Métodos bayesianos para la estimación de redes reguladoras de genes y de perfiles de proteínas a partir de microarrays de expresión genética

Resumen

The genetic inheritance that a living being transmits to its offspring is stored in DNA macromolecules inside the nucleus of prokaryote cells, or in form of RNA in the cytoplasm of eukaryotic organisms. The nucleotide sequence of these nucleic acids encodes the characteristics of each individual of a species and potentially controls its cell development. The part of the code that regulates a feature completely is called a gene and is the hereditary information storage unit. The central dogma of Molecular Biology describes by a unidirectional flowchart the way the genetic information is encoded, stored or transmitted from a living being to its offspring. In prokaryotes, unlike in eukaryotes, DNA is not directly responsible for the cellular development. First, the information stored in DNA is transcribed into a RNA molecule and subsequently it is translated into a protein, that is actively involved in cell metabolism. When the information stored within a gene is translated into a functional protein, it is said that the gene is expressed or activated. Microarray experiments is an experimental procedure for quantifying the expression of thousand of genes simultaneously. With this technique, gene expression can be massively profiled, leading to huge data sets that are widely available in public databases. Additionally, chromatin inmunoprecipitation as well as other novel techniques combined with gene sequencing allows to identifying gene-protein interactions and performing TF binding site prediction to estimate the topology of the transcriptional network. Whilst most recent experimental and computational techniques allows to measure gene expression and to predict the transcriptional regulatory structure, other kind of biological features of interest such as the gene regulatory network or protein abundance are difficult to estimate. Uncovering the GRN is interesting form the point of view of Systems Biology to understand how genes compete and are associated to produce complex responses and co-operative effects. On the other hand, TF profiles may dissect diseases with characteristic molecular signatures allowing its diagnostic. All these information is extremely important in many fields such as disease treatment and new drug design and it analysis demands help from the Computer Science community.