Abstract:All agree there is information in biological structure and function. Although the term information is commonly used in science, its precise definition and nature can be illusive, as illustrated by the following questions:
“…An elaboration on the specificd e finition of 'information' being used is first required. Shannon recognised his formulation of information could not be used in all contexts [1,2].…”
Section: Introductionmentioning
confidence: 99%
“…These questions cannot be answered properly with a direct ‘yes’ or ‘no.’ An elaboration on the specific definition of ‘information’ being used is first required. Shannon recognised his formulation of information could not be used in all contexts [1, 2]. “It seems to me that we all define ‘information’ as we choose; and, depending on what field we are working in, we will choose different definitions.…”
Both Shannon and Kolmogorov-Chaitin-Solomonoff (KCS) information models fail to measure meaningful information in images. Pictures of a cow and correlated noise can both have the same Shannon and KCS information, but only the image of the cow has meaning. The application of 'algorithmic specified complexity' (ASC) to the problem of distinguishing random images, simple images and content-filled images is explored. ASC is a model for measuring meaning using conditional KCS complexity. The ASC of various images given a context of a library of related images is calculated. The 'portable network graphic' (PNG) file format's compression is used to account for typical redundancies found in images. Images which containing content can thereby be distinguished from those containing simply redundancies, meaningless or random noise.
“…An elaboration on the specificd e finition of 'information' being used is first required. Shannon recognised his formulation of information could not be used in all contexts [1,2].…”
Section: Introductionmentioning
confidence: 99%
“…These questions cannot be answered properly with a direct ‘yes’ or ‘no.’ An elaboration on the specific definition of ‘information’ being used is first required. Shannon recognised his formulation of information could not be used in all contexts [1, 2]. “It seems to me that we all define ‘information’ as we choose; and, depending on what field we are working in, we will choose different definitions.…”
Both Shannon and Kolmogorov-Chaitin-Solomonoff (KCS) information models fail to measure meaningful information in images. Pictures of a cow and correlated noise can both have the same Shannon and KCS information, but only the image of the cow has meaning. The application of 'algorithmic specified complexity' (ASC) to the problem of distinguishing random images, simple images and content-filled images is explored. ASC is a model for measuring meaning using conditional KCS complexity. The ASC of various images given a context of a library of related images is calculated. The 'portable network graphic' (PNG) file format's compression is used to account for typical redundancies found in images. Images which containing content can thereby be distinguished from those containing simply redundancies, meaningless or random noise.
BackgroundFunctional information is normally communicated using specific, context-dependent strings of symbolic characters. This is true within the human realm (texts and computer programs), and also within the biological realm (nucleic acids and proteins). In biology, strings of nucleotides encode much of the information within living cells. How do such information-bearing nucleotide strings arise and become established?MethodsThis paper uses comprehensive numerical simulation to understand what types of nucleotide strings can realistically be established via the mutation/selection process, given a reasonable timeframe. The program Mendel’s Accountant realistically simulates the mutation/selection process, and was modified so that a starting string of nucleotides could be specified, and a corresponding target string of nucleotides could be specified. We simulated a classic pre-human hominin population of at least 10,000 individuals, with a generation time of 20 years, and with very strong selection (50 % selective elimination). Random point mutations were generated within the starting string. Whenever an instance of the target string arose, all individuals carrying the target string were assigned a specified reproductive advantage. When natural selection had successfully amplified an instance of the target string to the point of fixation, the experiment was halted, and the waiting time statistics were tabulated. Using this methodology we tested the effect of mutation rate, string length, fitness benefit, and population size on waiting time to fixation.ResultsBiologically realistic numerical simulations revealed that a population of this type required inordinately long waiting times to establish even the shortest nucleotide strings. To establish a string of two nucleotides required on average 84 million years. To establish a string of five nucleotides required on average 2 billion years. We found that waiting times were reduced by higher mutation rates, stronger fitness benefits, and larger population sizes. However, even using the most generous feasible parameters settings, the waiting time required to establish any specific nucleotide string within this type of population was consistently prohibitive.ConclusionWe show that the waiting time problem is a significant constraint on the macroevolution of the classic hominin population. Routine establishment of specific beneficial strings of two or more nucleotides becomes very problematic.
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