Signal transduction systems regulate the majority of cellular activities including the metabolism, development, host-recognition, biofilm production, virulence, and antibiotic resistance of human pathogens. Thus, knowledge of the proteins and interactions that comprise these communication networks is an essential component to furthering biomedical discovery. The proliferation of genomic data from high-throughput sequencing projects has dramatically accelerated biological discovery, yet there is an ever-increasing need for computational ? approaches to synthesize meaningful higher-order knowledge from this data. The inherent complexity of signal transduction systems has impeded efforts to elucidate the higher-order properties of these critical regulatory networks at the genomic level. The goal of the proposed research is to develop an effective, computational approach for deriving microbial signal transduction pathways from genomic data and linking these systems to their respective regulatory and metabolic targets. The proposed solution to computationally inferring signal transduction pathways involves a """"""""bottom-up"""""""" approach that begins with analyzing signal transduction proteins at the domain level, followed by establishing clusters of orthologous domains, and finally grouping functionally associated two-component proteins into pathways using genomic context methods. The final product will be a comprehensive database of microbial signal transduction systems and their associated metabolic networks. Knowledge of the signaling systems responsible for the virulence of human pathogens will promote our understanding of bacteria-host interactions, the immune response, novel antibiotic drug discovery, and therapeutic applications for treating disease. Because of the critical role of signal transduction in bacteria, this ? project will significantly impact and contribute to biomedical discovery, public health care, bioremediation, and agriculture. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Small Business Innovation Research Grants (SBIR) - Phase I (R43)
Project #
1R43GM083177-01
Application #
7356345
Study Section
Special Emphasis Panel (ZRG1-BST-Q (03))
Program Officer
Remington, Karin A
Project Start
2008-01-18
Project End
2009-10-17
Budget Start
2008-01-18
Budget End
2009-10-17
Support Year
1
Fiscal Year
2008
Total Cost
$99,500
Indirect Cost
Name
Agile Genomics, LLC
Department
Type
DUNS #
782774314
City
MT Pleasant
State
SC
Country
United States
Zip Code
29466
Ulrich, Luke E; Zhulin, Igor B (2010) The MiST2 database: a comprehensive genomics resource on microbial signal transduction. Nucleic Acids Res 38:D401-7