Abstract

The spatial distribution of DNA base sequence A, C, G and T exhibit selfsimilar fractal fluctuations and the corresponding power spectra follow inverse power law of the form 1/fwhere f is the frequency andthe exponent. Inverse power law form for power spectra implies the following: (1) A scale invariant eddy continuum, namely, the amplitudes of component eddies are related to each other by a scale factor alone. In general, the scale factor is different for different scale ranges and indicates a multifractal structure for the spatial distribution of DNA base sequence. (2) Scale invariance of eddies also implies long-range spatial correlations of the eddy fluctuations. Multifractal structure to space-time fluctuations and the associated inverse power law form for power spectra is generic to spatially extended dynamical systems in nature and is a signature of self-organized criticality. Mathematical models for the simulation and prediction of fractal fluctuations of dynamical systems are nonlinear and do not have analytical solutions. Finite precision computer realizations of non-linear mathematical models of dynamical systems also exhibit self-organized criticality manifested as sensitive dependence on initial conditions and give chaotic solutions resulting in 'deterministic chaos'. The exact physical mechanism for the observed self- organized criticality is not yet identified. The author has developed a general systems theory where quantum mechanical laws emerge as self-consistent explanations for the observed long-range space-time correlations in macro-scale dynamical systems, i.e., the apparently chaotic fractal fluctuations are signatures of quantum-like chaos in dynamical systems. The model also provides unique quantification for the observed inverse power law form for power spectra in terms of the statistical normal distribution. In this paper it is shown that the frequency distribution of the bases C+G in all available contiguous sequences for Human chromosome Y DNA exhibit model predicted quantum-like chaos.

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