Cells rely on complex regulatory networks to sense and respond to environmental cues. The dynamics of theregulatory network governing cellular responses cannot be understood at the level of individual regulatoryproteins, but rather emerges as a result of a complex web of biochemical interactions between multipleproteins, mRNA, and DMA. Our long-term objective to develop computational and experimental methods todissect and analyze regulatory networks. With respect to human health, one of the most importantprokaryotic regulatory networks underlies the type III secretion system (TTSS). The TTSS acts like amolecular syringe to inject bacterial effector proteins into the host cytosol. The TTSS is critical for virulencefor many gram-negative pathogens, including Salmonella, Pseudomonas, E. coli, Shigella, Yersinia,Chlamydia, and Bordetella. Of these, the Salmonella TTSS responsible for invading mammalian cells (SPI-1)has the most well-characterized structure and regulation and is the focus of this proposal. In preliminary experiments, we have observed: 1. there is a temporal order in the expression of SPI-1genes, 2. there is independent control of structural and effector genes, 3. there is hysteresis in theexpression of effectors, and 4. the stochastic component of gene expression is differentially controlled.Based on these experiments, we hypothesize that the dynamics are dictated by two genetic circuits in thepathway. The first acts like a multi-signal integrator that commits to SPI-1 expression. The second is abistable switch, where effectors are irreversibly activated after the needle structure is completed. Thisproposal seeks to use a combination of experiments, theory, and engineering to quantitatively characterizethese circuits.
Aim 1 : Characterize two genetic circuits in the SPI-1 regulatory pathway. The first is responsible forintegrating many environmental inputs and committing to the expression of the TTSS. The second forms abistable switch that causes effector expression to persist after the input stimulus is removed.
Aim 2 : Engineer the network dynamics by adding artificial feedback loops. To determine how the topology ofregulatory interactions encodes network dynamics, artificial feedback loops will be used to geneticallyperturb the network. This will provide insight into how complex dynamics evolve.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
7R01AI067699-06
Application #
8398145
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Alexander, William A
Project Start
2007-01-01
Project End
2012-08-15
Budget Start
2011-08-01
Budget End
2012-08-15
Support Year
6
Fiscal Year
2011
Total Cost
$101,418
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
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