Detecting Concentration Changes with Cooperative Receptors

Cells constantly need to monitor the state of the environment to detect changes and timely respond. The detection of concentration changes of a ligand by a set of receptors can be cast as a problem of hypothesis testing, and the cell viewed as a Neyman–Pearson detector. Within this framework, we investigate the role of receptor cooperativity in improving the cell’s ability to detect changes. We find that cooperativity decreases the probability of missing an occurred change. This becomes especially beneficial when difficult detections have to be made. Concerning the influence of cooperativity on how fast a desired detection power is achieved, we find in general that there is an optimal value at finite levels of cooperation, even though easy discrimination tasks can be performed more rapidly by noncooperative receptors.

[1]  Yuhai Tu,et al.  The nonequilibrium mechanism for ultrasensitivity in a biological switch: Sensing by Maxwell's demons , 2008, Proceedings of the National Academy of Sciences.

[2]  Clive G. Bowsher,et al.  Identifying sources of variation and the flow of information in biochemical networks , 2012, Proceedings of the National Academy of Sciences.

[3]  P. R. ten Wolde,et al.  Signal detection, modularity, and the correlation between extrinsic and intrinsic noise in biochemical networks. , 2005, Physical review letters.

[4]  Yuhai Tu,et al.  The energy-speed-accuracy tradeoff in sensory adaptation , 2012, Nature Physics.

[5]  Aleksandra M. Walczak,et al.  Trade-Offs in Delayed Information Transmission in Biochemical Networks , 2015, 1504.03637.

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

[7]  Ned S Wingreen,et al.  Dynamics of cooperativity in chemical sensing among cell-surface receptors. , 2011, Physical review letters.

[8]  Udo Seifert,et al.  Thermodynamic uncertainty relation for biomolecular processes. , 2015, Physical review letters.

[9]  A. C. Barato,et al.  Information-theoretic vs. thermodynamic entropy production in autonomous sensory networks , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  W. Bialek,et al.  Cooperativity, sensitivity, and noise in biochemical signaling. , 2005, Physical review letters.

[11]  W. Bialek.  Biophysics: Searching for Principles , 2012 .

[12]  N. Wingreen,et al.  Maximum likelihood and the single receptor. , 2009, Physical review letters.

[13]  Stuart A Sevier,et al.  Properties of cooperatively induced phases in sensing models. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Massimiliano Esposito,et al.  Mutual entropy production in bipartite systems , 2013, 1307.4728.

[15]  Eric D. Siggia,et al.  Decisions on the fly in cellular sensory systems , 2013, Proceedings of the National Academy of Sciences.

[16]  M Marsili,et al.  Time-dependent information transmission in a model regulatory circuit. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  F. Tostevin,et al.  Spatial partitioning improves the reliability of biochemical signaling , 2013, Proceedings of the National Academy of Sciences.

[18]  Pieter Rein ten Wolde,et al.  Thermodynamics of Computational Copying in Biochemical Systems , 2015, 1503.00909.

[19]  M. Grabe,et al.  Cooperativity Can Enhance Cellular Signal Detection , 2014, 1401.3262.

[20]  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.

[21]  Robert G. Endres,et al.  Memory improves precision of cell sensing in fluctuating environments , 2014, Scientific Reports.

[22]  T. Mora,et al.  Limits of sensing temporal concentration changes by single cells. , 2010, Physical review letters.

[23]  Sarah Marzen,et al.  Statistical mechanics of Monod-Wyman-Changeux (MWC) models. , 2013, Journal of molecular biology.

[24]  Kinetic versus energetic discrimination in biological copying. , 2012, Physical review letters.

[25]  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.

[26]  F. Tostevin,et al.  Mutual information between input and output trajectories of biochemical networks. , 2009, Physical review letters.

[27]  David J Schwab,et al.  Energetic costs of cellular computation , 2012, Proceedings of the National Academy of Sciences.

[28]  P. R. ten Wolde,et al.  Fundamental limits on sensing chemical concentrations with linear biochemical networks. , 2012, Physical review letters.

[29]  H. Berg,et al.  Physics of chemoreception. , 1977, Biophysical journal.

[30]  Thierry Mora,et al.  Thermodynamics of statistical inference by cells. , 2014, Physical review letters.

[31]  F. Tostevin,et al.  The Berg-Purcell limit revisited. , 2014, Biophysical journal.

[32]  U. Alon,et al.  Optimality and evolutionary tuning of the expression level of a protein , 2005, Nature.

[33]  W. Bialek,et al.  Physical limits to biochemical signaling. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Andre C. Barato,et al.  Nonequilibrium sensing and its analogy to kinetic proofreading , 2015, 1502.02594.

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

[36]  Gasper Tkacik,et al.  Positional information, in bits , 2010, Proceedings of the National Academy of Sciences.

[37]  Monica L. Skoge,et al.  Chemical sensing by nonequilibrium cooperative receptors. , 2013, Physical review letters.

[38]  L. Tsimring,et al.  Accurate information transmission through dynamic biochemical signaling networks , 2014, Science.

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

[40]  Andre C. Barato,et al.  Efficiency of cellular information processing , 2014, 1405.7241.

[41]  Clive G. Bowsher,et al.  Environmental sensing, information transfer, and cellular decision-making. , 2014, Current opinion in biotechnology.

[42]  Hong Qian,et al.  Reducing intrinsic biochemical noise in cells and its thermodynamic limit. , 2006, Journal of molecular biology.

[43]  Pieter Rein ten Wolde,et al.  Energy dissipation and noise correlations in biochemical sensing. , 2014, Physical review letters.

[44]  W. Rappel,et al.  Physical limits on cellular sensing of spatial gradients. , 2010, Physical review letters.

[45]  Shinya Kuroda,et al.  Robustness and Compensation of Information Transmission of Signaling Pathways , 2013, Science.

[46]  I. Nemenman,et al.  Cellular noise and information transmission. , 2014, Current opinion in biotechnology.

[47]  Clive G. Bowsher,et al.  Information transfer by leaky, heterogeneous, protein kinase signaling systems , 2014, Proceedings of the National Academy of Sciences.

[48]  Jordan M. Horowitz,et al.  Thermodynamic Costs of Information Processing in Sensory Adaptation , 2014, PLoS Comput. Biol..

[49]  Stanislas Leibler,et al.  Speed, dissipation, and error in kinetic proofreading , 2012, Proceedings of the National Academy of Sciences.

[50]  I. Nemenman,et al.  Optimal Signal Processing in Small Stochastic Biochemical Networks , 2006, PloS one.

[51]  Gašper Tkačik,et al.  Noise and information transmission in promoters with multiple internal States. , 2013, Biophysical journal.

[52]  Aleksandra M Walczak,et al.  Information transmission in genetic regulatory networks: a review , 2011, Journal of physics. Condensed matter : an Institute of Physics journal.

[53]  Joël Janin,et al.  Physical biology of the cell, Second Edition , 2013 .

[54]  Hong Qian,et al.  Nonequilibrium thermodynamics and nonlinear kinetics in a cellular signaling switch. , 2005, Physical review letters.

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

[56]  Peter S. Swain,et al.  Trade-Offs and Constraints in Allosteric Sensing , 2011, PLoS Comput. Biol..

[57]  Tetsuya J Kobayashi,et al.  Implementation of dynamic Bayesian decision making by intracellular kinetics. , 2010, Physical review letters.

[58]  Pieter Rein Ten Wolde,et al.  Optimal resource allocation in cellular sensing systems , 2014, Proceedings of the National Academy of Sciences.

[59]  W. Rappel,et al.  Receptor noise and directional sensing in eukaryotic chemotaxis. , 2008, Physical review letters.

[60]  T. L. Hill.  Cooperativity Theory in Biochemistry: Steady-State and Equilibrium Systems , 2011 .

[61]  Wolfgang Maass,et al.  Searching for principles of brain computation , 2016, Current Opinion in Behavioral Sciences.

[62]  Gerardo Aquino,et al.  Optimal receptor-cluster size determined by intrinsic and extrinsic noise. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[63]  T. Ouldridge,et al.  On the Connection between Computational and Biochemical Measurement , 2014 .