Cellular noise and information transmission

Current Opinion in Biotechnology

Volumn 28, Issue , 2014, Pages 156-164

  Levchenko, Andre ^a   Nemenman, Ilya ^b  

^a Yale University   (United States)

^b EMORY UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOTECHNOLOGY;

BIOLOGICAL RESEARCH; CELL-TO-CELL VARIABILITY; CELLULAR BEHAVIORS; ENVIRONMENTAL SIGNALS; INFORMATION TRANSMISSION; MATHEMATICAL LANGUAGES; MOLECULAR FLUORESCENT LABELS; TECHNOLOGICAL REVOLUTION;

INFORMATION THEORY;

CELL ACTIVATION; CELL DEATH; CELL DIVISION; CELL FUNCTION; CELL HETEROGENEITY; CELL MIGRATION; CELL POPULATION; CELL PROLIFERATION; CELLULAR DISTRIBUTION; CELLULAR NOISE; CHEMICAL REACTION; DOSE RESPONSE; INFORMATION PROCESSING; NOISE; PHENOTYPE; PRIORITY JOURNAL; QUANTUM MECHANICS; REVIEW; SIGNAL TRANSDUCTION;

ALGORITHMS; BACTERIA; MODELS, BIOLOGICAL; SINGLE-CELL ANALYSIS; SYSTEMS BIOLOGY;

EID: 84902113310     PISSN: 09581669     EISSN: 18790429     Source Type: Journal    
DOI: 10.1016/j.copbio.2014.05.002     Document Type: Review

References (80)

1. Balazsi G., van Oudenaarden A., Collins J.J. Cellular decision making and biological noise: from microbes to mammals. Cell 2011, 144:910-925.
2. Zhang J., et al. Creating new fluorescent probes for cell biology. Nat Rev Mol Cell Biol 2002, 3:906-918.
3. Thattai M., van Oudenaarden A. Intrinsic noise in gene regulatory networks. Proc Natl Acad Sci U S A 2001, 98:8614-8619.
4. Elowitz M.B., et al. Stochastic gene expression in a single cell. Science 2002, 297:1183-1186.
5. Raj A., et al. Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 2008, 5:877-879.
6. Raj A., van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 2008, 135:216-226.
7. Ozbudak E.M., et al. Multistability in the lactose utilization network of Escherichia coli. Nature 2004, 427:737-740.
8. Rao C.V., Wolf D.M., Arkin A.P. Control, exploitation and tolerance of intracellular noise. Nature 2002, 420:231-237.
9. van Kampen N.G. Stochastic Processes in Physics and Chemistry 2007, North-Holland Personal Library, Amsterdam, Boston: Elsevier. xvi, 463 p. edn 3.
10. Gillespie D. Exact stochastic simulation of coupled chemical reactions. J Phys Chem 1977, 81:2340.
11. Gillespie D.T. Stochastic simulation of chemical kinetics. Annu Rev Phys Chem 2007, 58:35-55.
12. Gibson M., Bruck J. Efficient exact stochastic simulation of chemical systems with many species and many channels. J Phys Chem A 2000, 104:1876.
13. Cao Y., Gillespie D., Petzold L. The slow-scale stochasitc simulation algorithm. J Chem Phys 2005, 122:014116.
14. Sinitsyn N.A., Hengartner N., Nemenman I. Adiabatic coarse-graining and simulations of stochastic biochemical networks. Proc Natl Acad Sci U S A 2009, 106:10546-10551.
15. Faeder J.R., Blinov M.L., Hlavacek W.S. Rule-based modeling of biochemical systems with BioNetGen. Methods Mol Biol 2009, 500:113-167.
16. Paulsson J. Summing up the noise in gene networks. Nature 2004, 427:415-418.
17. Walczak A.M., Mugler A., Wiggins C.H. A stochastic spectral analysis of transcriptional regulatory cascades. Proc Natl Acad Sci U S A 2009, 106:6529-6534.
18. Walczak A.M., Mugler A., Wiggins C.H. Analytic methods for modeling stochastic regulatory networks. Methods Mol Biol 2012, 880:273-322.
19. Munsky B., Khammash M. The finite state projection algorithm for the solution of the chemical master equation. J Chem Phys 2006, 124:044104.
20. Andrews S.S., Bray D. Stochastic simulation of chemical reactions with spatial resolution and single molecule detail. Phys Biol 2004, 1:137-151.
21. de Ronde W., et al. Statistical properties of multistep enzyme-mediated reactions. IET Syst Biol 2009, 3:429.
22. Bel G., Munsky B., Nemenman I. The simplicity of completion time distributions for common complex biochemical processes. Phys Biol 2010, 7:016003.
23. Sneddon M.W., Faeder J.R., Emonet T. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods 2011, 8:177-183.
24. Machta B.B., et al. Parameter space compression underlies emergent theories and predictive models. Science 2013, 342:604-607.
25. Swain P.S., Elowitz M.B., Siggia E.D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci U S A 2002, 99:12795-12800.
26. Raser J.M., O'Shea E.K. Control of stochasticity in eukaryotic gene expression. Science 2004, 304:1811-1814.
27. Ozbudak E.M., et al. Regulation of noise in the expression of a single gene. Nat Genet 2002, 31:69-73.
28. Pedraza J.M., van Oudenaarden A. Noise propagation in gene networks. Science 2005, 307:1965-1969.
29. Suel G.M., et al. An excitable gene regulatory circuit induces transient cellular differentiation. Nature 2006, 440:545-550.
30. Paliwal S., et al. MAPK-mediated bimodal gene expression and adaptive gradient sensing in yeast. Nature 2007, 446:46-51.
31. Cagatay T., et al. Architecture-dependent noise discriminates functionally analogous differentiation circuits. Cell 2009, 139:512-522.
32. Ferrell J.E., Machleder E.M. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 1998, 280:895-898.
33. Mettetal J.T., et al. Predicting stochastic gene expression dynamics in single cells. Proc Natl Acad Sci U S A 2006, 103:7304-7309.
34. Munsky B., Neuert G., van Oudenaarden A. Using gene expression noise to understand gene regulation. Science 2012, 336:183-187.
35. Neuert G., et al. Systematic identification of signal-activated stochastic gene regulation. Science 2013, 339:584-587.
36. Kaern M., et al. Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 2005, 6:451-464.
37. Eldar A., Elowitz M.B. Functional roles for noise in genetic circuits. Nature 2010, 467:167-173.
38. Balaban N.Q., et al. Bacterial persistence as a phenotypic switch. Science 2004, 305:1622-1625.
39. Deris J.B., et al. The innate growth bistability and fitness landscapes of antibiotic-resistant bacteria. Science 2013, 342:1237435.
40. Gallistel C.R., Fairhurst S., Balsam P. The learning curve: implications of a quantitative analysis. Proc Natl Acad Sci U S A 2004, 101:13124-13131.
41. Kussell E., et al. Bacterial persistence: a model of survival in changing environments. Genetics 2005, 169:1807-1814.
42. Kussell E., Leibler S. Phenotypic diversity, population growth, and information in fluctuating environments. Science 2005, 309:2075-2078.
43. Lewis K. Persister cells. Annu Rev Microbiol 2010, 64:357-372.
44. Johnson P.J., Levin B.R. Pharmacodynamics, population dynamics, and the evolution of persistence in Staphylococcus aureus. PLoS Genet 2013, 9:e1003123.
45. Dean M., Fojo T., Bates S. Tumour stem cells and drug resistance. Nat Rev Cancer 2005, 5:275-284.
46. Shannon C.E., Weaver W. The Mathematical Theory of Communication 1949, University of Illinois Press, Urbana.
47. Cover T.M., Thomas J.A. Elements of Information Theory 1991, John Wiley & Sons, New York.
48. Mackay D.J.C. Information Theory, Inference, and Learning Algorithms 2003, Cambridge University Press, Cambridge.
49. El Gamal A.A., Kim Y.-H. Network Information Theory 2011, Cambridge University Press, Cambridge, New York, xxviii, 685 p.
50. Schneidman E., et al. Network information and connected correlations. Phys Rev Lett 2003, 91:238701.
51. Margolin A.A., et al. Multivariate dependence and genetic networks inference. IET Syst Biol 2010, 4:428.
52. Fairhall A., Shea-Brown E., Barreiro A. Information theoretic approaches to understanding circuit function. Curr Opin Neurobiol 2012, 22:653-659.
53. Ziv E., Nemenman I., Wiggins C.H. Optimal signal processing in small stochastic biochemical networks. PLoS ONE 2007, 2:e1077.
54. Tkacik G., Callan C.G., Bialek W. Information flow and optimization in transcriptional regulation. Proc Natl Acad Sci U S A 2008, 105:12265-12270.
55. Tostevin F., ten Wolde P.R. Mutual information between input and output trajectories of biochemical networks. Phys Rev Lett 2009, 102:218101.
56. Cheong R., et al. Information transduction capacity of noisy biochemical signaling networks. Science 2011, 334:354-358.
57. Dubuis J.O., et al. Positional information, in bits. Proc Natl Acad Sci U S A 2013, 110:16301-16308.
58. Mugler A., Tostevin F., ten Wolde P.R. Spatial partitioning improves the reliability of biochemical signaling. Proc Natl Acad Sci U S A 2013, 110:5927-5932.
59. Uda S., et al. Robustness and compensation of information transmission of signaling pathways. Science 2013, 341:558-561.
60. Tkacik G., Walczak A.M. Information transmission in genetic regulatory networks: a review. J Phys Condens Matter 2011, 23:p153102.
61. Nemenman I. Information theory and adaptation. Quantitative Biology: From Molecules to Cellular Systems 2012, CRC Press. M. Wall (Ed.).
62. Strong S.P., et al. Entropy and information in neural spike trains. Phys Rev Lett 1998, 80:197.
63. Nemenman I., Shafee F., Bialek W. Entropy and inference, revisited. Advances in Neural Information Processing Systems 2002, vol 14. MIT Press.
64. Paninski L. Estimation of entropy and mutual information. Neur Comp 2003, 15:1191-1253.
65. Kraskov A., Stoegbauer H., Grassberger P. Estimating mutual information. Phys Rev E 2004, 69:066138.
66. Nemenman I., et al. Neural coding of natural stimuli: information at sub-millisecond resolution. PLoS Comput Biol 2008, 4:e1000025.
67. Nemenman I. Coincidences and estimation of entropies of random variables with large cardinalities. Entropy 2011, 13:2013-2023.
68. Slonim N., et al. Information-based clustering. Proc Natl Acad Sci U S A 2005, 102:18297-18302.
69. Grassberger P. Entropy Estimates from Insufficient Samplings 2003, arxiv:physics/0307138.
70. Laughlin S. A simple coding procedure enhances a neuron's information capacity. Z Naturforsch C 1981, 36:910-912.
71. Reinagel P., Laughlin S. Natural stimulus statistics. Network 2001, 12:237-240.
72. Tkacik G., et al. Natural images from the birthplace of the human eye. PLoS ONE 2011, 6:e20409.
73. Tishby N., Pereira F.C., Bialek W. The information bottleneck method. Proceedings of 37th Annual Allerton Conference on Communication, Control and Computing 1999.
74. Sharpee T., Rust N.C., Bialek W. Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput 2004, 16:223-250.
75. Stromberg S.P., Antia R., Nemenman I. Population-expression models of immune response. Phys Biol 2013, 10:035010.
76. Buchler N.E., Gerland U., Hwa T. On schemes of combinatorial transcription logic. Proc Natl Acad Sci U S A 2003, 100:5136-5141.
77. Toettcher J.E., Weiner O.D., Lim W.A. Using optogenetics to interrogate the dynamic control of signal transmission by the Ras/Erk module. Cell 2013, 155:1422-1434.
78. Walczak A.M., Tkacik G., Bialek W. Optimizing information flow in small genetic networks. II. Feed-forward interactions. Phys Rev E Stat Nonlin Soft Matter Phys 2010, 81(Pt 1):041905.
79. Rivoire O., Leibler S. The value of information for populations in varying environments. J Stat Phys 2011, 142:1124-1166.
80. Bergstrom C., Lachmann M. Shannon information and biological fitness. IEEE Information Theory Workshop 2004.