I doubt you have thought this through. the simplest cells are pretty complex (and that seems to be the strongest argument against abiogenesis). There are single-celled organisms with genomes many times longer that that of humans.
Like Betty said- not that single incidents might extrapolate to every other organism, it appears that, after a brief look, that cloned organisms are able to ‘stave off scenescene’ by replacing old modules with new- Whiel this extends their ‘life’ for a long time, it appears that there still is a mainatanace system inplce replacing elements which have succumbed to scenescene- or the effects of entropy.
“Using two models, we examined how stage-specific life-history rates of a clone’s modules determine whether a genetic individual escapes senescence by replacing old modules with new ones.”
It also appears that long lived organisms or animals such a tortoises are subject to entropy- they are just aging more slowly- in some cases much more slowly
If you have info to hte cotnrary- could you post it?
Perhaps that's because the genomes of single-celled organisms may contain a heck of a lot of "redundant" information? Remove the redundancy, and they are found to be simple after all?
I mean, for example, the situation where the text message "Happy Birthday" is endlessly reiterated as a screen saver on a PC. It looks like a whole lot of information. But in algorithmic complexity terms, the information size is teensy, amounting to what is conveyed by the two words, "happy" and "birthday."...
Or take as another example the following exchange between Philip Ball and Grandpierre, added as a "dialogue" to the latter's Chapter 28 of the already cited work.
Philip Ball
At root, I am perhaps most perplexed by the notion that algorithmic complexity has to be high to account for biological phenomena. Has it not been one of the underpinnings of complexity science that complex behaviours can arise from simple rules?Attila Grandpierre
This is a very good question.... Indeed, today it is a dominant view that complexity can arise from simple rules. But one must distinguish between the above weak form of such a statement and a stronger form claiming that all complexities found in nature must be derived from simple rules regulating only the physical properties of the systems and organisms. ...[A]lgorithmic complexity has a fundamentally different nature from morphological or phenomenological complexity. Let us take an example. The circle has a low algorithmic complexity (cca. 100 bits), and an extremely high morphological complexity (infinite points, infinite bits). Therefore, it is apparent that a small algorithmic complexity is able to produce an extremely high amount of morphological complexity. In this sense, algorithmic complexity is more fundamental than [the] morphological one. Does it follow from the fact that the circle has a low algorithmic complexity that we must think that all the mathematical functions can be derived from simple rules?No, because, for example, there are many mathematical objects that cannot be given in algebraically closed form. Let us take another example. There are simple machines like a watch having a low algorithmic complexity. Does it follow that we must accept that all machines must have low algorithmic complexity? No, because a computer with higher algorithmic complexity can solve more tasks and more easily than a smaller computer. Moreover, once the machine is ready, its functions are specified, its algorithmic complexity is given.
But there are tasks for living organisms requiring revealing a problem, to realize the existence of an unexpected task. Living organisms must continuously solve new and new problems, and problem solving ... by definition corresponds to the production of algorithmic complexity. Production of algorithmic complexity is possible only if a still deeper level of complexity (generative complexity) exists which can produce algorithmic complexity on the basis of a unified context corresponding to the generative principle. ...[W]e must realize that algorithmic complexity and generative complexity can be regarded as full members of the conceptual framework of science and they are fundamental aspects of nature. My answer to Ball’s problem is that we have to consider systems and organisms [as having] high algorithmic complexity. The algorithmic complexity of a circle or a fractal is low, a watch has a higher algorithmic complexity, a computer still higher, and a living organism still much higher. Indeed, the algorithmic complexity of a living organism must be high to account for biological phenomena. — ibid., p. 608.