Abstract
DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques –an unsupervised artificial neural network called a Self-Organizing Map (SOM)–which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms.
Highlights
A number of methods have been applied over the years in an attempt to uncover the relationships among genes
Instead of combining the gene expression levels from the different datasets to perform the analysis of the behavior of genes during the cell cycle process, we propose to find possible regulatory relationships based on the consistency of gene co-expression patterns in the five time series microarray databases, since genes that are co-expressed throughout a variety of conditions may be controlled by a common regulatory system [21]
The analysis presented in this paper was useful in discovering possible relationships among genes using relatively few time points, based on their consistent behavior over time in the five databases of the S. cerevisiae cell cycle, without any prior knowledge about their characteristics, such as biological function or binding sites contained in them
Summary
A number of methods have been applied over the years in an attempt to uncover the relationships among genes One of these methods involves the modeling of gene relationships as Boolean networks, in which the state of a gene is represented as being ‘‘off’’ or ‘‘on’’ [1]. Even though these models are easy to interpret, it is usually difficult to determine the best way to convert gene expression levels into discrete values; there can be loss of information during the discretization process, which may affect the inference outcome. The genetic regulatory network models obtained with different approaches tend to differ, without being able to reach a consensus on which of them is the most accurate; central assumptions of some models are not supported by experimental evidence [12,13]
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