Optimal Census by Quorum Sensing

Quorum sensing is the regulation of gene expression in response to changes in cell density. To measure their cell density, bacterial populations produce and detect diffusible molecules called autoinducers. Individual bacteria internally represent the external concentration of autoinducers via the level of monitor proteins. In turn, these monitor proteins typically regulate both their own production and the production of autoinducers, thereby establishing internal and external feedbacks. Here, we ask whether feedbacks can increase the information available to cells about their local density. We quantify available information as the mutual information between the abundance of a monitor protein and the local cell density for biologically relevant models of quorum sensing. Using variational methods, we demonstrate that feedbacks can increase information transmission, allowing bacteria to resolve up to two additional ranges of cell density when compared with bistable quorum-sensing systems. Our analysis is relevant to multi-agent systems that track an external driver implicitly via an endogenously generated signal.

[1]  B. Bassler,et al.  Cutting through the complexity of cell collectives , 2013, Proceedings of the Royal Society B: Biological Sciences.

[2]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Kirsten Jung,et al.  Heterogeneity in quorum sensing‐regulated bioluminescence of Vibrio harveyi , 2009, Molecular microbiology.

[4]  N. Wingreen,et al.  The Small RNA Chaperone Hfq and Multiple Small RNAs Control Quorum Sensing in Vibrio harveyi and Vibrio cholerae , 2004, Cell.

[5]  S. Leibler,et al.  Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments , 2005, Science.

[6]  M. Berger,et al.  The Vibrio harveyi master quorum-sensing regulator, LuxR, a TetR-type protein is both an activator and a repressor: DNA recognition and binding specificity at target promoters , 2008, Molecular microbiology.

[7]  Karina B. Xavier,et al.  The Multiple Signaling Systems Regulating Virulence in Pseudomonas aeruginosa , 2012, Microbiology and Molecular Reviews.

[8]  E. Ruby,et al.  Lessons from a cooperative, bacterial-animal association: the Vibrio fischeri-Euprymna scolopes light organ symbiosis. , 1996, Annual review of microbiology.

[9]  K. Vahala.  Handbook of stochastic methods for physics, chemistry and the natural sciences , 1986, IEEE Journal of Quantum Electronics.

[10]  Ned S Wingreen,et al.  Quantifying the Integration of Quorum-Sensing Signals with Single-Cell Resolution , 2009, PLoS biology.

[11]  E. Levine,et al.  Regulating the many to benefit the few: role of weak small RNA targets. , 2013, Biophysical journal.

[12]  S. Campagna,et al.  Direct quantitation of the quorum sensing signal, autoinducer-2, in clinically relevant samples by liquid chromatography-tandem mass spectrometry. , 2009, Analytical chemistry.

[13]  E. Greenberg,et al.  Census and consensus in bacterial ecosystems: the LuxR-LuxI family of quorum-sensing transcriptional regulators. , 1996, Annual review of microbiology.

[14]  Wendell A. Lim,et al.  Secreting and Sensing the Same Molecule Allows Cells to Achieve Versatile Social Behaviors , 2014, Science.

[15]  Stanislas Leibler,et al.  The Value of Information for Populations in Varying Environments , 2010, ArXiv.

[16]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[17]  B. Bassler,et al.  Quorum Sensing Regulates Type III Secretion in Vibrio harveyi and Vibrio parahaemolyticus , 2004, Journal of bacteriology.

[18]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells , 2010, Science.

[19]  Xiongzhi Chen.  Brownian Motion and Stochastic Calculus , 2008 .

[20]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[21]  B. Bassler,et al.  Bacterial quorum-sensing network architectures. , 2009, Annual review of genetics.

[22]  W. Bialek,et al.  Optimizing information flow in small genetic networks. II. Feed-forward interactions. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  I. Nemenman,et al.  Information Transduction Capacity of Noisy Biochemical Signaling Networks , 2011, Science.

[24]  J. Bluestone,et al.  Type 1 diabetes : a subject collection from Cold Spring Harbor Perspectives in Medicine , 2012 .

[25]  O. Woolpert.  Biological Sciences , 1980, Nature.

[26]  W. Bialek,et al.  Information flow and optimization in transcriptional regulation , 2007, Proceedings of the National Academy of Sciences.

[27]  Xiaohua Zhang,et al.  Vibrio harveyi: a significant pathogen of marine vertebrates and invertebrates , 2006, Letters in applied microbiology.

[28]  B. Bassler,et al.  Quorum sensing in bacteria. , 2001, Annual review of microbiology.

[29]  S. Laughlin.  A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.

[30]  S. Cohen,et al.  Regulation of tissue growth through nutrient sensing. , 2009, Annual review of genetics.

[31]  Martin Fussenegger,et al.  Hysteresis in a synthetic mammalian gene network. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[32]  T. Defoirdt,et al.  Quorum sensing positively regulates flagellar motility in pathogenic Vibrio harveyi. , 2015, Environmental microbiology.

[33]  Ned S Wingreen,et al.  Information processing and signal integration in bacterial quorum sensing , 2009, Molecular systems biology.

[34]  W. Bialek,et al.  Optimizing information flow in small genetic networks. III. A self-interacting gene. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[36]  N. Wingreen,et al.  A quantitative comparison of sRNA-based and protein-based gene regulation , 2008, Molecular systems biology.

[37]  Eduardo Sontag,et al.  Untangling the wires: A strategy to trace functional interactions in signaling and gene networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[38]  A. van Oudenaarden,et al.  Using Gene Expression Noise to Understand Gene Regulation , 2012, Science.

[39]  Gasper Tkacik,et al.  Optimizing information flow in small genetic networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  R. Yu,et al.  Fus3 generates negative feedback that improves information transmission in yeast pheromone response , 2008, Nature.

[41]  Ned S Wingreen,et al.  Active regulation of receptor ratios controls integration of quorum-sensing signals in Vibrio harveyi , 2011, Molecular systems biology.

[42]  J. Paulsson.  Summing up the noise in gene networks , 2004, Nature.

[43]  Lingchong You,et al.  Optimal tuning of bacterial sensing potential , 2009, Molecular systems biology.

[44]  Omar P. Tabbaa,et al.  Mutual information and the fidelity of response of gene regulatory models , 2014, Physical biology.

[45]  Bonnie L Bassler,et al.  Bacterial quorum sensing: its role in virulence and possibilities for its control. , 2012, Cold Spring Harbor perspectives in medicine.

[46]  Ned S Wingreen,et al.  Measurement of the copy number of the master quorum-sensing regulator of a bacterial cell. , 2010, Biophysical journal.