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Modeling the Flagella Regulon in Salmonella

Investigators
Mittler, John
Institutions
University of Washington
Start date
2005
End date
2007
Objective
This is a collaborative project between a bacterial geneticist, a mathematical biologist and a mathematician that will use mathematical models to study the genetic networks responsible for the ordered assembly of the flagellum. To quantify the regulatory molecules responsible for the construction of the flagella, we will develop a system of differential equations to explain the kinetics of appearance of structural and regulatory proteins following induction of flagella regulon in S. typhimurium.

The initial model will consist of a equations for approximately 18 key proteins and operons that govern the ordered assembly of bacterial flagella. We will then use quantitative Western-blot analysis, operon and gene fusions, micro-arrays, microscopy, and bacterial motility assays to estimate parameter values, test the assumptions of the model, and investigate other genes not incorporated in the initial model for flagella biosynthesis.

This data will be used to create refined models that can be used to study the role of genetic feedbacks and time-delays in creating cyclic patterns. These refined models can also be manipulated to predict what would happen had these operons evolved differentially. For example, we could test theoretically what one would expect if some of some of the class 3 genes were under the control of a class 2 promoter.

These theoretical predictions will then be tested in the laboratory using genetically manipulated strains. Because TTSS is a potential target for antibodies, a quantitative study of these regulatory molecules could aid in the development of such vaccines. This is an easy-to-manipulate system in which structures can be easily visualized. Thus, we propose that the flagella could serve a model system for complex regulatory systems.

Funding Source
Nat'l. Inst. of Allergy and Infectious Diseases
Project number
1R21AI059513-01A1
Categories
Salmonella
Predictive Microbiology