arXiv subjects

Romain Yvinec

Publications and source records attributed to Romain Yvinec.

At least 19 recordsLinked to original sources

arXiv2025

The insertion of Neomycin cassette impairs maternal and social behaviors in Arc/Arg3.1 knock-out mice

The Neomycin resistance cassette (Neo+) is commonly inserted in the genome of mice to generate knock-out (KO) models. The effect of gene deletion on social behaviors in mice is controversial between studies using different Neo+ and Neo-mouse lines, particularly Arc/Arg3.1 KO lines. In this study, we identified severe maternal behavior impairments in Neo+, but not Neo-Arc/Arg3.1 KO dams. These deficits resulted from reduced sociability and abnormal social information processing in Neo+ Arc/Arg3.1 KO dams, exacerbated by social communication impairments in pups. The expression of the Neo cassette product did not cause cytotoxicity, but led to altered ERK signaling, gene expression, and oxytocin system. However, oxytocin administration did not improve social impairments in Neo+ Arc/Arg3.1 KO animals. Interestingly, early social environment enrichment enhanced social interaction with familiar, but not unfamiliar conspecifics or maternal behavior. Overall, our findings reveal a major impact of the Neo cassette on behaviors, particularly social behaviors, in Arc/Arg3.1 KO mice, underscoring the need to re-examine phenotypes of animal models carrying the Neo cassette in neuroscience research.

q-bio.NC↗

arXiv2023

Long-time asymptotic of the Lifshitz-Slyozov equation with nucleation

We consider the Lifshitz-Slyozov model with inflow boundary conditions of nucleation type. We show that for a collection of representative rate functions the size distributions approach degenerate states concentrated at zero size for sufficiently large times. The proof relies on monotonicity properties of some quantities associated to an entropy functional. Moreover, we give numerical evidence on the fact that the convergence rate to the goal state is algebraic in time. Besides their mathematical interest, these results can be relevant for the interpretation of experimental data.

math.AP↗

arXiv2023

Modeling compartmentalization within intracellular signaling pathway

We present a new modeling approach for G protein coupled receptors signaling systems, that take into account the compartmentalization of receptors and their effectors, both at plasma membrane and in dynamic intra-cellular vesicles called endosomes. The first building block of the model is about compartment dynamics. It takes into account creation of de-novo endosomes, i.e. endocytosis, recycling of endosomes back to plasma membrane, degradation through transfer into lysosomes as well as endosomes fusion through coagulation dynamics. The second building block is biochemical reactions into each compartments and the transfer of molecules between the dynamical compartments. In this work, we prove sufficient conditions to obtain exponentially ergodicity for the size distribution of intracellular compartments. We futher design a finite volume scheme to simulate our model and show two application cases for receptor trafficking and spatially biased second effector signaling.

math.AP↗

arXiv2021

The Becker-Döring process: pathwise convergence and phase transition phenomena

In this note, we study an infinite reaction network called the stochastic Becker-Döring process, a sub-class of the general coagulation-fragmentation models. We prove pathwise convergence of the process towards the deterministic Becker-Döring equations which improves classical tightness-based results. Also, we show by studying the asymptotic behavior of the stationary distribution, that the phase transition property of the deterministic model is also present in the finite stochastic model. Such results might be interpreted closed to the so-called gelling phenomena in coagulation models. We end with few numerical illustrations that support our results.

math.PR↗

arXiv2020

Stochastic nonlinear model for somatic cell population dynamics during ovarian follicle activation

In mammals, female germ cells are sheltered within somatic structures called ovarian follicles, which remain in a quiescent state until they get activated, all along reproductive life. We investigate the sequence of somatic cell events occurring just after follicle activation, starting by the awakening of precursor somatic cells, and their transformation into proliferative cells. We introduce a nonlinear stochastic model accounting for the joint dynamics of the two cell types, and allowing us to investigate the potential impact of a feedback from proliferative cells onto precursor cells. To tackle the key issue of whether cell proliferation is concomitant or posterior to cell awakening, we assess both the time needed for all precursor cells to awake, and the corresponding increase in the total cell number with respect to the initial cell number. Using the probabilistic theory of first passage times, we design a numerical scheme based on a rigorous Finite State Projection and coupling techniques to compute the mean extinction time and the cell number at extinction time. We find that the feedback term clearly lowers the number of proliferative cells at the extinction time. We calibrate the model parameters using an exact likelihood approach. We carry out a comprehensive comparison between the initial model and a series of submodels, which helps to select the critical cell events taking place during activation, and suggests that awakening is prominent over proliferation.

q-bio.CB↗

arXiv2020

The Initial-boundary value problem for the Lifshitz-Slyozov equation with non-smooth rates at the boundary

We prove existence and uniqueness of solutions to the initial-boundary value problem for the Lifshitz--Slyozov equation (a nonlinear transport equation on the half-line), focusing on the case of kinetic rates with unbounded derivative at the origin. Our theory covers in particular those cases with rates behaving as power laws at the origin, for which an inflow behavior is expected and a boundary condition describing nucleation phenomena needs to be imposed. The method we introduce here to prove existence is based on a formulation in terms of characteristics, with a careful analysis on the behavior near the singular boundary. As a byproduct we provide a general theory for linear continuity equations on a half-line with transport fields that degenerate at the boundary. We also address both the maximality and the uniqueness of inflow solutions to the Lifshitz--Slyozov model, exploiting monotonicity properties of the associated transport equation.

math.AP↗

arXiv2020

Quasi-stationary distribution and metastability for the stochastic Becker-Döring model

We study a stochastic version of the classical Becker-Döring model, a well-known kinetic model for cluster formation that predicts the existence of a long-lived metastable state before a thermodynamically unfavorable nucleation occurs, leading to a phase transition phenomena. This continuous-time Markov chain model has received little attention, compared to its deterministic differential equations counterpart. We show that the stochastic formulation leads to a precise and quantitative description of stochastic nucleation events thanks to an exponentially ergodic quasi-stationary distribution for the process conditionally on nucleation has not yet occurred.

math.PR↗

arXiv2019

Multiscale population dynamics in reproductive biology: singular perturbation reduction in deterministic and stochastic models

In this study, we describe different modeling approaches for ovarian follicle population dynamics, based on either ordinary (ODE), partial (PDE) or stochastic (SDE) differential equations, and accounting for interactions between follicles. We put a special focus on representing the population-level feedback exerted by growing ovarian follicles onto the activation of quiescent follicles. We take advantage of the timescale difference existing between the growth and activation processes to apply model reduction techniques in the framework of singular perturbations. We first study the linear versions of the models to derive theoretical results on the convergence to the limit models. In the nonlinear cases, we provide detailed numerical evidence of convergence to the limit behavior. We reproduce the main semi-quantitative features characterizing the ovarian follicle pool, namely a bimodal distribution of the whole population, and a slope break in the decay of the quiescent pool with aging.

q-bio.TO↗

arXiv2018

Profiling of FSHR Negative Allosteric Modulators on LH/CGR Reveals Biased Antagonism with Implications in Steroidogenesis

Biased signaling has recently emerged as an interesting mean to modulate the function of many G protein-coupled receptors (GPCRs). Previous studies reported two negative allosteric modulators (NAMs) of follicle-stimulating hormone receptor (FSHR), ADX68692 and ADX68693, with differential effects on FSHR-mediated steroidogenesis and ovulation. In this study, we attempted to pharmacologically profile these NAMs on the closely related luteinizing/chorionic gonadotropin hormone receptor (LH/CGR) with regards to its canonical Gs/cAMP pathway as well as β-arrestin recruitment in HEK293 cells. The NAMs effects on progesterone and testosterone production were also assessed in murine Leydig tumor cell line (mLTC-1). We found that both NAMs strongly antagonized LH/CGR signaling in both HEK293 and mLTC-1 cells. ADX68693 appeared more potent than ADX68692 to inhibit hCG-induced cAMP and β-arrestin 2 in HEK293 and mLTC-1 cells whereas no significant difference in their efficacy on hCG-promoted β-arrestin 2 recruitment. Interestingly, differential antagonism of the two NAMs on hCG-promoted steroidogenesis in mLTC-1 cells was observed with significant inhibition of testosterone but not progesterone production. These observations suggest biased effects of the two NAMs on LH/CGR-dependent pathways controlling steroidogenesis, which appeared to be different to that previously shown on FSHR. This also illustrates the complexity of signaling pathways controlling FSHR- and LH/CGR-mediated steroidogenesis, suggesting differential implication of cAMP and β-arrestins. Together, our data demonstrate that ADX68692 and ADX68693 are NAMs at the LH/CGR in addition to FSHR. These pharmacological characteristics are important to consider for potential contraceptive and therapeutic applications based on such compounds.

q-bio.MN↗

arXiv2018

Advances in computational modeling approaches in pituitary gonadotropin signaling

Pituitary gonadotropins play an essential and pivotal role in the control of human and animal reproduction within the hypothalamic-pituitary-gonadal (HPG) axis. The computational modeling of pituitary gonadotropin signaling encompasses phenomena of different natures such as the dynamic encoding of gonadotropin secretion, and the intracellular cascades triggered by gonadotropin binding to their cognate receptors, resulting in a variety of biological outcomes. We overview historical and ongoing issues in modeling and data analysis related to gonadotropin secretion in the field of both physiology and neuro-endocrinology. We mention the different mathematical formalisms involved, their interest and limits. We discuss open statistical questions in signal analysis associated with key endocrine issues. We also review recent advances in the modeling of the intracellular pathways activated by gonadotropins, which yields promising development for innovative approaches in drug discovery. The greatest challenge to be tackled in computational modeling of pituitary gonadotropin signaling is the embedding of gonadotropin signaling within its natural multi-scale environment, from the single cell level, to the organic and whole HPG level. The development of modeling approaches of G protein-coupled receptor signaling, together with multicellular systems biology may lead to unexampled mechanistic understanding with critical expected fallouts in the therapeutic management of reproduction.

q-bio.MN↗

arXiv2018

Workflow description to dynamically model β-arrestin signaling networks

Dynamic models of signaling networks allow the formulation of hypotheses on the topology and kinetic rate laws characterizing a given molecular network, in-depth exploration and confrontation with kinetic biological data. Despite its standardization, dynamic modeling of signaling networks still requires successive technical steps that need to be carefully performed. Here, we detail these steps by going through the mathematical and statistical framework. We explain how it can be applied to the understanding of β-arrestin-dependent signaling networks. We illustrate our methodology through the modeling of β-arrestin recruitment kinetics at the Follicle Stimulating Hormone (FSH) receptor supported by in-house Bioluminescence Resonance Energy Transfer (BRET) data.

q-bio.MN↗

arXiv2018

Follicle-stimulating hormone receptor: Advances and remaining challenges

Follicle-stimulating hormone (FSH) is produced in the pituitary and is essential for reproduction. It specifically binds to a membrane receptor (FSHR) expressed in somatic cells of the gonads. The FSH/FSHR system presents many peculiarities compared to classical G protein-coupled receptors (GPCRs). FSH is a large naturally heterogeneous heterodimeric glycoprotein. The FSHR is characterized by a very large NH2-terminal extracellular domain, which binds the FSH and participates to the activation/inactivation switch of the receptor. Once activated, the FSHR couples to Gαs and, in some instances, to other Gα subunits. G protein-coupled receptor kinases and β-arrestins are also recruited to the FSHR and account for its desensitization, the control of its trafficking and its intracellular signalling. Of note, the FSHR internalization and recycling are very fast and involve very early endosomes instead of early endosomes. All the transduction mechanisms triggered upon FSH stimulation lead to the activation of a complex signalling network that controls gene expression by acting at multiple levels. The integration of these mechanisms leads to context-adapted responses from the target gonadal cells, but also indirectly affects the fate of germ cells. Depending of the physiological/developmental stage, FSH elicits proliferation, differentiation or apoptosis in order to maintain the homeostasis of the reproductive system. Pharmacological tools targeting FSHR recently came to the fore and open promising prospects both for basic research and therapeutic applications. This paper provides an updated review of the most salient aspects and peculiarities of FSHR biology and pharmacology.

q-bio.MN↗

arXiv2018

Computational modeling approaches in gonadotropin signaling

Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play essential roles in animal reproduction. They exert their function through binding to their cognate receptors, which belong to the large family of G protein-coupled receptors (GPCRs). This recognition at the plasma membrane triggers a plethora of cellular events, whose processing and integration ultimately lead to an adapted biological response. Understanding the nature and the kinetics of these events is essential for innovative approaches in drug discovery. The study and manipulation of such complex systems requires the use of computational modeling approaches combined with robust in vitro functional assays for calibration and validation. Modeling brings a detailed understanding of the system and can also be used to understand why existing drugs do not work as well as expected, and how to design more efficient ones.

q-bio.MN↗

arXiv2018

Human Luteinizing Hormone and Chorionic Gonadotropin Display Biased Agonism at the LH and LH/CG Receptors

Human luteinizing hormone (LH) and chorionic gonadotropin (hCG) have been considered biologically equivalent because of their structural similarities and their binding to the same receptor; the LH/CGR. However, accumulating evidence suggest that LH/CGR differentially responds to the two hormones triggering differential intracellular signaling and steroidogenesis. The mechanistic basis of such differential responses remains mostly unknown. Here, we compared the abilities of recombinant rhLH and rhCG to elicit cAMP, β-arrestin 2 activation, and steroidogenesis in HEK293 cells and mouse Leydig tumor cells (mLTC-1). For this, BRET and FRET technologies were used allowing quantitative analyses of hormone activities in real-time and in living cells. Our data indicate that rhLH and rhCG differentially promote cell responses mediated by LH/CGR revealing interesting divergences in their potencies, efficacies and kinetics: rhCG was more potent than rhLH in both HEK293 and mLTC-1 cells. Interestingly, partial effects of rhLH were found on β-arrestin recruitment and on progesterone production compared to rhCG. Such a link was further supported by knockdown experiments. These pharmacological differences demonstrate that rhLH and rhCG act as natural biased agonists. The discovery of novel mechanisms associated with gonadotropin-specific action may ultimately help improve and personalize assisted reproduction technologies

q-bio.MN↗

arXiv2017

Analysis and calibration of a linear model for structured cell populations with unidirectional motion : Application to the morphogenesis of ovarian follicles

We analyze a multi-type age dependent model for cell populations subject to unidirectional motion, in both a stochastic and deterministic framework. Cells are distributed into successive layers; they may divide and move irreversibly from one layer to the next. We adapt results on the large-time convergence of PDE systems and branching processes to our context, where the Perron-Frobenius or Krein-Rutman theorem can not be applied. We derive explicit analytical formulas for the asymptotic cell number moments, and the stable age distribution. We illustrate these results numerically and we apply them to the study of the morphodynamics of ovarian follicles. We prove the structural parameter identifiability of our model in the case of age independent division rates. Using a set of experimental biological data, we estimate the model parameters to fit the changes in the cell numbers in each layer during the early stages of follicle development.

q-bio.PE↗

arXiv2017

Probabilistic and Piecewise Deterministic models in Biology

We present recent results on Piecewise Deterministic Markov Processes (PDMPs), involved in biological modeling. PDMPs, first introduced in the probabilistic literature by Davis (1984), are a very general class of Markov processes and are being increasingly popular in biological applications. They also give new interesting challenges from the theoretical point of view. We give here different examples on the long time behavior of switching Markov models applied to population dynamics, on uniform sampling in general branching models applied to structured population dynamic, on time scale separation in integrate-and-fire models used in neuroscience, and, finally, on moment calculus in stochastic models of gene expression.

math.PR↗

arXiv2016

Deterministic and Stochastic Becker-Döring equations: Past and Recent Mathematical Developments

We present a survey on the results on a particular coagulation-fragmentation model given by the Becker-Döring equations. For both the deterministic and stochastic versions, we include well-posedness, long-time behavior, convergence rate towards equilibrium, coarsening and relation to transport equations, time-dependent properties, metastability and classical nucleation theory. All along this survey, we highlight recent results and open questions.

math-ph↗

arXiv2016

Quasi steady state approximation of the small clusters in Becker-Döring equations leads to boundary conditions in the Lifshitz-Slyozov limit

This papers addresses the connection between two classical models of phase transition phenomena describing different stages of the growth of clusters. The Becker-Döring model (BD) describes discrete-sized clusters through an infinite set of ordinary differential equations. The Lifshitz-Slyozov equation (LS) is a transport partial differential equation on the continuous half-line $x\in (0,+\infty)$. We introduce a scaling parameter $\varepsilon>0$, which accounts for the grid size of the state space in the BD model, and recover the LS model in the limit $\varepsilon\to 0$. The connection has been already proven in the context of outgoing characteristic at the boundary $x=0$ for the LS model, when small clusters tend to shrink. The main novelty of this work resides in a new estimate on the growth of small clusters, which behave at a fast time scale. Through a rigorous quasi steady state approximation, we derive boundary conditions for the incoming characteristic case, when small clusters tend to grow.

math.AP↗