Biology subjects

Lepoutre, T.

Publications and source records attributed to Lepoutre, T..

4 recordsLinked to original sources

bioRxiv2025

Three-State Gene Expression Model Parameterized for Single-Cell Multi-Omics Data

We present a novel three-state gene expression model designed to elucidate the underlying mechanisms of mRNA transcription and its regulation. Our model incorporates gene regulatory processes by explicitly including a transcription factor-bound state, thereby capturing the dynamic interplay between transcription activation and chromatin dynamics. We fit the model to paired single-cell ATAC-seq and single-cell RNA-seq data, as these data give us simultaneous information on a genes transcriptional state and its accompanying chromatin state. Working at the pseudo-bulk level, we extract biologically meaningful high-level descriptors from homogeneous cell (sub)populations, such as the mean and variance of gene expression as well as the fraction of accessible chromatin. Crucial to the computational feasibility of our approach, these descriptors can be analytically related to our model parameters. Despite the increased complexity needed to capture regulatory processes in our model, it remains sufficiently parsimonious to infer parameters reliably from experimental data. Each parameter has a clear biological interpretation, reflecting properties such as burst frequency, chromatin opening and closing dynamics, and basal or regulated expression. Fitting the model to a large collection of genes allows us to analyze the parameters and distinguish so-called gene expression strategies. The model parameters reveal a small number of distinct expression strategies among gene clusters, providing data-driven novel insight into context-dependent regulation of gene expression.

bioinformatics↗

bioRxiv2024

Modeling relaxation experiments with a mechanistic model of gene expression

BackgroundIn the present work, we aimed at modeling a relaxation experiment which consists in selecting a subfraction of a cell population and observing the speed at which the entire initial distribution for a given marker is reconstituted. MethodsFor this we first proposed a modification of a previously published mechanistic two-state model of gene expression to which we added a state-dependent proliferation term. This results in a system of two partial differential equations. Under the assumption of a linear proliferation rate, we could derive the asymptotic profile of the solutions of this model. ResultsIn order to confront our model with experimental data, we generated a relaxation experiment of the CD34 antigen on the surface of TF1-BA cells, starting either from the highest or the lowest CD34 expression levels. We observed in both cases that after approximately 25 days the distribution of CD34 returns to its initial stationary state. Numerical simulations, based on parameter values estimated from the dataset, have shown that the model solutions closely align with the experimental data from the relaxation experiments. ConclusionAltogether our results strongly support the notion that cells should be seen and modeled as probabilistic dynamical systems.

systems biology↗

bioRxiv2022

Adaptation to a changing environment: what me Normal?

Predicting the adaptation of populations to a changing environment is crucial to assess the impact of human activities on biodiversity. Many theoretical studies have tackled this issue by modeling the evolution of quantitative traits subject to stabilizing selection around an optimal phenotype, whose value is shifted continuously through time. In this context, the population fate results from the equilibrium distribution of the trait, relative to the moving optimum. Such a distribution may vary with the shape of selection, the system of reproduction, the number of loci, the mutation kernel or their interactions. Here, we develop a methodology that provides quantitative measures of population maladaptation and potential of survival directly from the entire profile of the phenotypic distribution, without any a priori on its shape. We investigate two different systems of reproduction (asexual and infinitesimal sexual models of inheritance), with various forms of selection. In particular, we recover that fitness functions such that selection weakens away from the optimum lead to evolutionary tipping points, with an abrupt collapse of the population when the speed of environmental change is too high. Our unified framework allows deciphering the mechanisms that lead to this phenomenon. More generally, it allows discussing similarities and discrepancies between the two systems of reproduction, which are ultimately explained by different constraints on the evolution of the phenotypic variance. We demonstrate that the mean fitness in the population crucially depends on the shape of the selection function in the infinitesimal sexual model, in contrast with the asexual model. In the asexual model, we also investigate the effect of the mutation kernel and we show that kernels with higher kurtosis tend to reduce maladaptation and improve fitness, especially in fast changing environments. HighlightsO_LIAdaptation to a changing environment may generate non Normal phenotypic distribution. C_LIO_LIThe phenotypic variance at equilibrium truly depends on reproduction model; C_LIO_LISelection shapes mean fitness only in sexual infinitesimal model; C_LIO_LIWeak selection away from the optimum leads to evolutionary tipping points with fast changes; C_LIO_LIFrequent mutations with large effects reduce maladaptation and improve fitness. C_LI

evolutionary biology↗

bioRxiv2020

Reduction of a stochastic model of gene expression: Lagrangian dynamics gives acces to basins of attraction as cell types and metastability

Differentiation is the process whereby a cell acquires a specific phenotype, by differential gene expression as a function of time. This is thought to result from the dynamical functioning of an underlying Gene Regulatory Network (GRN). The precise path from the stochastic GRN behavior to the resulting cell state is still an open question. In this work we propose to reduce a stochastic model of gene expression, where a cell is represented by a vector in a continuous space of gene expression, to a discrete coarse-grained model on a limited number of cell types. We develop analytical results and numerical tools to perform this reduction for a specific model characterizing the evolution of a cell by a system of piecewise deterministic Markov processes (PDMP). Solving a spectral problem, we find the explicit variational form of the rate function associated to a large deviations principle, for any number of genes. The resulting Lagrangian dynamics allows us to define a deterministic limit of which the basins of attraction can be identified to cellular types. In this context the quasipotential, describing the transitions between these basins in the weak noise limit, can be defined as the unique solution of an Hamilton-Jacobi equation under a particular constraint. We develop a numerical method for approximating the coarse-grained model parameters, and show its accuracy for a symmetric toggle-switch network. We deduce from the reduced model an approximation of the stationary distribution of the PDMP system, which appears as a Beta mixture. Altogether those results establish a rigorous frame for connecting GRN behavior to the resulting cellular behavior, including the calculation of the probability of jumps between cell types.

systems biology↗