Project Number: 1907-21000-027-03
Project Type: Specific Cooperative Agreement

Start Date: May 24, 2004
End Date: May 23, 2009

Objective:
The broad aim of this project is to investigate the functional organization of regulatory and signaling networks in bacterial cells, that is, to understanding the information processing embedded within biomolecular networks, with a particular focus on processes of gene regulation in the bacterial plant pathogen Pseudomonas syringae. The proposed research will endeavor to uncover aspects of distributed functionality, specificity, and control by developing models of regulatory and signaling processes at various scales of resolution, and incorporating ideas and techniques from molecular and cell biology, computer science, computational science, statistical mechanics, biochemistry, dynamical systems, and complex systems. Close interaction with the emerging experimental capabilities of the USDA-ARS P. syringae systems biology group will lead to analyses and models of regulatory networks, through the examination of data describing sigma factor-DNA interactions, gene and protein expression, mutant phenotypes, and transcription factor binding sites.

Approach:
Methods motivated by statistical physics, computer science, and complex systems theory will be used to identify putative regulatory motifs in the genome of Pseudomonas syringae. Collections of regulatory sequences upstream of coordinately regulated transcription units in experimental datasets (e.g., microarrays, promoter trap libraries, and transposon mediated mutagenesis screens with reporter systems) will be used to define putative regulons and infer transcription-level gene regulation networks. Molecular mechanisms underlying information processing and control will be related to functional redundancies and compensation, distributed control and specificity of recognition, signal transduction, and interplay with metabolic pathways. The kinematic and dynamic aspects of gene regulation networks will be studied using computational and mathematical methods as sufficient experimental time-series data become available.