Papers and preprints I have contributed to. The papers and preprints are made available for research purposes only, unless otherwise specified in the document.

Analysis of gene regulatory networks using finite-field models

Humberto Ortiz-Zuazaga

My PhD thesis proposal. Draft version

Microarrays allow researchers to simultaneously measure the expression of thousands of genes. They give invaluable insight into the transcriptional state of biological systems, and can be important in understanding physiological as well as diseased conditions. However, the analysis of data from many thousands of genes, from only a few replications is very difficult.

The major goal of this proposal is to further develop information theoretic techniques for microarray analysis, and specifically, to develop procedures to cluster gene expression values and determine gene regulatory interactions.

We will use published microarray data sets, synthetic data, and a data set from learning and memory processes in rats to test our procedures. In our preliminary data, we have devised a novel method of correcting errors in microarray experiments, that also clusters genes into groups, and categorizes their measurements into coarse divisions, suitable for discrete techniques for reverse engineering. These techniques are based on finite fields and algebraic coding theory.

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Extended abstract, PDF

Candidacy exam presentation, PDF

A finite field deterministic genetic network model

Oscar Moreno, Humberto Ortiz-Zuazaga, Carlos J. Corrada Bravo, María Alicia Aviñó-Diaz, and Dorothy Bollman.

Several research groups \cite{tAsKoMsM98,rScS96, tIvTrK00} have described genetic networks as networks of Boolean variables. For example, in \cite{tIvTrK00} Ideker, Thorsson and Karp present a deterministic Boolean network model for genetic networks. We will show that these Boolean network models are examples of Finite Dynamical Systems (\cite{cBcR99}--\cite{rLbPb02}), and we will generalize the deterministic Boolean network to finite fields. We also show how these Finite Dynamical System models over finite fields may be constructed from microarray experimental data, similar to the method employed by \cite{tIvTrK00} to construct their models, using the results from \cite{tIvTrK00, oMdBmA02}. Our generalization, however, allows for a more natural treatment of microarray data than Boolean variables that have only two possible values, to a full range of discrete values.

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Error correction in genetic networks

Oscar Moreno, Carlos J. Corrada Bravo, Humberto Ortiz-Zuazaga, María Alicia Aviñó-Diaz, and Dorothy Bollman.

This preprint extends the model described in FFDeterGenNet above to include the concept of stimuli over a finite field, and describes an error correction system for micrarray data given a network model and a new expression measurement.

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Hippocampal Gene Expression Profiling in Spatial Discrimination Learning

Yolanda Robles, Pablo E. Vivas-Mejía, Humberto G. Ortiz-Zuazaga, Jahaira Féliz, Xiomara Ramos, and Sandra Peña de Ortiz.

We identify 19 genes significantly altered in rats that have undergone spatial training in a food search maze. The paper describes the experiments, analysis and discusses the biological significance of the altered genes.

Neurobiology of Learning and Memory. 2003 Jul;80(1):80-95. PMID: 12737936

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Troglodita approved!

Humberto Ortiz Zuazaga

Most recent change: 2005/12/1 at 08:07
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