Information Processing by Simple Molecular Motifs and Susceptibility to Noise
暂无分享,去创建一个
[1] Peter S. Swain,et al. The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks , 2013, PLoS Comput. Biol..
[2] Gregory B. Gloor,et al. Mutual information is critically dependent on prior assumptions: would the correct estimate of mutual information please identify itself? , 2010, Bioinform..
[3] ICHAEL,et al. Information Processing by Simple Molecular Motifs and Susceptibility to Noise , 2015, bioRxiv.
[4] N. Friedman,et al. Stochastic protein expression in individual cells at the single molecule level , 2006, Nature.
[5] Bill Ravens,et al. An Introduction to Copulas , 2000, Technometrics.
[6] Pablo A Iglesias,et al. A framework for designing and analyzing binary decision-making strategies in cellular systems. , 2012, Integrative biology : quantitative biosciences from nano to macro.
[7] Andre Levchenko,et al. The application of information theory to biochemical signaling systems , 2012, Physical biology.
[8] Pablo A. Iglesias,et al. An Information-Theoretic Characterization of the Optimal Gradient Sensing Response of Cells , 2007, PLoS Comput. Biol..
[9] Michael P H Stumpf,et al. Sensitivity, robustness, and identifiability in stochastic chemical kinetics models , 2011, Proceedings of the National Academy of Sciences.
[10] N. Wingreen,et al. Maximum likelihood and the single receptor. , 2009, Physical review letters.
[11] I. Nemenman,et al. Information Transduction Capacity of Noisy Biochemical Signaling Networks , 2011, Science.
[12] Jeffrey W. Smith,et al. Stochastic Gene Expression in a Single Cell , .
[13] J. Elf,et al. Stochastic reaction-diffusion kinetics in the microscopic limit , 2010, Proceedings of the National Academy of Sciences.
[14] 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.
[15] Clive G. Bowsher,et al. Environmental sensing, information transfer, and cellular decision-making. , 2014, Current opinion in biotechnology.
[16] D. Gillespie,et al. Linear noise approximation is valid over limited times for any chemical system that is sufficiently large. , 2012, IET systems biology.
[17] J. Stark,et al. Network motifs: structure does not determine function , 2006, BMC Genomics.
[18] Michael P H Stumpf,et al. Decomposing noise in biochemical signaling systems highlights the role of protein degradation. , 2011, Biophysical journal.
[19] Zhang Zhiyi,et al. A mutual information estimator with exponentially decaying bias. , 2015 .
[20] Carsten O. Daub,et al. The mutual information: Detecting and evaluating dependencies between variables , 2002, ECCB.
[21] John C Doyle,et al. Architecture, constraints, and behavior , 2011, Proceedings of the National Academy of Sciences.
[22] Nir Friedman,et al. Linking stochastic dynamics to population distribution: an analytical framework of gene expression. , 2006, Physical review letters.
[23] P. J. Green,et al. Density Estimation for Statistics and Data Analysis , 1987 .
[24] P. Swain,et al. Intrinsic and extrinsic contributions to stochasticity in gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[25] I. Nemenman,et al. Optimal Signal Processing in Small Stochastic Biochemical Networks , 2006, PloS one.
[26] P. Kloeden,et al. Numerical Solution of Stochastic Differential Equations , 1992 .
[27] W. Bialek,et al. Information flow and optimization in transcriptional regulation , 2007, Proceedings of the National Academy of Sciences.
[28] 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.
[29] U. Alon. Network motifs: theory and experimental approaches , 2007, Nature Reviews Genetics.
[30] Lorenz Wernisch,et al. Statistical model comparison applied to common network motifs , 2010, BMC Systems Biology.
[31] Filipe Tostevin,et al. Reliability of frequency and amplitude decoding in gene regulation. , 2012, Physical review letters.
[32] Sarah Filippi,et al. Information theory and signal transduction systems: from molecular information processing to network inference. , 2014, Seminars in cell & developmental biology.
[33] David A. Rand,et al. Bayesian inference of biochemical kinetic parameters using the linear noise approximation , 2009, BMC Bioinformatics.
[34] Mph Stumpf,et al. Protein-protein interactions: from global to local analyses. , 2008, Current opinion in biotechnology.
[35] Katherine C. Chen,et al. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. , 2003, Current opinion in cell biology.
[36] L. Tsimring,et al. Accurate information transmission through dynamic biochemical signaling networks , 2014, Science.
[37] Michael P. H. Stumpf,et al. Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data , 2008, PLoS Comput. Biol..
[38] Gasper Tkacik,et al. Information capacity of genetic regulatory elements. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[39] P. Bork,et al. Linear Motif Atlas for Phosphorylation-Dependent Signaling , 2008, Science Signaling.
[40] F. Tostevin,et al. Mutual information between input and output trajectories of biochemical networks. , 2009, Physical review letters.
[41] S. Mangan,et al. Structure and function of the feed-forward loop network motif , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[42] Bruce Tidor,et al. Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology , 2013, PLoS Comput. Biol..
[43] D. Talay. Numerical solution of stochastic differential equations , 1994 .
[44] Shinya Kuroda,et al. Robustness and Compensation of Information Transmission of Signaling Pathways , 2013, Science.
[45] P. Rapp,et al. Statistical validation of mutual information calculations: comparison of alternative numerical algorithms. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[46] Michael P. H. Stumpf,et al. StochSens - matlab package for sensitivity analysis of stochastic chemical systems , 2012, Bioinform..
[47] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Aleksandra M Walczak,et al. Information transmission in genetic regulatory networks: a review , 2011, Journal of physics. Condensed matter : an Institute of Physics journal.
[49] Thomas M. Cover,et al. Elements of Information Theory , 2005 .