Areas of Research: Biological modeling; intracellular networks; molecular biophysics
- Molecular Biology and the Lewis-Sigler Institute for Integrative Genomics
243 Carl Icahn Laboratory
Biological modeling; intracellular networks; molecular biophysics
Intracellular networks in bacteria
Bacteria are constantly sensing their environments and adjusting their behavior accordingly. Signaling occurs through networks of proteins and nucleic acids, culminating in changes of gene expression and so changes in the proteome of the cell. We are focused on the architecture of these intracellular networks. What is the relation between network architecture and function? For example, can we understand the selection of architectures in terms of general information-processing concepts such as signal to noise, memory, and adaptation? Even in a single bacterium such as E. coli, there are hundreds of coexisting networks. Our belief is that a deep study of a small number of "model" networks will yield general tools to analyze information processing by cell. It is important to choose these model networks carefully. The network components should be well characterized and the physiological function of the network should be known and subject to quantitative measurement. Probes of the internal dynamics of the network such as fluorescence resonance energy transfer (FRET) or direct imaging of dynamic spatial structure, will be critical in developing and testing quantitative models. It will also be important to choose networks which complement each other well, spanning a broad range of architectures and functions. A preliminary list includes (i) quorum sensing, in which the cell slowly integrates signals from its neighbors to commit to a developmental decision such as invasion of a host, (ii) chemotaxis, which requires adaptation and rapid response to changing chemical concentrations, (iii) cell-division networks, where accuracy and checkpoints are essential, and (iv) metabolic networks which tie together diverse inputs to maintain homeostasis.
Quorum sensing. Bacteria communicate with each other by diffusible chemical signals. These signals allow bacteria to detect their own population density and, at high enough density, to undertake collective activities such a light production or invasion of a host. We collaborate with the group of Professor Bonnie Bassler (our neighbors) to study the signaling pathway in a number of species including the human pathogen Vibrio cholerae. The quorum-sensing pathway in Vibrios has many features, including coincidence detection and signal averaging, designed to assure robust, high-fidelity signal transduction despite fluctuations both in the environment and in internal protein concentrations. Quorum sensing is a model for developmental decisions, in which multiple signals are integrated over time, culminating in commitment to a particular cell fate.
Chemotaxis. Chemotaxis networks in bacteria allow cells to swim toward attractants such as amino acids or sugars, and away from repellents. In the well-studied case of E. coli, cells perform chemotaxis by detecting temporal changes in their chemical environment and transducing this information into a decision to swim straight or change direction (tumble). The chemotaxis system is remarkable for its high sensitivity to small relative changes in chemical concentrations, over a range of up to five orders of magnitude of concentration. The range of sensitivity depends on an adaptation system in which receptors are actively methylated and demethylated at specific modification sites. Adaptation in chemotaxis is both precise, i.e. cells return precisely to the same rate of tumbles, and robust with respect to the stimulus strength and to variations in the levels of chemotaxis proteins. Motivated by the in vivo FRET studies of receptor activity done by Howard Berg and Victor Sourjik at Harvard, we have developed a model for chemotaxis signaling based on mixed clusters of chemotaxis receptors. We continue to collaborate with the experimental group of Professor Victor Sourjik (now at Heidelberg) to understand the remarkable signaling properties of the chemotaxis network.
Cell-division networks. Cell division requires the proper spatial and temporal organization of numerous division proteins. In the bacteria E. coli and B. subtilis, division into equal daughter cells is measured to be accurate to within a few percent. How do these cells recognize their own shapes and engineer accurate division? We study a number of systems involved in cell division in bacteria, including the Min system that helps position the division apparatus at midcell. The Min proteins in E. coli form a spatial oscillator based on a Turing instability. Our modeling results indicate that the Min oscillations can spontaneously orient along the long axis of cells, even in nearly round cells (cocci). We continue to study the Min system, and other systems involved in protein targeting. In addition, we are collaborating with the group of Professor Zemer Gitai (our other neighbors) on cell division in Caulobacter crescentus. In this species, the two daughter cells have distinct cell fates, a sessile stalked cell and a motile swarmer cell, and cell division is reliably unequal, favoring the stalked cell. The asymmetric cell division in Caulobacter is likely to be a source of insight into the mechanisms of spatial organization at work in bacteria.
Metabolism. Metabolism is the central network of all organisms. While the pathways and enzymes of metabolism have been well studied, there are many open questions about how cells respond dynamically to changes in their nutrient environment. We focus on nitrogen metabolism in bacteria as a tractable sub-system for modeling. The metabolic pathways for nitrogen utilization are relatively simple and the regulatory system, while complex, has been well studied. We believe that the nitrogen system contains the essential features, e.g. failsafe regulatory architecture, sophisticated dynamic control, critical for a general understanding of metabolism. Our modeling studies are complemented by experimental work being done at Princeton in the groups of Professors Josh Rabinowitz (mass spectroscopy) and David Botstein (microarray studies of gene expression).
- Rachael Barry, Anne-Florence Bitbol, Alexander Lorestani, Emeric J. Charles, Chris H Habrian, Jesse m Hansen, Hsing-Jung LI, Enoch P Baldwin, Ned S Wingreen, Justin M Kollman, Zemer Gitai. (2014) Large-scale filament formation inhibits the activity of CTP synthetase. Elife. 2014; published ahead of print July 16, 2014 doi: 10.7554/eLife.03638 Pubmed
- Mikhail Tikhonov, Robert W Leach and Ned S Wingreen. (2014) Interpreting 16S metagenomic data without clustering to achieve sub-OTU resotution. ISME J. 2014; published ahead of print July 11, 2014 doi:10.1038/ismej.2014.117 Pubmed
- Bassler BL, Wingreen NS. (2014) Working together at the interface of physics and biology. Phys Biol. 11: 053010. Pubmed
- Castellana M, Wilson MZ, Xu Y,...Gitai Z, Wingreen NS. (2014) Enzyme clustering accelerates processing of intermediates through metabolic channeling. Nat Biotechnol. 32: 1011-8. Pubmed
- Tikhonov M, Leach RW, Wingreen NS. (2014) Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution. ISME J. Jul 11. [Epub ahead of print]
- Broedersz CP, Wang X, Meir Y, Loparo JJ, Rudner DZ, Wingreen NS. (2014) Condensation and localization of the partitioning protein ParB on the bacterial chromosome. Proc Natl Acad Sci. 111: 8809-14. Pubmed
- Neumann S, Vladimirov N, Krembel AK, Wingreen NS, Sourjik V. (2014) Imprecision of adaptation in Escherichia coli chemotaxis. PLoS One. 9: e84904. Pubmed
- Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. (2013) Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 24: 50-55. Pubmed
- Dwyer RS, Ricci DP, Colwell LJ, Silhavy TJ, Wingreen NS. (2013) Predicting functionally informative mutations in Escherichia coli BamA using evolutionary covariance analysis. Genetics. 195: 443-55. Pubmed
- Borenstein DB, Meir Y, Shaevitz JW, Wingreen NS. (2013) Non-local interaction via diffusible resource prevents coexistence of cooperators and cheaters in a lattice model. PLoS One. 8: e63304. Pubmed
- Wang S, Wingreen NS. (2013) Cell shape can mediate the spatial organization of the bacterial cytoskeleton. Biophys J. 104: 541-52. PubMed
- Cooper RM, Wingreen NS, Cox EC. (2012) An excitable cortex and memory model successfully predicts new pseudopod dynamics. PLoS One. 7: e33528. PubMed
- Wang Y, Tu KC, Ong NP, Bassler BL, Wingreen NS. (2011) Protein-level fluctuation correlation at the microcolony level and its application to the Vibrio harveyi quorum-sensing circuit. Biophys J. 100: 3045-53. PubMed
- Furchtgott L, Wingreen NS, Huang KC. (2011) Mechanisms for maintaining cell shape in rod-shaped Gram-negative bacteria. Mol Microbiol. 81: 340-53. PubMed
- Wyart M, Botstein D, Wingreen NS. (2010) Evaluating gene expression dynamics using pairwise RNA FISH data. PLoS Comput Biol. 6: e1000979. PubMed
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