The long term goal of this research project is to achieve quantitative understandings of system-level E. coli chemotaxis behaviors and their underlying molecular level mechanisms. We will develop mathematical models of protein interaction network and its dynamics based on structural and biochemical details of the chemotaxis signaling pathway. These models will be studied by using analytical analysis and numerical simulation methods. The results from these models will be used to explain experimental data and make testable predictions. The iterative comparison between models and experimental data will be used to improve/refine the models. In this proposal, we will focus on studying two essential aspects of the E. coli chemotaxis pathway: 1) The signaling dynamics and function of the chemorecetor array. We will investigate how the mixed receptor array can distinguish different stimuli (signals) and how the cell makes decision based on the information. We want to study the effect of ATP hydrolysis in sensor kinase signaling and how it modulates the kinase response sensitivity to receptor ligand binding. 2) The switching mechanism for flagellar motor and its dependence on mechanical signals. We want to understand how the flagellar motor can be controlled by changes in its mechanical environment (force, load) in addition to the intracelur chemical signals. In summary, we plan to investigate and understand how an E. coli cell senses different (chemical and physical) signals, how it processes this information, and how it makes decisions in complex environments with multiple, time varying cues.

Public Health Relevance

The concepts and tools developed in the quantitative, system-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 #
5R01GM081747-05
Application #
8336875
Study Section
Special Emphasis Panel (ZRG1-BST-F (02))
Program Officer
Deatherage, James F
Project Start
2007-09-24
Project End
2015-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
5
Fiscal Year
2012
Total Cost
$259,481
Indirect Cost
$84,481
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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