To survive, organisms must continually adapt to changing conditions. While some of these responses represent relatively simple mechanisms of homeostasis, others are more complex, reflecting associative learning and even predictive ability. Such sophisticated responses are not solely the domain of multicellular organisms - bacteria have also evolved refined strategies to deal with their complex, changing environments, e.g. circadian rhythms and temperature/oxygen association. While such examples primarily reflect metabolic adaptation, there is recent evidence that bacterial sensory systems are also reshaped by the cell's growth environment. In particular, the chemotaxis network of Escherichia coli undergoes ~10-fold changes in protein levels and ratios in response to nutrient abundance, temperature, and cell density. The large number of assays available for this system and the existence of well-tested quantitative models of its operation make it an ideal target to explore the principles of history-dependent sensing. To carry out this exploration, we will grow E. coli cells under a wide range of physiologically relevant conditions, including nutrient type and abundance, temperature, pH, O2 levels, osmolarity, cell density, and the presence of multiple chemical signals. We will then characterize the chemotactic network at three levels: protein abundances, signaling response to stimulation (via fluorescence resonance energy transfer), and chemotactic behavior (via tracking of single cells swimming in microfluidic gradients). We will exploit the well-established model for chemotactic signaling to interpret our experimental results, and to develop a working model for how growth conditions reshape the chemosensory apparatus. The molecular mechanisms underlying history-dependent regulation, both known and newly discovered, will be characterized by assaying mRNA and protein levels/stability and by exploiting a variety of fluorescent reporters. Finally, we will extend the existing model for chemotactic signaling to determine how chemotactic performance depends on network composition, predict optimal scaling relations between protein levels and receptor cooperativity, and test these predictions with microevolution experiments.

Public Health Relevance

We will investigate how cells of the model bacterium Escherichia coli remodel their sensory apparatus in response to a broad range of external conditions, including nutrients and temperature. It is advantageous for our purposes that the chemosensory system of Escherichia coli is the best-studied and most tractable sensory system of any living organism - it is therefore a natural place to look for general insights into how a cel's history shapes its strategies for survival in a complex and changing environment. We expect the results of our study to apply to a wide range of bacterial species - including major human pathogens - and also to help us understand the sensory strategies employed by eukaryotic cells such as our own.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Lyster, Peter
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Princeton University
Schools of Arts and Sciences
United States
Zip Code
Neumann, Silke; Vladimirov, Nikita; Krembel, Anna K et al. (2014) Imprecision of adaptation in Escherichia coli chemotaxis. PLoS One 9:e84904
Barry, Rachael M; Bitbol, Anne-Florence; Lorestani, Alexander et al. (2014) Large-scale filament formation inhibits the activity of CTP synthetase. Elife 3:e03638
Bitbol, Anne-Florence; Schwab, David J (2014) Quantifying the role of population subdivision in evolution on rugged fitness landscapes. PLoS Comput Biol 10:e1003778
Khalifat, Nada; Rahimi, Mohammad; Bitbol, Anne-Florence et al. (2014) Interplay of packing and flip-flop in local bilayer deformation. How phosphatidylglycerol could rescue mitochondrial function in a cardiolipin-deficient yeast mutant. Biophys J 107:879-90
Press, Maximilian O; Li, Hui; Creanza, Nicole et al. (2013) Genome-scale co-evolutionary inference identifies functions and clients of bacterial Hsp90. PLoS Genet 9:e1003631
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
Borenstein, David Bruce; Meir, Yigal; Shaevitz, Joshua W et al. (2013) Non-local interaction via diffusible resource prevents coexistence of cooperators and cheaters in a lattice model. PLoS One 8:e63304
Cooper, Robert M; Wingreen, Ned S; Cox, Edward C (2012) An excitable cortex and memory model successfully predicts new pseudopod dynamics. PLoS One 7:e33528
Sourjik, Victor; Wingreen, Ned S (2012) Responding to chemical gradients: bacterial chemotaxis. Curr Opin Cell Biol 24:262-8
Oleksiuk, Olga; Jakovljevic, Vladimir; Vladimirov, Nikita et al. (2011) Thermal robustness of signaling in bacterial chemotaxis. Cell 145:312-21

Showing the most recent 10 out of 19 publications