated GPCRs and discuss their potential therapeutic applications.6.5.4 Confocal and TIRF microscopy based approaches to visualize arrestin trafficking in living cells
Participants: Frédéric JeanAlphonse, Silvia Sposini.
Arrestins are key proteins that serve as versatile scaffolds to control and mediate G protein coupled receptors (GPCR) activity. Arrestin control of GPCR functions involves their recruitment from the cytosol to plasma membranelocalized GPCRs and to endosomal compartments, where they mediate internalization, sorting and signaling of GPCRs. Several methods can be used to monitor trafficking of arrestins; however, live fluorescence imaging remains the method of choice to both assess arrestin recruitment to ligandactivated receptors and to monitor its dynamic subcellular localization. We have presented two approaches based on Total Internal Fluorescence (TIRF) microscopy and confocal microscopy to visualize arrestin trafficking in live cells in real time and to assess their colocalization with the GPCR of interest and their localization at specific subcellular locations 41.6.5.5 Receptors  ThyroidStimulating Hormone/Luteinizing Hormone/FollicleStimulating Hormone Receptors
Participants: Pascale Crépieux, Yves Combarnous, Eric Reiter.
We have reviewed the signaling of the thyroidstimulating hormone receptor (TSHR), luteinizing hormone receptor (LHCGR, and folliclestimulating hormone receptor (FSHR), which are collectively referred to as the glycoprotein hormone receptors because they bind structurally similar glycoprotein hormones 39. The glycoprotein hormones consist of the pituitary thyroidstimulating hormone (thyrotropin, TSH), luteinizing hormone (lutropin, LH), and folliclestimulating hormone (follitropin, FSH) as well as the chorionic gonadotropin (choriogonadotropin, CG) placental hormone. The glycoprotein hormones are each composed of two dissimilar subunits ($\alpha $ and $\beta $) that are noncovalently associated. Within a given species, the $\alpha $subunit is identical and the $\beta $subunits are distinct but homologous. Due to the largely similar nature of LH$\beta $ and CG$\beta $, the LHCGR binds either LH or CG. The binding specificity of LH/CG to the LHCGR, FSH to the FSHR, and TSH to the TSHR is fairly strict. However, in some cases, extremely high levels of hormone can cause activation of the inappropriate receptor. Because the LHCGR and FSHR are localized primarily to the gonads, these two receptors are also referred to as the gonadotropin receptors. The glycoprotein hormone receptors belong to the family A (rhodopsinlike) of G proteincoupled receptors (GPCRs). However, they share the relatively unique feature of having a large extracellular domain that mediates high affinity binding of the hormone. In spite of their different physiological roles, the glycoprotein hormone receptors share a similar structural organization and mechanism of action.6.5.6 Reduced FSH and LH action: implications for medically assisted reproduction
Participants: Pascale Crépieux, and collaborators.
Impairment of the production or action of gonadotropins causes relative or absolute LH and FSH deficiency that compromises gametogenesis and gonadal steroid production, thereby reducing fertility. In women, LH and FSH deficiency is a spectrum of conditions with different functional or organic causes that are characterized by low or normal gonadotropin levels and low oestradiol levels. While the causes and effects of reduced LH and FSH production are well known, the notion of reduced action has received less attention by researchers. Recent evidence show that ageing and some polymorphisms negatively affect gonadotropin action. These findings have important clinical implications, in particular for medically assisted reproduction in which diminished action determined by the aforementioned factors, combined with reduced endogenous gonadotropin production caused by GnRH analogue protocols, may lead to resistance to gonadotropins and, thus, to an unexpected hyporesponse to ovarian stimulation. Indeed, the importance of LH and FSH action has been highlighted by the International Committee for Monitoring Assisted Reproduction Technologies (ICMART) in their definition of hypogonadotropic hypogonadism as gonadal failure associated with reduced gametogenesis and gonadal steroid production due to reduced gonadotropin production or action. In this review 22, we provide an overview of determinants of reduced FSH and LH action that are associated with a reduced response to ovarian stimulation.7 Partnerships and cooperations
7.1 International initiatives
7.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program
ANACONDA

Title:
Theoretical and numerical ANAlysis of CONservation laws for multicellular DynAmics

Duration:
2021 >

Coordinators:
Romain Yvinec and Mauricio Sepúlveda Cortés

Partners:
 Universidad del BíoBío, Concepción, Chile
 Inria Chile

Inria contact:
Romain Yvinec

Summary:
The formalism at the heart of our research program is that of structured population dynamics, which are wellsuited for describing multicellular dynamics in a compact way. The standard workflow in biological modeling of such objects starts with an individualbased stochastic formalism, specially relevant to represent cellular process such as growth, aging, division and apoptosis. The complexity of both the mathematical and numerical analysis of these stochastic models calls for the use of reduced models, among which partial differential equations (PDEs) of conservation law type are a good intermediate choice, and can be obtained from stochastic models by appropriate functional law of large numbers. The expected added value of the associate team will be to perform the theoretical analysis of PDE models, to design adapted numerical schemes and, whenever relevant, innovative inverse problems strategies, and to apply them in a synergistic way to various cell biology processes. Two main directions will be investigated thanks to the complementarity of the partners: scalar conservation laws and coarsening dynamics for cellular growth processes (WP1), and moving/freeboundary problems for multicellular morphodynamics (WP2).
7.1.2 Participation in other International Programs
 ECOS SUDCHILI 2020 : ECOS n° C20E03, “Coarsening dynamics: numerical and theoretical analysis of the LifshitzSlyozov equation with nucleation and applications to biology.” PIs: R. Yvinec and M. Sepúlveda (Universidad del BíoBío, Chile).
 iGPCRNet, International Research Network (IRN) on GPCRs, http://www.igpcrnet.com/
 Bill & Melinda Gates Foundation, ContraBody (20212023, PI Eric Reiter, 1.8 M US $) “NonHormonal Contraception by Nanobody Produced from Within the Body”. In partnership with University of Modena E Regio Emilia, Italy; MabSilico, France and InCellArt, France. Involved MUSCA members : E. Reiter, P. Crépieux, F. JeanAlphonse, R. Yvinec.
 Medical Research Council, MICA(20222025, PI (Investigating kisspeptin receptor signalling to improve the treatment of reproductive disease), Involved MUSCA members : E. Reiter
7.1.3 Visits to international teams
Research stays abroad$\phantom{\rule{0.277778em}{0ex}}$
Since midOctober, Guillaume Ballif has been visiting Lea Popovic in Concordia University (Montréal, Québec), for a four month stay, in the framework of the “Programme Visibilité Scientifique Junior" of Fondation Mathématique Jacques Hadamard.
7.2 European initiatives
7.2.1 FP7 & H2020 Projects
 ERC Advanced grant, Homo.Symbiosus (20192024, PI Joël Doré, 2.5 M€) “Assessing, preserving and restoring manmicrobes symbiosis”. Involved MUSCA member: B. Laroche.
 ERC Starting grant, Therautism (20202024, PI Lucie Pellissier, 1.5 M€) “New molecular targets and proofofconcept therapies for Autism Spectrum Disorders” Involved MUSCA member: P. Crépieux.
 ERNEST (European Research Network on Signal transduction) COST Action 18133.
7.3 National initiatives
 ANR ABLISS (20192022, PI A. Poupon, 441 K€) “Automating building from Literature of Signalling Systems”. Involved MUSCA members: A. Poupon, E. Reiter, P. Crépieux, R. Yvinec.
 ANR YDOBONAN (20212024, PI Vincent Aucagne, 497 K€) “Mirror Image Nanobodies: pushing forward the potential of enantiomeric proteins for therapeutic and pharmacological applications”. Involved MUSCA member: E. Reiter.
 ANR PHEROSENSOR (20212026, PI Philippe Lucas 1492K€) “Early detection of pest insects using pheromone receptorbased olfactory sensors”. Involved MUSCA member: B. Laroche.
 LabEx MAbImprove (20112025, PI Hervé Watier). Involved MUSCA members : E. Reiter, F. JeanAlphonse, P. Crépieux, A. Poupon, R. Yvinec.
 INRAE metaprogram HOLOFLUX, EggtoMeat project (20202022, PI Monique Zagorec). Involved MUSCA member: B. Laroche.
 INRAE metaprogram DIGITBIO; IMMO project (20212024, PI Violette Thermes), “IMagerie et MOdélisation multiéchelles pour la compréhension de la dynamique ovarienne chez le poisson”. Involved MUSCA members: F. Clément, R. Yvinec.
 ANSES GinFiz project (20212024, PI Rémy Beaudouin), “Gonadal aromatase inhibition and other toxicity pathways leading to Fecundity Inhibition in Zebrafish: from initiating events to population impacts”. Involved MUSCA members: F.Clément, R. Yvinec.
7.4 Regional initiatives
 SATT ParisSaclay POC'UP 2020 project COOPERATE, awarded to B. Laroche (together with L. Rigottier, P. Serror, V. Loux and O. Rué): “COnsortium de bactéries cOmmensales pour augmenter l’effet barrière du microbiote et limiter la Persistance et la prolifération des Entérocoques Résistants à la vancomycine après traitement AnTibiotiquE”.
 Ambition recherche développement Centre Val de Loire SELMAT (20202023, PI E. Reiter, 630 K€) “Méthodes in silico pour la sélection et la maturation d’anticorps : développement, validation et application à différentes cibles thérapeutiques”. Involved MUSCA members: E. Reiter, P. Crépieux, F. JeanAlphonse, R. Yvinec.
 Appel à projet région Centre Val de Loire, INTACT (20192022, PI P. Crépieux, 200 K€) “Pharmacologie réverse à l'aide d'anticorps intracellulaires antiRFSH actif”. Involved MUSCA members: P. Crépieux, E. Reiter, F. JeanAlphonse, A. Poupon, R. Yvinec. Industrial partner: McSAF, Tours.
 Appel à projet région Centre Val de Loire, NeuroMAbster (20182021, PI S. MorissetLopez, 200 K€) “Identification de nanobodies modulateurs du récepteur 5HT7 pour le traitement de maladies du SNC”. Involved MUSCA members: E. Reiter, A. Poupon.
8 Dissemination
8.1 Promoting scientific activities
8.1.1 Scientific events: organisation

F. Clément (together with Geneviève Dupont and Laurent Combettes),“Spatiotemporal encoding and decoding in cell signaling”, ITMO BCDE symposium, March 18, online (chair, organizing and scientific committee)

F. Clément (together with Joelle CohenTannoudji, Yves Combarnous, Florian Guillou, Sakina MhaoutyKodja, and François Vialard), ReproSciences 2021, April 1214, online (chair and scientific committee)
 A. Poupon, 9th Antibody Industrial Symposium, June 2225, online (chair)
8.1.2 Scientific events: selection

F. Clément,“Spatiotemporal encoding and decoding in cell signaling”, ITMO BCDE symposium, March 18, online
8.1.3 Journal
Member of the editorial boards

F. Clément, guest editor (together with Joseph DiStefano, Fady HannahShmouni, and William Joseph Jusko) of the Research Topic “Mechanistic, Machine Learning and Hybrid Models of the ‘Other’ Endocrine Regulatory Systems in Health and Disease” in Front. Endocrinol.

P. Crépieux, associate editor Front. Endocrinol. (cellular endocrinology)

F. JeanAlphonse and E. Reiter, guest editors (together with Francesco De Pascali, Aylin C. Hanyaloglu, and Francesco Poti) of the Research Topic “Pharmacology of endocrine related GPCR`” in Front. Endocrinol.

A. Poupon, editorial board member (molecular biology) Sci. Rep.

E. Reiter, guest editor (with Aylin C. Hanyaloglu) of the special issue on “G proteincoupled receptors: from molecules to medicine” in Curr. Opin. Endocrin. Metab. Res. (published on February 2021)

R. Yvinec, associate editor J. Math. Biol.
Reviewer  reviewing activities

F. Clément, J. Theor. Biol.

P. Crépieux, Mol. Cell. Endocrinol., Reproduction, Reprod. Sci.

F. JeanAlphonse, Proc. Natl. Acad. Sci. USA

A. Poupon, Front. Bioeng. Biotechnol. (review editor)

E. Reiter, J. Clin. Endocrinol. Metab., Front. Endocrinol., Sci. Rep., Mol. Cell. Endocrinol., Endocrinology, Proc. Natl. Acad. Sci. USA, eLife, Science, Nature Comm.

R. Yvinec, J. Math. Biol., J. Theor. Biol
8.1.4 Invited talks

B. Laroche. Workshop “Mathématiques et Microbiote”, Besançon, November 89.

E. Reiter. ITMO BCDE symposium on Spatiotemporal encoding and decoding in cell signaling, March 18.

E. Reiter. Société de Biologie, June 22

E. Reiter. Aging Pituitary Gonadal Axis Spring Retreat, June 28.

E. Reiter. Society for the Study of Reproduction meeting, July 28.

E. Reiter. Bill & Melinda Gates Foundation, Nonhomonal Contraceptive Discovery Program Meeting, May 20.

R. Yvinec. Mapping group online symposium, European Research Network on Signal Transduction, COST ACTION CA 18133, December 15.
8.1.5 Leadership within the scientific community

F. Clément, expert of the BCDE (Cell Biology, Development and Evolution) ITMO (MultiOrganization Thematic Institute) of the French National Alliance for Life and Health Sciences (Aviesan)

F. Clément, member of the direction and scientific boards of GDR 3606 REPRO (Integrative and translational approaches of human and animal reproduction), and cohead of WP “Biomathematics, Bioinformatics and Biophysics for Reproduction”

F. Clément, member of scientific board of PIXANIM (Phénotypage par Imagerie in/eX vivo de l'ANImal à la Molécule)

P. Crépieux, member of CNRS section 24 (and board member), “Physiologie, physiopathologie, biologie du cancer”

F. JeanAlphonse, coordinator of Key Question 1 (How can target activity be modulated through antibody binding?), LabEx MAbImprove

F. JeanAlphonse, guest researcher at Le Studium

F. JeanAlphonse, member of the Young investigator comittee of the International Research Network (IRN)

B. Laroche, member of the Steering Committee of the INRAE metagrogram HOLOFLUX

A. Poupon, coordinator of “Central Development Instrument 1 (Interdisciplinary Innovation)”, LabEx MAbImprove

R. Yvinec, cohead of WP “Biomathematics, Bioinformatics and Biophysics for Reproduction”, GDR 3606 REPRO
8.1.6 Scientific expertise

B. Laroche, member of the jury for the recruitment of a contractual lecturer at CentraleSupélec

B. Laroche, member of the jury for the recruitment of a junior scientist at INRAE

A. Poupon, member of the jury for the recruitment of a professor of mathematics at University of Tours

A. Poupon, member of the jury for the recruitment of a research engineer at INRAE

A. Poupon, member of the jury for the recruitment of a technician in IT at INRAE
8.1.7 Research administration

F. Clément is invited member of the scientific council of Graduate School Life Sciences and Health of University ParisSaclay

M. Haghebaert is a PhD student member of EDMH (École Doctorale Mathématiques Hadamard) council

B. Laroche is deputy head of MaIAGE lab.

E. Reiter is deputy director of UMR PRC

R. Yvinec is cohead of Fédération CaSciModOT (Calcul Scientifique et Modélisation OrléansTours)
8.2 Teaching  Supervision  Juries
8.2.1 Teaching

F. Clément has participated in the preparation of a data challenge on automatic ovarian follicle detection, in the framework of the data camp of Master program Data Science Institut Polytechnique de Paris (PI Alexandre Gramfort, collaboration François Caud, Céline Guigon, Raphaël Corre)

P. Crépieux, Master Biology of Reproduction (2h), Université de Tours

P. Crépieux, Master Infectiology, Immunity, Vaccinology and Biodrugs (4h), Université de Tours

P. Crépieux, Master Physiopathology (2h)

M. Haghebaert, Introduction to programming, firstyear Ecole Nationale des Ponts et Chaussées (30h)

L. Meyer, L1 Mathématiques, Université d'Orléans, algebra (18h)

L. Meyer L2 Informatiques, Université d'Orléans, probabilities (22h)

L. Meyer L3 Mathématiques, Université d'Orléans, numerical tools (24h)

E. Reiter, Master Infectiology, Immunity, Vaccinology and Biodrugs (4h), Université de Tours

E. Reiter, Master Physiopathology (2h), Université de Tours
8.2.2 Supervision

PhD in progress: Guillaume Ballif “Stochastic multiscale modeling in developmental and reproductive biology”, started October 2019, supervisors : F. Clément and R. Yvinec

PhD in progress: Camille Gauthier, “Manipulation of the activity and physiology of LH receptor through a small fragment of antibody”, started October 2020, supervisors: P. Crépieux and E. Reiter

PhD in progress: Juliette Gourdon “Manipulation of the intracellular traffic and endosomal signaling of gonadotropin receptors, LH/CGR and FSHR, by nanobodies: deciphering the molecular mechanisms and the consequences on reproduction”, started October 2021, supervisors: E. Reiter and F. JeanAlphonse)

PhD in progress: Marie Haghebaer, “Tools and methods for modelling the dynamics of complex microbial ecosystems from temporal experimental observations: application to the dynamics of the intestinal microbiota”, started November 2020, supervisor: B. Laroche

PhD in progress: Léo Meyer, “Modeling and analysis of models for adipocyte growth”, started October 2020, supervisors: M. Ribot and R. Yvinec

PhD in progress: Pauline Raynaud, “Intracellular antibodies to explore the relationships between conformations and activity of hormone receptors, and their application in reverse pharmacology”, started October 2019, supervisors: P. Crépieux and G. Bruneau

PhD in progress: Anielka Zehnaker, “Selective modulation of FSH receptor signaling pathways in vivo, consequences on ovarian and testicular functions ”, started October 2020, supervisor: E. Reiter
8.2.3 Juries
 P. Crépieux, HDR Jury of Mathilde Munier (referee), Université d'Angers, October 4
 P. Crépieux, PhD Jury of Myriam Guillien (referee), Université de Montpellier, December 14
 P. Crépieux, PhD Jury of Alexey Koshkin (referee and president), Université de Lyon, December 16
 B. Laroche, HDR Jury of Annabelle Ballesta (referee), Université Paris Saclay, June 18
 B. Laroche, PhD Jury of Lou Zonca (referee), Sorbonne Université, July 16
 B. Laroche (referee), PhD Jury of Arthur Carcano, Université de Paris, December 13
8.3 Popularization
8.3.1 Articles and contents
F. JeanAlphonse. Des anticorps de lama pour contrôler la reproduction. CNRS MICROSCOOP (publication scheduled in 2022 first term)
9 Scientific production
9.1 Major publications
 1 articleA numerical method for kinetic equations with discontinuous equations : application to mathematical modeling of cell dynamics.SIAM Journal on Scientific Computing3562013, 27 pages
 2 articleCellKinetics Based Calibration of a Multiscale Model of Structured Cell Populations in Ovarian Follicles.SIAM Journal on Applied Mathematics7642016, 14711491
 3 articleAdaptive mesh refinement strategy for a nonconservative transport problem.ESAIM: Mathematical Modelling and Numerical AnalysisAugust 2014, 1381  1412
 4 articleMultiscale population dynamics in reproductive biology: singular perturbation reduction in deterministic and stochastic models.ESAIM: Proceedings and Surveys672020, 7299
 5 articleAnalysis and numerical simulation of an inverse problem for a structured cell population dynamics model.Mathematical Biosciences and Engineering164Le DOI n'est pas actif, voir http://www.aimspress.com/article/10.3934/mbe.20191502019, 30183046
 6 articleMultiscale modelling of ovarian follicular selection..Progress in Biophysics and Molecular Biology1133December 2013, 398408
 7 articleAnalysis and calibration of a linear model for structured cell populations with unidirectional motion : Application to the morphogenesis of ovarian follicles.SIAM Journal on Applied Mathematics791February 2019, 207229
 8 articleQuasi steady state approximation of the small clusters in BeckerDöring equations leads to boundary conditions in the LifshitzSlyozov limit.Communications in Mathematical Sciences1552017, 13531384
 9 articleA mixture model for the dynamic of the gut mucus layer.ESAIM: Proceedings55décembre2016, 111130
 10 articleInterpreting frequency responses to doseconserved pulsatile input signals in simple cell signaling motifs..PLoS ONE942014, e95613

11
articleCompeting G proteincoupled receptor kinases balance G protein and
$$ arrestin signaling..Molecular Systems Biology8June 2012, 117  12 articleComputable convergence bounds of series expansions for infinite dimensional linearanalytic systems and application.Automatica5092014, 23342340
 13 articleComputation of Convergence Bounds for Volterra Series of LinearAnalytic SingleInput Systems.IEEE Transactions on Automatic Control569September 2011, 20622072
 14 articleThe BeckerDoring Process: Pathwise Convergence and Phase Transition Phenomena.Journal of Statistical Physics17752018, 506527
 15 articleA mathematical model to investigate the key drivers of the biogeography of the colon microbiota..Journal of Theoretical Biology46272019, 552581
 16 articleMultiscale modeling of ovarian follicular development: From follicular morphogenesis to selection for ovulation.Biology of the Cell1086June 2016, 112
 17 articleCauchy problem for multiscale conservation laws: Application to structured cell populations.Journal of Mathematical Analysis and Applications40122013, 896  920
 18 articleChallenges in microbial ecology: building predictive understanding of community function and dynamics..ISME Journal10Marco Cosentino Lagomarsino (LCQB) is a member of Isaac Newton Institute Fellows consortium.2016, 112
 19 articleAdvances in computational modeling approaches of pituitary gonadotropin signaling.Expert Opinion on Drug Discovery1392018, 799813
9.2 Publications of the year
International journals
 20 articleAveraging of a stochastic slowfast model for population dynamics: application to the development of ovarian follicles.SIAM Journal on Applied Mathematics2022
 21 articleThe RanBP2/RanGAP1SUMO complex gates βarrestin2 nuclear entry to regulate the Mdm2p53 signaling axis.Oncogene4012March 2021, 22432257
 22 articleReduced FSH and LH action: implications for medically assisted reproduction.Human Reproduction3662021, 14691480
 23 articleThe initialboundary value problem for the LifshitzSlyozov equation with nonsmooth rates at the boundary.Nonlinearity34042021, 19752017
 24 articleDirect impact of gonadotropins on glucose uptake and storage in preovulatory granulosa cells: Implications in the pathogenesis of polycystic ovary syndrome.Metabolism115February 2021, 115
 25 articleMathematical modeling of ovarian follicle development : A population dynamics viewpoint.Current Opinion in Endocrine and Metabolic Research182021, 5461
 26 articleStochastic nonlinear model for somatic cell population dynamics during ovarian follicle activation.Journal of Mathematical Biology8232021, 152
 27 articlePharmacological characterization of low molecular weight biased agonists at the follicle stimulating hormone receptor.International Journal of Molecular Sciences2218September 2021, 123
 28 articleEditorial: G protein–coupled receptors: From molecules to medicine.Current Opinion in Endocrine and Metabolic Research16February 2021, ivvi
 29 articleInput/output reduced model of a damped nonlinear beam based on Volterra series and modal decomposition with convergence results.Nonlinear Dynamics1051July 2021, 515540
 30 articleQuasistationary distribution and metastability for the stochastic BeckerDöring model.Electronic Communications in Probability262021
 31 articleModelling the growth of Staphylococcus aureus on cooked broccoli under isothermal conditions.Brazilian Journal of Microbiology523May 2021, 15651571
 32 articlePhysiologically Based Modeling of Food Digestion and Intestinal Microbiota: State of the Art and Future Challenges. An INFOGEST Review.Annual Review of Food Science and Technology12January 2021, 16/116/19
 33 articleIn vitro effects of the endocrine disruptor p,p' DDT on human choriogonadotropin/luteinizing hormone receptor signalling.Archives of Toxicology955February 2021, 16711681
 34 articleMécanismes d’action et rôles multiples des betaarrestines dans la biologie des récepteurs couplés aux protéines G.Biologie Aujourd'hui2021
 35 articleAccurate determination of epitope for antibodies with unknown 3D structures.mAbs1312021, 110
 36 articleAgonist antiChemR23 mAb reduces tissue neutrophil accumulation and triggers chronic inflammation resolution.Science Advances 714March 2021
 37 articleSpatial bias in cAMP generation determines biological responses to PTH type 1 receptor activation.Science Signaling14703October 2021
Scientific book chapters
 38 inbookThe folliclestimulating hormone signaling network in gonadal cells..Cellular endocrinology in health and disease, Second EditionAcademic PressFebruary 2021, 486 p.
 39 inbookReceptors  ThyroidStimulating Hormone/Luteinizing Hormone/FollicleStimulating Hormone Receptors.6Encyclopedia of Biological Chemistry IIISignalingElsevier2021, 323328
 40 inbookβarrestins and endocrinerelated GPCRs.Cellular Endocrinology in Health and DiseaseElsevier2021, 445458
 41 inbookConfocal and TIRF microscopy based approaches to visualize arrestin trafficking in living cells.166Biomolecular Interactions Part AMethods in Cell BiologyElsevier2021, 179203
Reports & preprints
 42 miscSingle seed microbiota: assembly and transmission from parent plant to seedling.May 2021
 43 miscA PDMP model of the epithelial cell turnover in the intestinal crypt including microbiotaderived regulations.October 2021
Other scientific publications
 44 inproceedings16S rRNA microbial community analysis and relationship with Salmonella Super shedder status in pigs.EJPOne Health Annual Scientific MeetingCopenhagen, DenmarkJune 2021
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