Yves Vandecan

Bio: Yves Vandecan is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Nucleosome & Regulation of gene expression. The author has an hindex of 3, co-authored 3 publications receiving 35 citations.

Self-regulatory gene: an exact solution for the gene gate model

Abstract: The stochastic dynamics of gene expression is often described by highly abstract models involving only the key molecular actors DNA, RNA, and protein, neglecting all further details of the transcription and translation processes. One example of such models is the "gene gate model," which contains a minimal set of actors and kinetic parameters, which allows us to describe the regulation of a gene by both repression and activation. Based on this approach, we formulate a master equation for the case of a single gene regulated by its own product-a transcription factor-and solve it exactly. The obtained gene product distributions display features of mono- and bimodality, depending on the choice of parameters. We discuss our model in the perspective of other models in the literature.

Stochastic description of single nucleosome repositioning by ACF remodelers.

Abstract: Chromatin remodeling plays a crucial role in the activation or repression of transcription of eukaryotic genes. The chromatin remodeler ACF acts as a dimeric, processive motor to evenly space nucleosomes, favoring repression of gene transcription. Single-molecule experiments have established that ACF moves the nucleosome more efficiently towards the longer flanking DNA than towards the shorter flanking DNA, thereby centering an initially ill-positioned nucleosome on DNA substrates. In this paper we present a one-motor model with nucleosomal repositioning rates dependent on the DNA flanking length. The corresponding master equation is solved analytically with experimentally relevant parameter values. The velocity profile and the effective diffusion constant for nucleosome sliding, computed from the probability distributions, are in accordance with available experimental data. In order to address the observed kinetic pauses in experimental Forster Resonance Energy Transfer profiles, we extend the master equation to account for transitions between explicit motor states, i.e., adenosine triphosphate (ATP) loading and ATP hydrolysis in both ACF motors. The results of this extended two-motor model are compared to the previous effective one-motor model and allow insights into the role of the synchronization of the two motors acting on the nucleosome.

Fokker-Planck description of single nucleosome repositioning by dimeric chromatin remodelers.

Abstract: Recent experiments have demonstrated that the ATP-utilizing chromatin assembly and remodeling factor (ACF) is a dimeric, processive motor complex which can move a nucleosome more efficiently towards longer flanking DNA than towards shorter flanking DNA strands, thereby centering an initially ill-positioned nucleosome on DNA substrates. We give a Fokker-Planck description for the repositioning process driven by transitions between internal chemical states of the remodelers. In the chemical states of ATP hydrolysis during which the repositioning takes place a power stroke is considered. The slope of the effective driving potential is directly related to ATP hydrolysis and leads to the unidirectional motion of the nucleosome-remodeler complex along the DNA strand. The Einstein force relation allows us to deduce the ATP-concentration dependence of the diffusion constant of the nucleosome-remodeler complex. We have employed our model to study the efficiency of positioning of nucleosomes as a function of the ATP sampling rate between the two motors which shows that the synchronization between the motors is crucial for the remodeling mechanism to work.

Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization

Abstract: Gene transcription is a highly stochastic and dynamic process. As a result, the mRNA copy number of a given gene is heterogeneous both between cells and across time. We present a framework to model gene transcription in populations of cells with time-varying (stochastic or deterministic) transcription and degradation rates. Such rates can be understood as upstream cellular drives representing the effect of different aspects of the cellular environment. We show that the full solution of the master equation contains two components: a model-specific, upstream effective drive, which encapsulates the effect of cellular drives (e.g. entrainment, periodicity or promoter randomness) and a downstream transcriptional Poissonian part, which is common to all models. Our analytical framework treats cell-to-cell and dynamic variability consistently, unifying several approaches in the literature. We apply the obtained solution to characterize different models of experimental relevance, and to explain the influence on gene transcription of synchrony, stationarity, ergodicity, as well as the effect of time scales and other dynamic characteristics of drives. We also show how the solution can be applied to the analysis of noise sources in single-cell data, and to reduce the computational cost of stochastic simulations.

Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks.

Abstract: Many biochemical reaction networks are inherently multiscale in time and in the counts of participating molecular species. A standard technique to treat different time scales in the stochastic kinetics framework is averaging or quasi-steady-state analysis: it is assumed that the fast dynamics reaches its equilibrium (stationary) distribution on a time scale where the slowly varying molecular counts are unlikely to have changed. We derive analytic equilibrium distributions for various simple biochemical systems, such as enzymatic reactions and gene regulation models. These can be directly inserted into simulations of the slow time-scale dynamics. They also provide insight into the stimulus–response of these systems. An important model for which we derive the analytic equilibrium distribution is the binding of dimer transcription factors (TFs) that first have to form from monomers. This gene regulation mechanism is compared to the cases of the binding of simple monomer TFs to one gene or to multiple copies of a gene, and to the cases of the cooperative binding of two or multiple TFs to a gene. The results apply equally to ligands binding to enzyme molecules.

Stochastic hybrid models of gene regulatory networks - A PDE approach.

Abstract: A widely used approach to describe the dynamics of gene regulatory networks is based on the chemical master equation, which considers probability distributions over all possible combinations of molecular counts. The analysis of such models is extremely challenging due to their large discrete state space. We therefore propose a hybrid approximation approach based on a system of partial differential equations, where we assume a continuous-deterministic evolution for the protein counts. We discuss efficient analysis methods for both modeling approaches and compare their performance. We show that the hybrid approach yields accurate results for sufficiently large molecule counts, while reducing the computational effort from one ordinary differential equation for each state to one partial differential equation for each mode of the system. Furthermore, we give an analytical steady-state solution of the hybrid model for the case of a self-regulatory gene.