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50 Gonye et al.activity and problem within the biomedical research community. Gene lists canbe and have been acquired in any number of ways (e.g., differential display [1],serial analysis of gene expression [2], and large-scale cDNA sequencing [3], orderived from the literature or by pathway analysis), and recently this has beengreatly accelerated, as reagents have become available from the genome projectsenabling “functional genomic” techniques. These “-omics” techniques continueto rapidly proliferate, develop, and improve, and it is clearly by no means atthe end of this technology revolution. At present, for example, DNA microarraymethods have evolved to a point wherein gene expression can be simultaneouslymeasured for tens of thousands of genes under multiple conditions (4–6).However, bridging the gap from raw gene-expression data to interpretationor understanding of its relevance to functional processes remains a large unmetneed. For example, it is now understood that in microarray studies the wet-labdata acquisition phase of the study is minor in comparison with the analysiswork that follows. Thus, whatever the past, present, or future source of genelists there is a tremendous unmet need in the domain of their analysis withinthe research community and opportunity for informatics developments to meetthe need.The objective of the cluster of ongoing interactive developments under theumbrella of a transcriptional regulatory network analysis (TRNA) framework isto work in the area of this unmet need in at least two ways.1. The tools and analysis approaches are useful to the biologist who wants to analyzethe biological context of a gene list in order to more effectively identify transcriptionfactors (TF) and associated genes for more detailed study.2. The informatics will strengthen development of hypotheses and predictions of thefunctional regulation of systems of genes, and thereby greatly facilitate developmentof model structures, as an approach to systems biological problems.1.1. Rationale for a Systems-Level ApproachThe two points at the end of the previous paragraph highlight the intent thatTRNA will be a continuously evolving analysis approach that will expand inquality and application over time. The ability to develop useful informatics inthis area depends not just on the developments but also on the continued rapidexpansion and improvement of the web-accessible public and private resourceson which the approach rests. There is every reason to believe this will not onlycontinue but greatly accelerate. Recently, there have been elegant demonstrationsin simple model systems of how these kinds of data can be combined intomodels of system function (e.g., refs. 7 and 8). Thus, the potential for synthesizingthese kinds of data toward functional understanding is an exciting andrealistic prospect, for example, of predictions of network models of gene regulation,gene output phenotype, and of biochemical pathways/networks. However,