Self-organization in biological systems pdf download

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Self-ordering phenomena should not be confused with self-organization. Crystallization and the spontaneously forming dissipative structures of Prigogine are examples of self-ordering. Hypercycles, genetic and evolutionary algorithms, neural nets, and cellular automata have not been shown to self-self-organization in biological systems pdf download spontaneously into nontrivial functions.

Laws and fractals are both compression algorithms containing minimal complexity and information. Organization typically contains large quantities of prescriptive information. Prescriptive information either instructs or directly produces nontrivial optimized algorithmic function at its destination. Prescription requires choice contingency rather than chance contingency or necessity.

Organization requires prescription, and is abstract, conceptual, formal, and algorithmic. Physical switch settings allow instantiation of nonphysical selections for function into physicality. Switch settings represent choices at successive decision nodes that integrate circuits and instantiate cooperative management into conceptual physical systems. Switch positions must be freely selectable to function as logic gates.

Switches must be set according to rules, not laws. No falsifiable theory of self-organization exists. Check if you have access through your login credentials or your institution. What are Corrected Proof articles?

68 55 55 55 14. 18 45 45 0 12. An alternative biological synthesis is presented that conceptualizes evolutionary biology as an epiphenomenon of integrated self-referential information management. Since all biological information has inherent ambiguity, the systematic assessment of information is required by living organisms to maintain self-identity and homeostatic equipoise in confrontation with environmental challenges.

Through their self-referential attachment to information space, cells are the cornerstone of biological action. That individualized assessment of information space permits self-referential, self-organizing niche construction. That deployment of information and its subsequent selection enacted the dominant stable unicellular informational architectures whose biological expressions are the prokaryotic, archaeal, and eukaryotic unicellular forms. Multicellularity represents the collective appraisal of equivocal environmental information through a shared information space. This concerted action can be viewed as systematized information management to improve information quality for the maintenance of preferred homeostatic boundaries among the varied participants. When reiterated in successive scales, this same collaborative exchange of information yields macroscopic organisms as obligatory multicellular holobionts. Therefore, evolutionary biology can be framed as a complex reciprocating interactome that consists of the assessment, communication, deployment and management of information by self-referential organisms at multiple scales in continuous confrontation with environmental stresses.

Feedback exists between two parts when each affects the other. Simple causal reasoning about a feedback system is difficult because the first system influences the second and second system influences the first, leading to a circular argument. This makes reasoning based upon cause and effect tricky, and it is necessary to analyze the system as a whole. Self-regulating mechanisms have existed since antiquity, and the idea of feedback had started to enter economic theory in Britain by the eighteenth century, but it wasn’t at that time recognized as a universal abstraction and so didn’t have a name. This action of feeding back of the signal from output to input gave rise to the use of the term “feedback” as a distinct word by 1920.

Over the years there has been some dispute as to the best definition of feedback. To this the mathematician retorts that if feedback is to be considered present only when there is an actual wire or nerve to represent it, then the theory becomes chaotic and riddled with irrelevancies. He emphasizes that the information by itself is not feedback unless translated into action. The resulting change in engine torque, the feedback, combines with the torque exerted by the changing road grade to reduce the error in speed, minimizing the road disturbance. The terms “positive” and “negative” were first applied to feedback prior to WWII. 1934 paper first details the use of negative feedback in electronic amplifiers.

Positive feed-back increases the gain of the amplifier, negative feed-back reduces it. Friis and Jensen had made the same distinction Black used between ‘positive feed-back’ and ‘negative feed-back’, based not on the sign of the feedback itself but rather on its effect on the amplifier’s gain. In contrast, Nyquist and Bode, when they built on Black’s work, referred to negative feedback as that with the sign reversed. Black had trouble convincing others of the utility of his invention in part because confusion existed over basic matters of definition. The terms positive and negative feedback are defined in different ways within different disciplines. Yet even within a single discipline an example of feedback can be called either positive or negative, depending on how values are measured or referenced.

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