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Prediction Using PAINT 51even in simple systems this is a significant challenge, and one that is so farunaddressed for the particular needs of biomedically relevant mammaliancell systems.Zak et al. (9,10) performed in silico analyses of the potential for use of geneexpression results in estimation of functions such as gene-regulatory networks.From these analyses it is clear that, whereas gene-expression data can greatlyreduce the uncertainty in model estimation, meaningful predictions of a particularsystem of gene regulation (i.e., one that would be worth experimental test)cannot be reached using realistically obtainable gene-expression data alone.However, if gene-expression data can be combined with other data and/orknowledge, meaningful model predictions can be reliably achieved. For example,combination of gene-expression data with information on TF activity andlocalization can reliably predict gene-expression networks (10). These resultssupport the hypothesis that the nascent large-scale data-acquisition methods inthe present postgenomic period will be useful for a so-called systems biologyapproach, depending on development of appropriate informatics tools andanalysis approaches motivating “promoter analysis and interaction networktoolset” (PAINT) development for scalable TRNA (11).1.2. TRNA ApproachAs a starting point the TRNA approach resembles what an investigatorwould do when analyzing the regulation or expression of one or two genes byhand, finding what promoter sites are associated with the gene(s) and developingcontextual information from the literature. However, in the case of TRNAthis work is being done simultaneously for an indefinitely long gene list (11).The experimental and computational methods presented herein identify a setof genes and TF that are significant in understanding the function of the generegulatorynetwork in question. The primary purpose of PAINT is to provide ascalable and extensible platform to automate the process of mining the existingdatabases for known regulatory information for a large number of genes ofinterest in a particular biological experiment or analysis. The analysis rests onuse of databases of relevant information, includes evaluation of the results(e.g., of the probability of significance of a result), and automatically providespattern data on gene groupings and relationships by various standards. Presenttechnological developments have resulted in rapidly growing public resourcescontaining systematic data sets of various types: gene expression changesfrom microarrays; protein–DNA interaction and TF-activity data from proteinbindingassays, chromatin immunoprecipitation experiments, and DNA footprinting;protein–protein interactions from two-hybrid experiments andcoimmunoprecipitation; and genomic sequence and ontology information inpublic databases.