Estrutura genética populacional de Heliconius erato e Heliconius melpomene (Lepidoptera: Nymphalidae) em fragmentos de Mata Atlântica do Rio Grande do Norte

Extensive studies using molecular markers on butterflies have shown how a highly fragmented landscape may result in the reduction of gene flow among patches of habitat and, consequently, increase genetic differentiation among populations. However, little is known about Heliconius geographical structure and the effects of fragmentation on the connectivity of populations. Furthermore, findings on the effects of the population structure on the dynamics of mimicry evolution in Heliconius butterflies need to be tested in H. erato and H. melpomene specimens found in other locations other than Central and northern South Americas. For the present study, we had two motivations: (1) compare the population structure of H. erato and H. melpomene given the highly fragmented Brazil s Atlantic Forest habitat; and (2) studying population structure of co-mimics could give us insights into the dynamics of mimicry evolution. For this, we analysed the spatial structure and connectivity of eight populations of Heliconius butterflies, in a total of 137 H. erato specimens and 145 H. melpomene specimens, using nine microsatellites loci, 1144 AFLPs markers and 282 mitochondrial DNA sequences. In general, both species exhibited evidence of population subdivision but no isolation by distance indicating some extent of genetic differentiation among populations. Contrary to Kronforst & Gilbert s (2008) Costa Rican Heliconius, H. melpomene exhibited more genetic differentiation than H. erato based on nuclear markers. However, for mitochondrial DNA, H. erato populations showed more genetic differentiation than H. melpomene. Our results corroborate to other studies on Heliconius butterflies concerning the pronounced population subdivision and local genetic drift found in this genus. Nevertheless, the pattern of this differentiation varies significantly from the pattern found in studies conducted in Central America, where H. erato is generally more differentiated and structured than H. melpomene, based on nuclear markers. This different pattern may reflect different evolutionary histories of Heliconius species in Northeastern Brazil s Atlantic Forest

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

A influência do esqueleto açúcar-fostato no transporte da molécula de DNA

This dissertation analyses the influence of sugar-phosphate structure in the electronic transport in the double stretch DNA molecule, with the sequence of the base pairs modeled by two types of quasi-periodic sequences: Rudin-Shapiro and Fibonacci. For the sequences, the density of state was calculated and it was compared with the density of state of a piece of human DNA Ch22. After, the electronic transmittance was investigated. In both situations, the Hamiltonians are different. On the analysis of density of state, it was employed the Dyson equation. On the transmittance, the time independent Schrödinger equation was used. In both cases, the tight-binding model was applied. The density of states obtained through Rudin-Shapiro sequence reveal to be similar to the density of state for the Ch22. And for transmittance only until the fifth generation of the Fibonacci sequence was acquired. We have considered long range correlations in both transport mechanism

Conducão eletrônica e propriedades termodinâmicas da molécula de DNA

In this thesis, we study the thermo-electronic properties of the DNA molecule. For this purpose, we used three types of models with the DNA, all assuming a at geometry (2D), each built by a sequence of quasiperiodic (Fibonacci and / or Rudin-Shapiro) and a sequence of natural DNA, part of the human chromosome Ch22. The first two models have two types of components that are the nitrogenous bases (guanine G, cytosine C, adenine A and thymine T) and a cluster sugar-phosphate (SP), while the third has only the nitrogenous bases. In the first model we calculate the density of states using the formalism of Dyson and transmittance for the time independent Schr odinger equation . In the second model we used the renormalizationprocedure for the profile of the transmittance and consequently the I (current) versus V (voltage). In the third model we calculate the density of states formalism by Dean and used the results together with the Fermi-Dirac statistics for the chemical potential and the quantum specific heat. Finally, we compare the physical properties found for the quasi-periodic sequences and those that use a portion of the genomic DNA sequence (Ch22).