Thelong term goal of this research project is to achieve quantitative, systems level understanding of the signal transduction pathway in E. coli chemotaxis. We want to integrate the knowledge on the E. coli chemotaxis signaling pathway over different (length and time) scales into a mathematical description (model) of the system that can be used to explain and predict quantitatively the E. coli chemotaxis response to any given temporal and spatial signal (stimulus). The models will be constructed based on known molecular details of the signaling pathway and at the appropriate resolution comparable to experimental data. These models will be studied by using statistical physics methods, Monte Carlo simulation and dynamical systems analysis. The results from these models will be used to explain existing data, make testable predictions and the comparison with experimental data will feed back to improve/refine the models. In this proposal, we will fociis on two essential aspects of the E. coli chemotaxis pathway: 1) Signal amplification in the (fast) kinase response. We are interested in finding out the structural basis for the observed signal amplification, e.g., how many receptors each cooperative fucntional complex contains. We want to understand the molecular . mechanism for the wide dynamic range of high sensitivity observed in E. coli chemotaxis. We want to understand how cell achieve these excellent properties (high gain, high sensitivty over a wide range of backgrounds) with variable (noisy) internal components. 2) Kinetics of the (slower) adaptationprocess.^We want to understand the adaptation kinetics quantitatively, e.g.,how fast the system adapts and how the adaptation time depends on the external stimulus strength. We want to .understand the adaptation kinetics to time varying stimulus, such as exponential ramps with different ramp rates. Eventually, we want to be able to model and predict the signaling pathway dynamics as the cell moves in its natural environment. :?; The concepts and tools developed in the quantitative, systems level modeling of a complete sensory signal transduction pathway will be useful in understanding signaling pathways and sensory systems in higher organisms, including human. The molecular level understanding of the bacterial chemotaxis pathway is important to study the role of bacterial pathogens in human health.;

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM081747-03
Application #
7683905
Study Section
Special Emphasis Panel (ZRG1-IDM-H (02))
Program Officer
Deatherage, James F
Project Start
2007-09-24
Project End
2011-08-31
Budget Start
2009-09-01
Budget End
2011-08-31
Support Year
3
Fiscal Year
2009
Total Cost
$193,646
Indirect Cost
Name
Ibm Thomas J. Watson Research Center
Department
Type
DUNS #
084006741
City
Yorktown Heights
State
NY
Country
United States
Zip Code
10598
Tu, Yuhai; Cao, Yuansheng (2018) Design principles and optimal performance for molecular motors under realistic constraints. Phys Rev E 97:022403
Tu, Yuhai; Rappel, Wouter-Jan (2018) Adaptation of Living Systems. Annu Rev Condens Matter Phys 9:183-205
Mello, Bernardo A; Pan, Wenlin; Hazelbauer, Gerald L et al. (2018) A dual regulation mechanism of histidine kinase CheA identified by combining network-dynamics modeling and system-level input-output data. PLoS Comput Biol 14:e1006305
Fei, Chenyi; Cao, Yuansheng; Ouyang, Qi et al. (2018) Design principles for enhancing phase sensitivity and suppressing phase fluctuations simultaneously in biochemical oscillatory systems. Nat Commun 9:1434
Renault, Thibaud T; Abraham, Anthony O; Bergmiller, Tobias et al. (2017) Bacterial flagella grow through an injection-diffusion mechanism. Elife 6:
Cao, Li-Hui; Yang, Dong; Wu, Wei et al. (2017) Odor-evoked inhibition of olfactory sensory neurons drives olfactory perception in Drosophila. Nat Commun 8:1357
Li, Zhaojun; Cai, Qiuxian; Zhang, Xuanqi et al. (2017) Barrier Crossing in Escherichia coli Chemotaxis. Phys Rev Lett 118:098101
Cao, Li-Hui; Jing, Bi-Yang; Yang, Dong et al. (2016) Distinct signaling of Drosophila chemoreceptors in olfactory sensory neurons. Proc Natl Acad Sci U S A 113:E902-11
Lan, Ganhui; Tu, Yuhai (2016) Information processing in bacteria: memory, computation, and statistical physics: a key issues review. Rep Prog Phys 79:052601
Sartori, Pablo; Tu, Yuhai (2015) Free energy cost of reducing noise while maintaining a high sensitivity. Phys Rev Lett 115:118102

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