Proposta de Tese em Bioinformática

UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL

INSTITUTO DE INFORMÁTICA

PROGRAMA DE PÓS-GRADUAÇÃO EM COMPUTAÇÃO

———————————————-

DEFESA DE PROPOSTA DE TESE

Aluna: Fabiane Cristine Dillenburg

Orientadora: Profa. Dra. Leila Ribeiro

Título: An approach for analyzing and classifying microarray data using gene regulatory networks cycles

Linha de Pesquisa: Bioinformática

Data: 01/08/14

Horário: 8:30h

Local: Prédio 43412 – Sala 215 (Sala de Videoconferência), Instituto de Informática

Banca Examinadora:

Profa. Dra. Ana Lucia Cetertich Bazzan (UFRGS)

Prof. Dr. Guido Lenz (UFRGS)

Prof. Dr. Wagner Meira Junior (UFMG) por videoconfência

Presidente da Banca: Profa. Dra. Leila Ribeiro

Resumo:

One of the main research areas in Systems Biology concerns the discovery of biological regulatory pathways or networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. The process of constructing a regulatory network that explains some behavior of the cell using microarrays is done in two steps: first, the set of genes that is thought to be relevant for this biological process is selected and measured in the microarray (for all the samples); then, the gene expression data is analyzed to generate graphs that represent the desired regulatory network. In this work we present a new way of analyzing and classifying microarray datasets, based on the different kind of cycles found among genes, using quantized data obtained from the microarrays. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible. We use the proposed methodology to analyze the genes of the NFkB pathway – which are involved in the control of a plethora of biological processes, ranging from inhibition of apoptosis to pro-apoptotic effects, as well as controlling other important processes as inflammation, invasiveness and metastasis in cancer – in tissues of the most aggressive type of brain tumor (Gliobastomamultiforme – GBM), and in healthy tissues. In GBM samples, we could conclude that the stoichiometric relationship between genes involved in NFkB pathway regulation is unbalanced. This dysregulation can be measured and explained by the identification of a positive cycle, comprising activators genes and actuators genes without the presence of inhibitors genes. This conclusion helps one to understand more about the biology of this tumor type. Furthermore, we classify the samples of a microarray in healthy individuals and patients. Finally, we propose an extension for our methodology with the use of dynamical models (temporal) that will enable a more complete analyze, allowing the better understanding of biological processes and perform of safely biochemical interactions in the cell, allowing, for example, the effective treatment of diseases.

Palavras-chave: Bioinformatics, gene regulatory networks, negative feedback, systems biology, microarrays, gene expression, NFkB, gliobastomamultiforme, classification, temporal analysis.