Sur deux problèmes mathématiques de reconstruction phylogénétique

Ce travail de thèse traite de deux problèmes liés aux méthodes de reconstruction d'arbres phylogénétiques. Dans une première partie, nous fournissons des estimateurs consistants ainsi que des intervalles de confiance asymptotiques mathématiquement rigoureux pour le temps d'évolution de séquences d'ADN dans des modèles de substitutions plus réalistes que les modèles usuels, prenant en compte les effets de la méthylation des dinucléotides CpG dans le génome des mammifères. Dans une seconde partie, nous étendons un résultat récent de Steel et Matsen en prouvant qu'un des travers bien connu des méthodes Bayésiennes en phylogénie, appelé "star tree paradox", a en fait lieu dans un cadre plus large que celui de Steel et Matsen.In this thesis, we deal with two problems of phylogeny reconstruction. First, we consider models of nucleotidic substitution processes where the rate of substitution at a given site depends on the state of the neighbours of the site. We estimate the time elapsed between an ancestral sequence at stationarity and a present sequence. Then, assuming that two sequences are issued from a common ancestral sequence at stationarity, we estimate the time since divergence. In the simplest nontrivial case of a Jukes-Cantor model with CpG influence, we provide and justify mathematically consistent estimators in these two settings. We also provide asymptotic confidence intervals, valid for nucleotidic sequences of finite length, and we compute explicit formulas for the estimators and for their confidence intervals. In the general case of an RN model with YpR influence, we extend these results under a proviso, namely that the equation defining the estimator has a unique solution. Second, we show that the Bayesian star paradox, first proved mathematically by Steel and Matsen for a specific class of prior distribution, occurs in a wider context.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

Rapid, Sensitive and Real-Time Multiplexing Platform for the Analysis of Protein and Nucleic-Acid Biomarkers

We describe a multiplexing technology, named Evalution, based on novel digitally encoded microparticles in microfluidic channels. Quantitative multiplexing is becoming increasingly important for research and routine clinical diagnostics, but fast, easy-to-use, flexible and highly reproducible technologies are needed to leverage the advantages of multiplexing. The presented technology has been tailored to ensure (i) short assay times and high reproducibility thanks to reaction-limited binding regime, (ii) dynamic control of assay conditions and real-time binding monitoring allowing optimization of multiple parameters within a single assay run, (iii) compatibility with various immunoassay formats such as coflowing the samples and detection antibodies simultaneously and hence simplifying workflows, (iv) analyte quantification based on initial binding rates leading to increased system dynamic range and (v) high sensitivity via enhanced fluorescence collection. These key features are demonstrated with assays for proteins and nucleic acids showing the versatility of this technology

Unidirectional P-body transport during the yeast cell cycle.

P-bodies belong to a large family of RNA granules that are associated with post-transcriptional gene regulation, conserved from yeast to mammals, and influence biological processes ranging from germ cell development to neuronal plasticity. RNA granules can also transport RNAs to specific locations. Germ granules transport maternal RNAs to the embryo, and neuronal granules transport RNAs long distances to the synaptic dendrites. Here we combine microfluidic-based fluorescent microscopy of single cells and automated image analysis to follow p-body dynamics during cell division in yeast. Our results demonstrate that these highly dynamic granules undergo a unidirectional transport from the mother to the daughter cell during mitosis as well as a constrained "hovering" near the bud site half an hour before the bud is observable. Both behaviors are dependent on the Myo4p/She2p RNA transport machinery. Furthermore, single cell analysis of cell size suggests that PBs play an important role in daughter cell growth under nutrient limiting conditions

P-body transport to the daughter cell is dependent on Myo4p, She2p and She3p.

<p>(<b>A–C</b>) Sequence of images tracking p-bodies in strains lacking Myo4p, She2p or She3p. Experiments were performed and plotted as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099428#pone-0099428-g002" target="_blank">Fig. 2</a>. One cell is shown here for the <i>she2</i>Δ and <i>she3</i>Δ strains and two cells are shown for the <i>myo4</i>Δ strain. 3 images from the time-lapse experiment are shown. The last panel summarizes the path of the p-body during cell division. Scale bar, 2 µm. (<b>D–F</b>) Spatial coordinates of p-bodies from cells shown in (<b>A</b>) where color corresponds to time. (<b>G–I</b>) Cluster analysis performed in each cell, as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099428#pone-0099428-g002" target="_blank">Fig. 2C</a>. In the mutants p-bodies do not move to the daughter cell and p-bodies change from cluster 1 to cluster 2 multiple times.</p

Microfluidics device for studying p-body localization in yeast.

<p>(<b>A</b>) The device is a simplified version of a published design <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099428#pone.0099428-Falconnet1" target="_blank">[23]</a> that consists of 16 chambers in a 4-by-4 matrix with four media inputs accessible via rows and four cell inputs accessible via columns. The device has two layers: the control layer (red) and flow layer (blue). Cells and media are transported within the flow layer channels with the direction controlled by actuating overlaying control layer valves. Parts of the device are marked as follows: 1) cell-loading inlets, 2) chambers where cells are trapped, 3) medium inputs, 4) multiplexer to deliver medium to specific chambers, 5) waste outlet. (<b>B</b>) Close up of 4 chambers with the controlling valves. (<b>C</b>) Micrograph of a chamber with cells trapped, seen at a 4x magnification in bright field light illumination. Scale bar is 20 µm. (<b>D</b>) P-body formation in response to low glucose. Graph shows results from 2 experiment controls (black and grey) in the presence of 2% glucose medium and 2 experiments (purple and red) where p-bodies were induced in the presence of 0.1% glucose medium (4 experiments, total number of cells: 400). Images were taken every 60 seconds over 200 min, in bright field and fluorescent light. The resulting cell numbers were averaged over 20 min periods to filter noise. Custom software for automated quantification of p-bodies per cell was used (see Methods for a detailed description of the analysis).</p

P-body movement is directional and correlates with cell size.

<p>(<b>A</b>) Reversibility analysis of the movement. A reversibility rate of PB movement was calculated for each strain (wild-type, <i>she2</i>Δ, <i>she3</i>Δ and <i>myo4</i>Δ) for 11, 11, 12 and 16 cells respectively, as explained in the main text. Non-zero values for wild-type typically occur due to a few ‘miss-clustered’ points during the transition phase. (<b>B</b>) Cluster betweenness. By relating points within a cluster to points within the other cluster we can quantitatively compare the separation of PB movement between the strains (Materials and Methods). Similar to the temporal analysis by <i>R_rev</i> we see a significant difference between the wild-type and mutant strains. (<b>C</b>) Area of she2Δ cells at the time of budding that had received a PB (blue), not received a PB but later formed one <i>de novo</i> (gray), and completely lacked a detectable PB (red). The population of cells that received a PB during cell division were 33% larger than cells that did not (p<0.03).</p