Implementation of dynamic Bayesian decision making by intracellular kinetics.
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[1] W. Bialek,et al. Information flow and optimization in transcriptional regulation , 2007, Proceedings of the National Academy of Sciences.
[2] M. Ueda,et al. Stochastic signal processing and transduction in chemotactic response of eukaryotic cells. , 2007, Biophysical journal.
[3] L. Segel,et al. Th1 or Th2: How an appropriate T helper response can be made , 2001, Bulletin of mathematical biology.
[4] V. Shahrezaei,et al. The stochastic nature of biochemical networks. , 2008, Current opinion in biotechnology.
[5] D. Koshland,et al. An amplified sensitivity arising from covalent modification in biological systems. , 1981, Proceedings of the National Academy of Sciences of the United States of America.
[6] Claude Desplan,et al. Stochasticity and Cell Fate , 2008, Science.
[7] T. Helikar,et al. Emergent decision-making in biological signal transduction networks , 2008, Proceedings of the National Academy of Sciences.
[8] Lingchong You,et al. Optimal tuning of bacterial sensing potential , 2009, Molecular systems biology.
[9] W. Rappel,et al. Receptor noise and directional sensing in eukaryotic chemotaxis. , 2008, Physical review letters.
[10] W. Rappel,et al. Receptor noise limitations on chemotactic sensing , 2008, Proceedings of the National Academy of Sciences.
[11] P. Swain,et al. Noisy information processing through transcriptional regulation , 2007, Proceedings of the National Academy of Sciences.
[12] Shin Ishii,et al. Stochastic control of spontaneous signal generation for gradient sensing in chemotaxis. , 2008, Journal of theoretical biology.
[13] Janet Rossant,et al. Interaction between Oct3/4 and Cdx2 Determines Trophectoderm Differentiation , 2005, Cell.
[14] P. Dayan,et al. A Bayesian model predicts the response of axons to molecular gradients , 2009, Proceedings of the National Academy of Sciences.
[15] Mads Kærn,et al. Noise in eukaryotic gene expression , 2003, Nature.
[16] Sophie Denève,et al. Bayesian Spiking Neurons I: Inference , 2008, Neural Computation.
[17] W. Bialek,et al. Probing the Limits to Positional Information , 2007, Cell.
[18] F. Tostevin,et al. Mutual information between input and output trajectories of biochemical networks. , 2009, Physical review letters.
[19] Pablo A. Iglesias,et al. Optimal Noise Filtering in the Chemotactic Response of Escherichia coli , 2006, PLoS Comput. Biol..
[20] Avi Ma’ayan,et al. Systems biology of stem cell fate and cellular reprogramming , 2009, Nature Reviews Molecular Cell Biology.
[21] Farren J. Isaacs,et al. Phenotypic consequences of promoter-mediated transcriptional noise. , 2006, Molecular cell.
[22] K. Fujimoto,et al. Noisy signal amplification in ultrasensitive signal transduction. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[23] P. Swain,et al. Strategies for cellular decision-making , 2009, Molecular systems biology.
[24] Z. Cheng,et al. Cell fate decision mediated by p53 pulses , 2009, Proceedings of the National Academy of Sciences.
[25] Y. Iwasa,et al. Optimal placement of multiple morphogen sources. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[26] J. Paulsson. Summing up the noise in gene networks , 2004, Nature.
[27] Jonathan A. Cooper,et al. Potential positive and negative autoregulation of p60c-src by intermolecular autophosphorylation. , 1988, Proceedings of the National Academy of Sciences of the United States of America.
[28] Pablo A. Iglesias,et al. An Information-Theoretic Characterization of the Optimal Gradient Sensing Response of Cells , 2007, PLoS Comput. Biol..
[29] Mads Kærn,et al. A chance at survival: gene expression noise and phenotypic diversification strategies , 2009, Molecular microbiology.