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.
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.
|Hu, Bo; Tu, Yuhai (2014) Behaviors and strategies of bacterial navigation in chemical and nonchemical gradients. PLoS Comput Biol 10:e1003672|
|Zhu, Xuejun; Ge, Xin; Li, Ning et al. (2014) Angle sensing in magnetotaxis of Magnetospirillum magneticum AMB-1. Integr Biol (Camb) 6:706-13|
|Hu, Bo; Tu, Yuhai (2013) Precision sensing by two opposing gradient sensors: how does Escherichia coli find its preferred pH level? Biophys J 105:276-85|
|Bi, Shuangyu; Yu, Daqi; Si, Guangwei et al. (2013) Discovery of novel chemoeffectors and rational design of Escherichia coli chemoreceptor specificity. Proc Natl Acad Sci U S A 110:16814-9|
|Lan, Ganhui; Tu, Yuhai (2013) The cost of sensitive response and accurate adaptation in networks with an incoherent type-1 feed-forward loop. J R Soc Interface 10:20130489|
|Vladimirov, Nikita; Tu, Yuhai; Traub, Roger D (2013) Synaptic gating at axonal branches, and sharp-wave ripples with replay: a simulation study. Eur J Neurosci 38:3435-47|
|Tu, Yuhai (2013) Quantitative modeling of bacterial chemotaxis: signal amplification and accurate adaptation. Annu Rev Biophys 42:337-59|
|Zhu, Xuejun; Si, Guangwei; Deng, Nianpei et al. (2012) Frequency-dependent Escherichia coli chemotaxis behavior. Phys Rev Lett 108:128101|
|Lan, Ganhui; Schulmeister, Sonja; Sourjik, Victor et al. (2011) Adapt locally and act globally: strategy to maintain high chemoreceptor sensitivity in complex environments. Mol Syst Biol 7:475|
|Vladimirov, Nikita; Traub, Roger D; Tu, Yuhai (2011) Wave speed in excitable random networks with spatially constrained connections. PLoS One 6:e20536|
Showing the most recent 10 out of 16 publications