Autoregressive modeling of biological phenomen

Uncategorized

Abstract

Many natural phenomena can be described by power-laws of temporal or spatial correlations. The equivalence in frequency domain is the 1/f spectrum. A closer look at various experimental data reveals more or less significant deviations from a 1/f characteristic. Such deviations are especially evident at low frequencies and less evident at high frequencies where spectra are very noisy. We exemplify such cases with four different types of phenomena offered by molecular biology (series of coding sequence lengths from microbial genomes, series of the atomic mobility of the protein main chain), cell biophysics (flickering of red blood cells), cognitive psychology (mentally generated series of apparent random numbers) and astrophysics (the X-ray flux variability of a galaxy). All these examples appear to be described by autoregressive models of the first-order AR(1) or higher-order models. This further shows that a spectrum needs to be first subjected to averaging as, long ago, suggested by Mandelbrot otherwise the spectra can be more or less easily confused and/or approximated by power-laws.

 

Authors

V.V. Morariu

C. Vamoș
-Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy

S.M. Soltuz
-Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy

A. Pop

 

L. Buimaga-Iarinca

Keywords

Autoregresive model; molecular biology; cell biophysics; cognitive psychology

Cite this paper as:

V.V. Morariu, C. Vamoş, Ş.M. Şoltuz, A. Pop, L. Buimaga-Iarinca, O. Zainea, Autoregressive modeling of biological phenomena, Biophysical Reviews and Letters, vol. 5 (2010) no. 3, pp. 109-128.

References

PDF

Not available yet.

About this paper

Journal

Biophysical Reviews and Letters

Publisher Name
DOI
Print ISSN

1793-0480

Online ISSN

1793-7035

MR

?

ZBL

?

References

Paper in html format

References

[1] H. E.   Stanley and N.   Ostrowsky ,Correlations and Connectivity: Geometric Aspects of Physics, Chemistry and Biology ( Kluwer , Dordrecht , 1990 ) . CrossrefGoogle Scholar

[2] A.   Bunde and S.   Havlin , Fractals in Science ( Springer , Berlin , 1994 ) . Google Scholar

[3] E. Milotti, 1/f noise: a pedagogical review , arXiv:physics/0204033 . Google Scholar

[4] B. B.   Mandelbrot , Multifractals and 1/f Noise ( Springer , New York , 1998 ) . Google Scholar

[5] J. B.   Bassingthwaighte , Fractal Physiology ( Oxford University Press , New York , 1994 ) CrossrefGoogle Scholar

[6] V. V. Morariu and A. Coza, Physica A 320, 461 (2003), DOI: 10.1016/S0378-4371(02)01661-8.CrossrefISIGoogle Scholar

[7] V. V. Morariu, A. Isvoran and O. Zainea, Chaos Soliton. Fract. 32, 1305 (2007), DOI: 10.1016/j.chaos.2005.12.023.CrossrefISIGoogle Scholar

[8] V. V. Morariu and A. Coza, Fluct. Noise Lett 1 (2001) p. L111.Google Scholar

[9] V. V. Morariuet al., Fractals 9, 379 (2001), DOI: 10.1142/S0218348X01000919.LinkISIGoogle Scholar

[10] M. König and J. Timmer, Astron. Astrophys. Suppl. Ser. 124, 589 (1997), DOI: 10.1051/aas:1997104.CrossrefGoogle Scholar

[11] J. Timmeret al., Phys. Rev. E 61, 1342 (2000), DOI: 10.1103/PhysRevE.61.1342.CrossrefISIGoogle Scholar

[12] Th. L. Thornton and D. L. Gilden, Psychon. Bull. Rev. 12, 409 (2005).CrossrefISIGoogle Scholar

[13] P. J.   Brockwel and R. A.   Davies , Time Series: Theory and Methods , 2nd edn. ( Springer , New York , 1991 ) . CrossrefGoogle Scholar

[14] J. D.   Hamilton , Time Series Analysis ( Princeton University Press , 1994 ) . Google Scholar

[15] C. Vamoş, S. M. Şoltuz and M. Crăciun, Order 1 autoregressive process of finite length , arXiv:0709.2963 . Google Scholar

[16] P.   Stoica and R. L.   Moses ,Introduction to Spectral Analysis ( Prentice Hall , 1997 ) . Google Scholar

[17] W. S. Rasband, ImageJ, U.S. National Institutes of Health, Bethesda, Maryland, USA, http://rsb.info.nih.gov/ij, 1997-2010 . Google Scholar

[18] Y. P. Elsworth and J. F. James, Mon. Not. R. Astron. Soc. 198, 889 (1982).CrossrefISIGoogle Scholar

[19] J. C. Lochner, J. H. Swank and A. E. Szymkowiak, Astrophys. J. 76, 295 (1991).Google Scholar

[20] M. A. Smith and R. D. Robinson, ASP Conf. Ser. 292, 263 (2003).Google Scholar

[21] C. M. Gaskell and E. S. Klimek, Astron. Astrophys. Trans. 22, 661 (2003), DOI: 10.1080/1055679031000153851.CrossrefGoogle Scholar

[22] P. Uttley and I. M. McHardy, Mon. Not. R. Astron. Soc. 323, (2001), DOI: 10.1046/j.1365-8711.2001.04496.x.Google Scholar

[23] I. E. Papadakis and A. Lawrence, Mon. Not. R. Astron. Soc. 272, 161 (1995).CrossrefISIGoogle Scholar

[24] I. M. McHardyet al., Mon. Not. R. Astron. Soc. 348, 783 (2004), DOI: 10.1111/j.1365-2966.2004.07376.x.CrossrefISIGoogle Scholar

[25] C. K. Penget al., Phys. Rev. E 49, 1685 (1994), DOI: 10.1103/PhysRevE.49.1685.CrossrefISIGoogle Scholar

[26] C. Vamoş, Phys. Rev. E 75, 036705 (2007), DOI: 10.1103/PhysRevE.75.036705.CrossrefGoogle Scholar

[27] D. J.   Li and S.   Zhang , Prediction of Genomic Properties and Classification of Life by Protein Length Distribution ( ) ,   arXiv:0806.0205v1 . Google Scholar

[28] D. J. Li and S. Zhang, Mod. Phys. Lett. B 23, 3563 (2009), DOI: 10.1142/S0217984909021624.LinkISIGoogle Scholar

[29] D. J. Li and S. Zhang, Mod. Phys. Lett. B 23, 3471 (2009), DOI: 10.1142/S0217984909021533.LinkISIGoogle Scholar

[30] V. V. Morariu and L. Buimagă-Iarinca, Fluct. Noise Lett. 9, 47 (2010).LinkISIGoogle Scholar

Related Posts

Leave a Reply

Your email address will not be published. Required fields are marked *

Fill out this field
Fill out this field
Please enter a valid email address.

Menu