Reconstruction ancestrale et investigation de recombinaison génomique sur chloroplastes génomes

The theory of evolution is based on modern biology. All new species emerge of an existing species. As a result, different species share common ancestry,as represented in the phylogenetic classification. Common ancestry may explainthe similarities between all living organisms, such as general chemistry, cell structure,DNA as genetic material and genetic code. Individuals of one species share the same genes but (usually) different allele sequences of these genes. An individual inheritsalleles of their ancestry or their parents. The goal of phylogenetic studies is to analyzethe changes that occur in different organisms during evolution by identifying therelationships between genomic sequences and determining the ancestral sequences and theirdescendants. A phylogeny study can also estimate the time of divergence betweengroups of organisms that share a common ancestor. Phylogenetic trees are usefulin the fields of biology, such as bioinformatics, for systematic phylogeneticsand comparative. The evolutionary tree or the phylogenetic tree is a branched exposure the relationsevolutionary between various biological organisms or other existence depending on the differences andsimilarities in their genetic characteristics. Phylogenetic trees are built infrom molecular data such as DNA sequences and protein sequences. Ina phylogenetic tree, the nodes represent genomic sequences and are calledtaxonomic units. Each branch connects two adjacent nodes. Each similar sequencewill be a neighbor on the outer branches, and a common internal branch will link them to acommon ancestor. Internal branches are called hypothetical taxonomic units. Thus,Taxonomic units gathered in the tree involve being descended from a common ancestor. Ourresearch conducted in this dissertation focuses on improving evolutionary prototypesappropriate and robust algorithms to solve phylogenetic inference problems andancestral information about the order of genes and DNA data in the evolution of the complete genome, as well astheir applications.La théorie de l’évolution repose sur la biologie moderne. Toutes les nouvelles espèces émergent d’une espèce existante. Il en résulte que différentes espèces partagent une ascendance commune, telle que représentée dans la classification phylogénétique. L’ascendance commune peut expliquer les similitudes entre tous les organismes vivants, tels que la chimie générale, la structure cellulaire, l’ADN comme matériau génétique et le code génétique. Les individus d’une espèce partagent les mêmes gènes mais (d’ordinaire) différentes séquences d’allèles de ces gènes. Un individu hérite des allèles de leur ascendance ou de leurs parents. Le but des études phylogénétiques est d’analyser les changements qui se produisent dans différents organismes pendant l’évolution en identifiant les relations entre les séquences génomiques et en déterminant les séquences ancestrales et leurs descendants. Une étude de phylogénie peut également estimer le temps de divergence entre les groupes d’organismes qui partagent un ancêtre commun. Les arbres phylogénétiques sont utiles dans les domaines de la biologie, comme la bio informatique, pour une phylogénétique systématique et comparative. L’arbre évolutif ou l’arbre phylogénétique est une exposition ramifiée les relations évolutives entre divers organismes biologiques ou autre existence en fonction des différences et des similitudes dans leurs caractéristiques génétiques. Les arbres phylogénétiques sont construits à partir de données moléculaires comme les séquences d’ADN et les séquences de protéines. Dans un arbre phylogénétique, les nœuds représentent des séquences génomiques et s’appellent des unités taxonomiques. Chaque branche relie deux nœuds adjacents. Chaque séquence similaire sera un voisin sur les branches extérieures, et une branche interne commune les reliera à un ancêtre commun. Les branches internes sont appelées unités taxonomiques hypothétiques. Ainsi, les unités taxonomiques réunies dans l’arbre impliquent d’être descendues d’un ancêtre commun. Notre recherche réalisée dans cette dissertation met l’accent sur l’amélioration des prototypes évolutifs appropriés et des algorithmes robustes pour résoudre les problèmes d’inférence phylogénétiques et ancestrales sur l’ordre des gènes et les données ADN dans l’évolution du génome complet, ainsi que leurs applications.[...

Iterative method for solving linear operator equation of the first kind

In this work, we study the iterative method for solving linear operator equation of the first kind. We present a new version of method based on the applied the iterative performance on the modified Lavrentiev method. This method is used to resolve a linear operator problem of the first kind. The suggested iterative can used to compute approximate solutions with high quality than the (standard) modified Lavrentiev regularization method. We also compared the new iterative method (modified Lavrentiev) with Landweber iterative method. The numerical testing shows the efficiency of the new iterative method in its application to resolve the inverse heat equation when trying to find the boundary value function. • Studying of new iteration algorithm and mathematical experimentations show the efficiency of the new iteration method. • Iteration method is depended on decomposed the main linear operator by using polar decomposition in order to obtain unitary operator. • The new unitary operator increases the convergence of iteration

Relation between Gene Content and Taxonomy in Chloroplasts

International audienceThe aim of this study is to investigate the relation that can be found between the phylogeny of a large set of complete chloroplast genomes, and the evolution of gene content inside these sequences. Core and pan genomes have been computed on de novo annotation of these 845 genomes, the former being used for producing well-supported phylogenetic tree while the latter provides information regarding the evolution of gene contents over time. It details too the specificity of some branches of the tree, when specificity is obtained on accessory genes. After having detailed the material and methods, we emphasize some remarkable relation between well-known events of the chloroplast history, like endosymbiosis, and the evolution of gene contents over the phylogenetic tree

Ancestral Reconstruction and Investigations of Genomic Recombination on some Pentapetalae Chloroplasts

In this article, we propose a semi-automated method to rebuild genome ancestors of chloroplasts by taking into account gene duplication. Two methods have been used in order to achieve this work: a naked eye investigation using homemade scripts, whose results are considered as a basis of knowledge, and a dynamic programming based approach similar to Needleman-Wunsch. The latter fundamentally uses the Gestalt pattern matching method of sequence matcher to evaluate the occurrences probability of each gene in the last common ancestor of two given genomes. The two approaches have been applied on chloroplastic genomes from Apiales, Asterales, and Fabids orders, the latter belonging to Pentapetalae group. We found that Apiales species do not undergo indels, while they occur in the Asterales and Fabids orders. A series of experiments was then carried out to extensively verify our findings by comparing the obtained ancestral reconstruction results with the latest released approach called MLGO (Maximum Likelihood for Gene-Order analysis)

Comparison of metaheuristics to measure gene effects on phylogenetic supports and topologies

Abstract Background A huge and continuous increase in the number of completely sequenced chloroplast genomes, available for evolutionary and functional studies in plants, has been observed during the past years. Consequently, it appears possible to build large-scale phylogenetic trees of plant species. However, building such a tree that is well-supported can be a difficult task, even when a subset of close plant species is considered. Usually, the difficulty raises from a few core genes disturbing the phylogenetic information, due for example from problems of homoplasy. Fortunately, a reliable phylogenetic tree can be obtained once these problematic genes are identified and removed from the analysis.Therefore, in this paper we address the problem of finding the largest subset of core genomes which allows to build the best supported tree. Results As an exhaustive study of all core genes combination is untractable in practice, since the combinatorics of the situation made it computationally infeasible, we investigate three well-known metaheuristics to solve this optimization problem. More precisely, we design and compare distributed approaches using genetic algorithm, particle swarm optimization, and simulated annealing. The latter approach is a new contribution and therefore is described in details, whereas the two former ones have been already studied in previous works. They have been designed de novo in a new platform, and new experiments have been achieved on a larger set of chloroplasts, to compare together these three metaheuristics. Conclusions The ways genes affect both tree topology and supports are assessed using statistical tools like Lasso or dummy logistic regression, in an hybrid approach of the genetic algorithm. By doing so, we are able to provide the most supported trees based on the largest subsets of core genes

Solving of the Inverse Boundary Value Problem for the Heat Conduction Equation in Two Intervals of Time

The boundary value problem, BVP, for the PDE heat equation is studied and explained in this article. The problem declaration comprises two intervals; the (0, T) is the first interval and labels the heating of the inside burning chamber, and the second (T, ∞) interval defines the normal cooling of the chamber wall when the chamber temperature concurs with the ambient temperature. It is necessary to prove the boundary function of this problem has its place in the space H10,∞ in order to successfully apply the Fourier transform method. The applicability of the Fourier transform for time to this problem is verified. The method of projection regularization is used to solve the inverse boundary value problem for the heat equation and to obtain an evaluation for the error between the approximate and the real solution. These results are new and of practical interest as shown in the numerical case study