We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Highly parallel gene-expression analysis has led to analysis of gene regulation, in particular coregulation, at a system level. Promoter analysis and interaction network toolset (PAINT) was developed to provide the biologist a computational tool to integrate functional genomics data, for example, from microarray-based gene-expression analysis with genomic sequence data to carry out transcriptional regulatory network analysis (TRNA). TRNA combines bioinformatics, used to identify and analyze gene-regulatory regions, and statistical significance testing, used to rank the likelihood of the involvement of individual transcription factors (TF), with visualization tools to identify TF likely to play a role in the cellular process under investigation. In summary, given a list of gene identifiers PAINT can: (1) fetch potential promoter sequences for the genes in the list, (2) find TF-binding sites on the sequences, (3) analyze the TF-binding site occurrences for over/under-representation compared with a reference, with or without coexpression clustering information, and (4) generate multiple visualizations for these analyses. At present, PAINT supports TRNA of the human, mouse, and rat genomes. PAINT is currently available as an online, web-based service located at: http://www.dbi.tju.edu/dbi/tools/paint.

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information SPONSORING/MONITORING AGENCY REPORT NUMBER(S) AFRL-IF-WP-TR-2006-1532 DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. SUPPLEMENTARY NOTESReport contains color. PAO Case Number: AFRL/WS 06-2112, 30 Aug. 2006. ABSTRACTThe overall goal of the project was to develop structured approaches to modeling complex gene regulation dynamics underlying cellular adaptation in mammalian systems. This involves integration of two erstwhile disjoint aspects: mathematical modeling and bioinformatics of high-throughput biological data. As part of a structured approach to tackle this problem, we have developed methods and software tools for identification of 1) robust patterns of gene expression using a meta-clustering approach, 2) network structures from these patterns, and 3) a continuous-time regulatory network model based on temporally discrete gene expression data and predicted network structures. A web-based, graphical user interface was developed for the network structure prediction software, PAINT, and has been released as a DARPA BioSPICE module. We have successfully employed our structured approach in the study of various gene regulatory networks from in silico model systems, yeast cell cycle, neuronal differentiation and adaptation, circadian rhythms, and cellular response to pathogens.15.