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| Author(s): |
Carlos Henrique Aguena Higa
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Paulo. |
| Institution: | Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) |
| Defense date: | 2006-12-21 |
| Examining board members: |
Ronaldo Fumio Hashimoto;
Roberto Marcondes Cesar Junior;
Hernando Antonio Del Portillo Obando
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| Advisor: | Ronaldo Fumio Hashimoto |
| Abstract | |
This Master\'s degree dissertation describes a research that involves an analysis of gene expression data from microarray experiments with the purpose to find important genes in certain organisms or diseases such as cancer. We believe that these type of genes, called intrinsically multivariately predictive genes (IMP genes), can lead to the discovery of important biological process that are unknown in the literature. The search for IMP genes was done with the study of mathematical and statistical models such as Boolean Networks, Markov Chains, Coefficient of Determination (CoD), Classification and Error Estimation Methods. In the Boolean network model, introduced in Biology by Kauffman, the gene expression is quantized in only two levels: ON and OFF. The expression level (state) of each gene is related with the state of some other genes through a logical function. Adding a random perturbation to this model, we have a more general Boolean-type model called Boolean network with perturbation. The dynamical system represented by this network is an ergodic Markov chain and thereby it possesses a steady-state distribution. We have the hypothesis that the microarray experiments follow this steady-state distribution. The CoD is a normalized measure of how much a gene expression of a target gene can be better predicted observing the expression of a set of predictor genes. A certain configuration of CoDs characterizes a target gene as an IMP gene. We can deal not only with target genes, but also with target phenotypes, where the phenotype of a biological system could be represented by a binary random variable. For example, we could be interested in knowing which genes are related to a life/death cell phenotype. Since the joint probability distribution of the gene expressions is unknown, the CoDs must be computed through estimated values. Among the error estimation methods studied we can cite: Holdout, Resubstitution, Cross-validation, Bootstrap and .632 Bootstrap. Those methods were implemented as a software in order to compute the CoDs and thereby allowing us to search for IMP genes. The software we implemented in this research was used within a research developed by Professor Dr. Hugo A. Armelin from the Instituto de Química - University of Sao Paulo. This particular research involves the search for important genes related to the death of tumorigenic mouse cells triggered by FGF2 (Fibroblast Growth Factor 2). From this research cooperation, we built some gene subnetworks involved in the target biological process and we found some genes that could be related to the death phenotype of mouse cells. This approach of gene expression analysis, together with the research developed by Professor Armelin, results in a methodology to search for important genes that could be involved in new mechanisms of tumorigenic cells triggered by FGF2. Actually, this methodology can be applied to any biological process of scientific interest, if one can model the proposed problem in the context of Boolean Networks, Coefficient of Determination and IMP genes. (AU) | |