The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.
TopIntroduction
Since the detection of the composition of DNA our understanding of biological structures and processes has expanded to a great extent, mostly thanks to computer science which plays a fundamental role in the field of bioinformatics. The main target at present is to analyze and employ the huge amount of accessible data. It is particularly important to distinguish various diseases through useful selection of gene indicators for morbid state and information about the possible correlations between genes.
Data analysis is seen as the largest and possibly the most important area of microarray bioinformatics to obtain the above said targets. Some specific data analysis methods address the fundamental scientific questions about microarray data, that is:
- 1.
Which genes are differentially expressed in one set of samples relative to another,
- 2.
What are the associations between the genes or samples being observed, and
- 3.
Is it possible to group samples based on gene expression values?
In the next section, we illustrate the basic concepts underlying the previous questions and the bioinformatics research. Then we describe the methods for the first of these questions: the search for differentially (up or down) expressed genes. The following sections address the other two topics of clustering and classifying gene profiles. In the end, we show some concerns and issues of interest for future study and development in the field of (microarray) bioinformatics.
TopBackground
Gene expression profiling is an extensively used method in the analysis of microarray data. The leading hypothesis is that genes with similar expression profiles are co-regulated and are probably connected functionally.
Cluster analysis helps reaching these objectives; in particular, gene expression clusters help typify unknown genes assigned to the cluster by those genes that have a known function, and are the support for distinguishing common upstream regulatory sequence elements (Brazma et al., 2000).
Clustering of expression profiles and functional grouping is especially compelling if the complete gene set is known. Hence, we used the large publicly available data set included in the Stanford Yeast Database at http://genome-www.stanford.edu for our clustering study.
Many applications aim at the molecular classification of diseases based on gene expression profiling and clustering. See, for example, works on leukemias (Golub et al., 1999) and B-cell lymphomas (Alizadeh et al., 2000). These and other studies confirm the usefulness of microarray bioinformatics for scientific and industrial research.