Role of nonlinear dynamics in endocrine feedback

C.K. Bose, B.K. Sarkar, Herbert Jelinek

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Abstract

Endocrine feedback system may not be appreciated by conventional hormone level estimation at a single point of time. Our body also needs to be looked at as a dynamical system, a dissipative structure, a stability outside equilibrium as Ilya Prigogine, the Nobel laureate, called it. Specially, the information processing at neuroendocrine level must be guided by a science that is not random but deterministic. For sustenance of life body environ must always (at present moment) balance itself delicately and relentlessly. In this dynamical system feedback control will essentially be mathematical control that has to be understood in the light of physical control theory. Like, endocrine reaction that will follow Michaelis-Menten-Hill kinetics can be looked at as signal that moves in the phase space of our body and can be plotted in graph that can be digitized and analyzed using nonlinear science. Though image processing and texture screening is done using nonlinear complexity science, or chaos theory, as we call it, this signal of reaction kinetics may be looked as a vector running in phase space and its trajectory can be calculated in an n dimensional context which will require mathematical and statistical manipulation to bring in two dimensional graph. This graph then will be used to detect some fractal dimension after such normalization as is necessary. Whether the same kinetics of two separate body system will coincide or differ can be judged by those dimension. This can be used to differentiate disease state from normal state. Thus positive and negative feedback may be more rationally and mechanically explained and more definitive biochemical knowledge and prediction will be possible. Fractal geometry and nonlinear chaos has proven to be very much useful tool in quantifying the structure of idealized and naturally occurring from pure mathematics, through physics and chemistry, to biology and medicine. Many processes of normalization of data are discussed. Ahypothalamo-pituitary ovarian axis and ovarian cancer is taken up as example to use this science for better understanding of cancer aetiopathology.(Keywords: dynamical system, nonlinearity, endocrine, feedback)
Original languageEnglish
Pages (from-to)251-272
Number of pages22
JournalChaos and Complexity Letters
Volume3
Issue number3
Publication statusPublished - 2008

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