• Models. As discussed in Section 5.3.4, computational models must be compared and evaluated. As the number of computational models grows, machine-readable data types that describe computational models—both the form and the parameters of the model—are necessary to facilitate comparison among models.

  • Prose. The biological literature itself can be regarded as data to be exploited to find relationships that would otherwise go undiscovered. Biological prose is the basis for annotations, which can be regarded as a form of metadata. Annotations are critical for researchers seeking to assign meaning to biological data. This issue is discussed further in Chapter 4 (automated literature searching).

  • Declarative knowledge such as hypotheses and evidence. As the complexity of various biological systems is unraveled, machine-readable representations of analytic and theoretical results as well as the underlying inferential chains that lead to various hypotheses will be necessary if relationships are to be uncovered in this enormous body of knowledge. This point is discussed further in Section

In many instances, data on some biological entity are associated with many of these types: for example, a protein might have associated with it two-dimensional images, three-dimensional structures, one-dimensional sequences, annotations of these data structures, and so on.

Overlaid on these types of data is a temporal dimension. Temporal aspects of data types such as fields, geometric information, high-dimensional data, and even graphs—important for understanding dynamical behavior—multiply the data that must be managed by a factor equal to the number of time steps of interest (which may number in the thousands or tens of thousands). Examples of phenomena with a temporal dimension include cellular response to environmental changes, pathway regulation, dynamics of gene expression levels, protein structure dynamics, developmental biology, and evolution. As noted by Jagadish and Olken,4 temporal data can be taken absolutely (i.e., measured on an absolute time scale, as might be the case in understanding ecosystem response to climate change) or relatively (i.e., relative to some significant event such as division, organism birth, or environmental insult). Note also that in complex settings such as disease progression, there may be many important events against which time is reckoned. Many traditional problems in signal processing involve the extraction of signal from temporal noise as well, and these problems are often found in investigating biological phenomena.

All of these different types of data are needed to integrate diverse witnesses of cellular behavior into a predictive model of cellular and organism function. Each data source, from high-throughput microarray studies to mass spectroscopy, has characteristic sources of noise and limited visibility into cellular function. By combining multiple witnesses, researchers can bring biological mechanisms into focus, creating models with more coverage that are far more reliable than models created from one source of data alone. Thus, data of diverse types including mRNA expression, observations of in vivo protein-DNA binding, protein-protein interactions, abundance and subcellular localization of small molecules that regulate protein function (e.g., second messengers), posttranslational modifications, and so on will be required under a wide variety of conditions and in varying genetic backgrounds. In addition, DNA sequence from diverse species will be essential to identify conserved portions of the genome that carry meaning.


Data of all of the types described above contribute to an integrated understanding of multiple levels of a biological organism. Furthermore, since it is generally not known in advance how various components of an organism are connected or how they function, comprehensive datasets from each of these


H.V. Jagadish and F. Olken, “Database Management for Life Science Research,” OMICS: A Journal of Integrative Biology 7(1):131-137, 2003.

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