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Information transfer in signaling pathways : a study using coupled simulated and experimental data

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Pahle, Jürgen, Green, Anne K., Dixon, C. Jane and Kummer, U. (Ursula). (2008) Information transfer in signaling pathways : a study using coupled simulated and experimental data. BMC Bioinformatics, Vol.9 (No.13). ISSN 1471-2105

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Official URL: http://dx.doi.org/10.1186/1471-2105-9-139

Abstract

Background: The topology of signaling cascades has been studied in quite some detail. However, how information is processed exactly is still relatively unknown. Since quite diverse information has to be transported by one and the same signaling cascade (e.g. in case of different agonists), it is clear that the underlying mechanism is more complex than a simple binary switch which relies on the mere presence or absence of a particular species. Therefore, finding means to analyze the information transferred will help in deciphering how information is processed exactly in the cell. Using the information-theoretic measure transfer entropy, we studied the properties of information transfer in an example case, namely calcium signaling under different cellular conditions. Transfer entropy is an asymmetric and dynamic measure of the dependence of two (nonlinear) stochastic processes. We used calcium signaling since it is a well-studied example of complex cellular signaling. It has been suggested that specific information is encoded in the amplitude, frequency and waveform of the oscillatory Ca2+-signal. Results: We set up a computational framework to study information transfer, e.g. for calcium signaling at different levels of activation and different particle numbers in the system. We stochastically coupled simulated and experimentally measured calcium signals to simulated target proteins and used kernel density methods to estimate the transfer entropy from these bivariate time series. We found that, most of the time, the transfer entropy increases with increasing particle numbers. In systems with only few particles, faithful information transfer is hampered by random fluctuations. The transfer entropy also seems to be slightly correlated to the complexity (spiking, bursting or irregular oscillations) of the signal. Finally, we discuss a number of peculiarities of our approach in detail. Conclusion: This study presents the first application of transfer entropy to biochemical signaling pathways. We could quantify the information transferred from simulated/experimentally measured calcium signals to a target enzyme under different cellular conditions. Our approach, comprising stochastic coupling and using the information-theoretic measure transfer entropy, could also be a valuable tool for the analysis of other signaling pathways.

Item Type:	Journal Article
Subjects:	Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010) Faculty of Medicine > Warwick Medical School > Education Development and Research Faculty of Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH):	Calcium, Biochemistry, Signaling pathways
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central Ltd.
ISSN:	1471-2105
Date:	4 March 2008
Volume:	Vol.9
Number:	No.13
Identification Number:	10.1186/1471-2105-9-139
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Klaus Tschira Foundation (KTF), Germany. Bundesministerium für Bildung und Forschung (BMBF)
References:	1. Endy D, Brent R: Modelling cellular behavior. Nature 2001, 409:391-395. 2. Érdi P, Tóth J: Mathematical models of chemical reactions – Theory and applications of deterministic and stochastic models Princeton, NJ: Princeton University Press; 1989. 3. Heinrich R, Schuster S: The Regulation of Cellular Systems New York, NY: Chapman and Hall; 1996. 4. Berridge M, Bootman M, Lipp P: Calcium – a life and death signal. Nature 1998, 395:645-648. 5. Carafoli E: Calcium signaling: A tale for all seasons. PNAS 2002, 99(3):1115-1122. 6. Woods N, Cuthbertson K, Cobbold P: Repetitive transient rises in cytoplasmic free calcium in hormone-stimulated hepatocytes. Nature 1986, 319:600-602. 7. Dolmetsch R, Xu K, Lewis R: Calcium oscillations increase the efficiency and specificity of gene expression. Nature 1998, 392:933-936. 8. Falcke M: Deterministic and stochastic models of intracellular Ca2+ waves. New Journal of Physics 2003, 5:96.1-96.28. 9. Keener J, Sneyd J: Mathematical Physiology Springer 2001 chap. Calcium Dynamics. 10. Somogyi R, Stucki J: Hormone-induced Calcium Oscillations in Liver Cells Can Be Explained by a Simple One Pool Model. J Biol Chem 1991, 266(17):11068-11077. 11. Larsen A, Olsen L, Kummer U: On the encoding and decoding of calcium signals in hepatocytes. Biophys Chem 2004, 107:83-99. 12. Schuster S, Marhl M, Höfer T: Modelling of simple and complex calcium oscillations – From single-cell responses to intercellular signalling. Eur J Biochem 2002, 269:1333-1355. 13. Celio M, (Ed): Guidebook to the Calcium-Binding Proteins Oxford: Oxford University Press; 1996. 14. Larsen A, Kummer U: Information Processing in Calcium Signal Transduction. In Understanding Calcium Dynamics Volume 623. Edited by: Falcke M, Malchow D. Berlin: Springer Verlag; 2003:153-178. 15. De Koninck P, Schulman H: Sensitivity of CaM Kinase II to the Frequency of Ca2+ Oscillations. Science 1998, 279:227-230. 16. Li WH, Llopis J, Whitney M, Zlokarnik G, Tsien R: Cell-permeant caged InsP3 ester shows that Ca2+ spike frequency can optimize gene expression. Nature 1998, 392:936-941. 17. Oancea E, Meyer T: Protein Kinase C as a Molecular Machine for Decoding Calcium and Diacylglycerol Signals. Cell 1998, 95(3):307-318. 18. Dupont G, Houart G, Koninck PD: Sensitivity of CaM kinase II to the frequency of Ca2+ oscillations: a simple model. Cell Calcium 2003, 34:485-497. 19. Gall D, Baus E, Dupont G: Activation of the Liver Glycogen Phosphorylase by Ca2+ Oscillations: a Theoretical Study. J Theor Biol 2000, 207:445-454. 20. Salazar C, Politi A, Höfer T: Decoding of calcium oscillations by phosphorylation cycles. Proceedings of Fourth International Workshop on Bioinformatics and Systems Biology 2004:50-51. 21. Marhl M, Perc M, Schuster S: Selective regulation of cellular processes via protein cascades acting as band-pass filters for time-limited oscillations. FEBS Letters 2005, 579:5461-5465. 22. Marhl M, Perc M, Schuster S: A minimal model for decoding of time-limited Ca2+ oscillations. Biophys Chem 2006, 120:161-167. 23. Schuster S, Knoke B, Marhl M: Differential regulation of proteins by bursting calcium oscillations – a theoretical study. BioSystems 2005, 81:49-63. 24. Rozi A, Jia Y: A theoretical study of effects of cytosolic Ca2+ oscillations on activation of glycogen phosphorylase. Biophys Chem 2003, 106:193-202. 25. Weaver W, Shannon C: The Mathematical Theory of Communication Urbana and Chicago, IL: University of Illinois Press; 1949. 26. Imas O, Ropella K, Ward B, Wood J, Hudetz A: Volatile anesthetics disrupt frontal-posterior recurrent information transfer at gamma frequencies in rat. Neuroscience Lett 2005, 387(3):145-50. 27. Borst A, Theunissen F: Information theory and neural coding. Nature Neuroscience 1999, 2(11):947-957. 28. Prank K, Schöfl C, Läer L, Wagner M, von zur Mühlen A, Brabant G, Gabbiani F: Coding of time-varying hormonal signals in intracellular calcium spike trains. Pac Symp Biocomput 1998:633-44. 29. Kropp M, Gabbiani F, Prank K: Differential coding of humoral stimuli by timing and amplitude of intracellular calcium spike trains. IEE Proc-Syst Biol 2005, 152(4):263-268. 30. Prank K, Läer L, von zur Mühlen A, Brabant G, Schöfl C: Decoding of intracellular calcium spike trains. Europhys Lett 1998, 42(2):143-147. 31. Schreiber T: Measuring information transfer. Phys Rev Lett 2000, 85(2):461-4. 32. Katura T, Tanaka N, Obata A, Sato H, Maki A: Quantitative evaluation of interrelations between spontaneous low-frequency oscillations in cerebral hemodynamics and systemic cardiovascular dynamics. Neuroimage 2006, 31(4):1592-600. 33. Marschinski R, Kantz H: Analysing the information flow between financial time series – An improved estimator for transfer entropy. Europ Phys J B 2002, 30(2):275-281. 34. Materassi M, Wernik A, Yordanova E: Determining the verse of magnetic turbulent cascades in the Earth's magnetospheric cusp via transfer entropy analysis: preliminary results. Nonlin Processes Geophys 2007, 14:153-161. 35. Nichols J, Seaver M, Trickey S, Salvino L, Pecora D: Detecting impact damage in experimental composite structures: an information-theoretic approach. Smart Mater Struct 2006, 15(2):424-434. 36. Lungarella M, Sporns O: Mapping Information Flow in Sensorimotor Networks. PLOS Comp Biol 2006, 2(10):e144. 37. Kummer U, Olsen L, Dixon C, Green A, Bornberg-Bauer E, Baier G: Switching from Simple to Complex Oscillations in Calcium Signaling. Biophys J 2000, 79:1188-1195. 38. Gillespie D: A General Method for Numerically Simulating the Stochastic Time Evolution of Coupled Chemical Reactions. J Comp Phys 1976, 22:403-434. 39. Schreiber T, Schmitz A: Surrogate time series. Physica D 2000, 142(3–4):346-382. 40. Kummer U, Krajnc B, Pahle J, Green A, Dixon C, Marhl M: Transition from Stochastic to Deterministic Behavior in Calcium Oscillations. Biophys J 2005, 89(3):1603-1611. 41. Rao C, Wolf D, Arkin A: Control, exploitation and tolerance of intracellular noise. Nature 2002, 420:231-237. 42. Savage V, Allen A, Brown J, Gillooly J, Herman A, Woodruff W, West G: Scaling of number, size, and metabolic rate of cells with body size in mammals. PNAS 2007, 104(11):4718-4723. 43. Steuer R, Kurths J, Daub C, Weise J, Selbig J: The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics 2002, 18(Suppl 2):S231-S240. 44. Silverman B: Density estimation for statistics and data analysis London: Chapman & Hall/CRC; 1986. 45. Kaiser A, Schreiber T: Information transfer in continuous processes. Physica D 2002, 166(1–2):43-62. 46. Gillespie D: Exact Stochastic Simulation of Coupled Chemical Reactions. J Phys Chem 1977, 81(25):2340-2361. 47. Gibson M, Bruck J: Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. J Phys Chem A 2000, 104(9):1876-1889. 48. Cao Y, Li H, Petzold L: Efficient formulation of the stochastic simulation algorithm for chemically reacting systems. J Chem Phys 2004, 121(9):4059-4067. 49. Copasi [http://www.copasi.org] 50. Dizzy [http://magnet.systemsbiology.net/software/Dizzy] 51. Gillespie D: Marcov Processes – An Introduction for Physical Scientists San Diego, CA: Academic Press, Inc; 1992. 52. Salis H, Kaznessis Y: Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. J Chem Phys 2005, 122(5):054103. 53. Alfonsi A, Cancès E, Turinici G, Di Ventura B, Huisinga W: Adaptive Simulation of Hybrid Stochastic and Deterministic Models for Biochemical Systems. ESAIM Proceedings 2005, 14:1-13. 54. Octave [http://www.octave.org] 55. Kantz H, Schreiber T: Nonlinear Time Series Analysis Cambridge, UK: Cambridge University Press; 1997. 56. Dixon C, Cobbold P, Green A: Actions of ADP, but not ATP, on cytosolic free Ca2+ in single rat hepatocytes mimicked by 2- methylthioATP. Br J Pharmacol 1995, 116:1979-1984. 57. Cobbold P, Lee J: Aequorin measurements of cytoplasmic free calcium. In Cellular Calcium: A Practical Approach Edited by: McCormack J, Cobbold P. Oxford: I.R.L. Press; 1991:55-81.
URI:	http://wrap.warwick.ac.uk/id/eprint/526