Fusión de Conocimiento para la Identificación de Perfiles Genéticos en el Problema de Inflamación Sistémica

  1. Rubio Escudero, Cristina
Supervised by:
  1. Jorge Sergio Igor Zwir Nawrocki Director
  2. Óscar Cordón García Director

Defence university: Universidad de Granada

Fecha de defensa: 14 December 2007

Type: Thesis


To achieve the proposed objectives, this work is divided in several chapters which are structured as follows: Chapter 1, where we introduce some basic biology concepts. We include a brief description of the components which make up living organisms, followed by the molecular processes that underlie biological systems, and a short note to describe biological methods which study DNA sequences. We also make an introduction to the Bioinformatic topic, a relatively young discipline, which has attracted the attention of a great part of the scientific community and has grown up in a world of high-throughput large volume data that requires automatic analysis to enable us to make use of it all. We also make an introduction to microarray experiments, describing the different technologies underlying DNA microarrays and their scope of application. Chapter 2, where we review the microarray development and analysis state of the art. We describe the analysis processes necessary to extract information from microarray experiments, such as high and low level analysis, and show the results obtained applying some conventional microarray analysis methods to a set of data acquired from an experiment carried out over inflammation and host response to injury problem. Such problem, which will be analyzed in detail throughout this work, is described in detail as part of this chapter. Chapter 3, where we propose a conceptual clustering approach which combines the advantages of several microarray analysis methods in an attempt to retrieve all significant gene expression changes from microarray experiments by identifying differential profiles (i.e., sets of genes with coordinate changes in RNA abundance). We define both, gene expression profiles and differential profiles, which are a basic component of our methodology and will be used throughout this work. We show the results obtained applying the proposed methodology on the inflammation and host response to injury problem, and compare them with the results obtained applying conventional microarray analysis methods. We also apply the methodology to an artificial microarray data set created to test our proposal. Chapter 4, where we provide some biological meaning to the differential profiles obtained in Chapter 3, which are a basic components of our methodology. We mine into several biological databases, human gene and diseases, biological pathways, gene product and DNA sequence, in order to, on the one hand, asses the biological cohesiveness of the differential profiles obtained from the problem being studied, and on the other hand, acquire a further understanding of the gene behavior in an inflammation process. Chapter 5, where we apply different methodologies to create gene networks from the differential profiles obtained from the problem under study. We compare genetic network building algorithms from two of the main categories they can be divided in: static and dynamic models. We also use both discrete and continuous data inputs, to get a better knowledge of how this methods work.