Self-replication inputs

As an illustration, we start by analyzing a very simple type of response experiment involving the evaluation of self-replication constants. We consider the perturbation of the self-replication of a species by an input flux, whereas the response to this perturbation is given by the variation of the disappearance rate of the species due to the input perturbation. We study two types of excitation (a) an increase of the input flux according to a step function and (b) an excitation of the neutral type, where the total input flux is kept constant but the fraction of a labeled compound in the input flux is varied, also as a step function. We assume that the measured values of the variation of the output flux (disappearance rate) are subject to experimental errors. In order to emulate the two types of response experiments, we solve the evolution equations (12.126) exactly for the two... 

Figures 12.2 and 12.3 show the error of the evaluated self-replication rate constant as a function of the experimental error and of the variation of the input flux for emulated experiments of type (a) and (b), respectively. In the case of emulated experiments of type (a) for which the evaluation of the rate constant is based on linearized kinetic equations, the error of the evaluated rate constant depends strongly on the variation of the input perturbation. The range of the final output error (—40%,+10%) is distorted in comparison with the range of the experimental error (—20%,+20%). For small values of the input perturbation, between 20% and 40%, the output error is surprisingly small— between 10% and 0%. As the input perturbation increases, the accuracy of the method deteriorates rapidly and for large perturbations the output error is almost twice as big as the experimental error. For the emulated experiments of type (b), where the rate coefficient is evaluated from our exact response law (12.105) without linearization, the situation is different. For input perturbations between 20% and 70% the error of the evaluated rate coefficient has about the same range of variation as the experimental error (-20%,+20%) and does not depend much on the size of the perturbation. For very large input perturbations, between 70% and 80%, the output error increases abruptly. In fig. 12.4 we show the difference of errors of the evaluated self-replication...

Fig. 1 2.4 The difference of errors of the evaluated self-replication rate constant (output error) evaluated from emulated experiments of type (a), with linearization, and type (b), without linearization, respectively. The figure shows that the variations of the input perturbation and of the experimental error have a different effect on the two types of response methods. The biggest difference occurs for large perturbations, because for large perturbations the linear approach is very inaccurate. (From [11].)...

This is the hrst in a three-part series investigating the internals of the simplest possible self-replicator (SSR). The SSR is dehned as having an enclosure with input and output gateways and having the ability to create an exact replica of itself by ingesting and processing materials from its environment. This hrst part takes an analytical approach and identihes, one by one, the internal functions that must operate inside the SSR to be a fully autonomous replicator. 

The goal of this study is to use an engineering approach to develop insights into the internal design of a simplest possible self-replicator (SSR). The SSR is defined for the purpose of this study as an autonomous artifact that has the ability to obtain material input from its environment, grow, and create an exact replica of itself. The replica should inherit the ability from its mother SSR to create, in its turn, an exact copy of itself. 

Eliminate the requirement that the SSR fabricate the most technically demanding parts, components, and assemblies (e.g., computer boards, microprocessors, semiconductor chips, memories, etc.). These high-technology parts/-components (referred to as vitamins in the self-replication literature) will be supplied and carefully labeled from the environment through the input gateways. 

All the objects in the SharedARK software are replicated on each participating machine. The SharedARK objects are represented identically within each computer. In addition, an input state broadcast process on each host lets all the machines on the network know its host s current mouse and keyboard state. Thus, each computer always has a self-contained and up-to-date representation of the entire virtual world. 

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