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The Darwinist and the computer programmer

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Actually the available hardware computing power is enormous and the software technologies are very sophisticated and powerful. Given the above fortunate situation about the technological advance of informatics, many phenomena and processes in many fields are successfully computer simulated. Routinely airplane pilots and astronauts learn their job in dedicated simulators, and complex processes, as weather forecast and atomic explosions, are simulated on computers.

Question: why Darwinian unguided evolution hasn’t been yet computer simulated? I wonder why evolutionists haven’t yet simulated it, so to prove us that Darwinism works. As known, experiments of evolution in vitro failed, then maybe experiments in silico would work. Why don’t evolutionists show us in a computer the development of new biological complexity by simulating random mutations and selection on self-reproductive digital organisms?

Here I try my answer, then you are free to provide your own. I will do it in the format of an imaginary dialogue. Let’s suppose a Darwinist who meets a computer programmer to ask him to develop a simulation program of Darwinian evolution.

Programmer (P): “What’s your problem? I can program whatever you want. What we need is a detailed description of the phenomenon and a correct model of the process.”

Darwinist (D): “I would like to simulate biological evolution, the process thanks to which a species transforms into another species, by means of random mutations and natural selection”.

P: “Well, I think first off we need a model of an organism and its development, or something like that”.

D: “We have a genotype (containing the heritable information, the genome, the DNA) and its product, the phenotype”.

P: “I read that the DNA is a long sequence of four symbols. We could model it as a long string of characters. String of characters and operations on them are easily manipulable by computers. Just an idea.”

D: “Good, it is indeed unguided variations on DNA that drive evolution.”

P: “Ok, if you want, after modeling the genome, we can perform on the DNA character strings any unguided variation: permutations, substitutions, translations, insertions, deletions, import, export, pattern scrambling, whatever you like. We have very good pseudo random generators to simulate these operations”.

D: “Cool. Indeed those unintelligent variations produce the transformations of the phenotypes, what is called ‘evolution'”.

P: “Hmm… wait, just a question. There is a thing not perfectly clear to me. To write the instructions to output the phenotype from the genotype I need also a complete model of the phenotype and a detailed description of how it arises from the genotype. You see, the computer wants anything in the format of sequences composed of 0s and 1s, it is not enough to send it generic commands”.

D: “The genotype determines the genes and in turn the genes are receipts for proteins. The organisms basically are made of proteins.”

P: “Organisms are made of proteins, like buildings are made of bricks, aren’t they? It seems to me that these definitions are an extremely simplistic and reductive way of considering organisms and buildings. Both are not simple “containers” of proteins/bricks, as potatoes in a bag. It seems to me it is entirely missing the process of construction from proteins to organisms (while it is perfectly known in the case of bricks and buildings)”.

D: “To be honest I don’t know in detail how the phenotype comes from the genotype… actually no one on earth do.”

P: “Really? You know, in my damn job one has to perfectly specify all instructions and data in a formal language that doesn’t allow equivocations. It is somewhat mathematical. If you are unable to perfectly specify the phenotypic model and the process driving the construction of the phenotype from the genotype, I cannot program the simulation of evolution for you. What we would eventually obtain would be less than a toy and would have no explicative value compared to the biological reality (by the way I assure you that, differently, all computer games are serious works, where everything is perfectly specified and programmed, at the bit and pixel level, believe me)… Sorry… I don’t want to be indiscreet, but how can Darwinists claim with such certainty that variations in a process produce certain results if they know little of the models and nothing of the process involved in the first place?

D: _no-answer_

The above short dialogue between the Darwinist and the programmer shows us a thing. There are two worlds: the world of informatics where all instructions/data must be perfectly specified and have to pass checks, otherwise the business doesn’t work; and the world of the just so stories, where the statements may be equivocal and even inconsistent and have to pass no check. Evolutionism pertains to the latter kind of worlds. As the programmer politely noted, evolutionism pretends to claim that variations on a process produce specific results when the process itself is unknown and unspecified. In other words, why – to put it a la Sermonti – from the genome of a fly arises a fly, not a horse? If they cannot answer that basic question, how can they claim that unguided variations on genomes produced even the 500 million past and living species?

This fundamental incoherence and simplism can “work” in the Darwin’s world, but stops at the outset in the logic world of informatics. This is one of the reasons why a convincing and complete computer simulation of Darwinian evolution has not yet been performed far now, despite Darwinians would like to get it.

P.S. Thanks to Mung for the suggestion about the topic of this post.

Comments
p.s. perhaps not impossible, but biochemistry seems would be far too computationally intensive to be practical.JGuy
November 5, 2013
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Given an algorithm to translate genotype to phenotype, you would need to model biochemistry - that too me seems impossible to model in a computer program. Why not build a semi-complex replicating program that copies itself, and place it in a virtual environment where it has access to program bits, bytes or whatever...and competes with others. Perhaps, even include in the replicator program, a 3D representation. It seems to me, that this would at least test the creative power of RM + NS. My predication is that the code will end up smaller than the original replicator... not a replicator with more novel & more complex survival features (physical traits or or behaviors).JGuy
November 5, 2013
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“It is not feasible to understand,, organisms by exhaustively cataloging all interactions in a comprehensive, bottom-up manner.”
Which would be like trying to understand a battleship by modeling the interactions its molecules. One can only hope to "understand" such entities by hypothesizing design macro-feature purpose and reverse engineering. Nothing useful can be gleaned from the materialist approach; only the assumption that the macro-feature was designed and purposefully engineered offers a worthwhile, actionable investigatory pathway.William J Murray
November 5, 2013
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Some notes on trying (and failing) to model organisms realistically with computers:
The Humpty-Dumpty Effect: A Revolutionary Paper with Far-Reaching Implications - Paul Nelson - October 23, 2012 Excerpt: Put simply, the Levinthal paradox states that when one calculates the number of possible topological (rotational) configurations for the amino acids in even a small (say, 100 residue) unfolded protein, random search could never find the final folded conformation of that same protein during the lifetime of the physical universe. http://www.evolutionnews.org/2012/10/a_revolutionary065521.html So Much For Random Searches - PaV - September 2011 Excerpt: There’s an article in Discover Magazine about how gamers have been able to solve a problem in HIV research in only three weeks (!) that had remained outside of researcher’s powerful computer tools for years. This, until now, unsolvable problem gets solved because: "They used a wide range of strategies, they could pick the best places to begin, and they were better at long-term planning. Human intuition trumped mechanical number-crunching." Here’s what intelligent agents were able to do within the search space of possible solutions:,,, "until now, scientists have only been able to discern the structure of the two halves together. They have spent more than ten years trying to solve structure of a single isolated half, without any success. The Foldit players had no such problems. They came up with several answers, one of which was almost close to perfect. In a few days, Khatib had refined their solution to deduce the protein’s final structure, and he has already spotted features that could make attractive targets for new drugs." Thus,, Random search by powerful computer: 10 years and No Success Intelligent Agents guiding powerful computing: 3 weeks and Success. https://uncommondescent.com/intelligent-design/so-much-for-random-searches/ To Model the Simplest Microbe in the World, You Need 128 Computers - July 2012 Excerpt: Mycoplasma genitalium has one of the smallest genomes of any free-living organism in the world, clocking in at a mere 525 genes. That's a fraction of the size of even another bacterium like E. coli, which has 4,288 genes.,,, The bioengineers, led by Stanford's Markus Covert, succeeded in modeling the bacterium, and published their work last week in the journal Cell. What's fascinating is how much horsepower they needed to partially simulate this simple organism. It took a cluster of 128 computers running for 9 to 10 hours to actually generate the data on the 25 categories of molecules that are involved in the cell's lifecycle processes.,,, ,,the depth and breadth of cellular complexity has turned out to be nearly unbelievable, and difficult to manage, even given Moore's Law. The M. genitalium model required 28 subsystems to be individually modeled and integrated, and many critics of the work have been complaining on Twitter that's only a fraction of what will eventually be required to consider the simulation realistic.,,, http://www.theatlantic.com/technology/archive/2012/07/to-model-the-simplest-microbe-in-the-world-you-need-128-computers/260198/ "Complexity Brake" Defies Evolution - August 2012 Excerpt: "This is bad news. Consider a neuronal synapse -- the presynaptic terminal has an estimated 1000 distinct proteins. Fully analyzing their possible interactions would take about 2000 years. Or consider the task of fully characterizing the visual cortex of the mouse -- about 2 million neurons. Under the extreme assumption that the neurons in these systems can all interact with each other, analyzing the various combinations will take about 10 million years..., even though it is assumed that the underlying technology speeds up by an order of magnitude each year.",,, Even with shortcuts like averaging, "any possible technological advance is overwhelmed by the relentless growth of interactions among all components of the system," Koch said. "It is not feasible to understand,, organisms by exhaustively cataloging all interactions in a comprehensive, bottom-up manner." He described the concept of the Complexity Brake:,,, to read more go here: http://www.evolutionnews.org/2012/08/complexity_brak062961.html
Related notes:
Stephen Meyer - Functional Proteins And Information For Body Plans - video http://www.metacafe.com/watch/4050681
Dr. Stephen Meyer comments at the end of the preceding video,,,
‘Now one more problem as far as the generation of information. It turns out that you don’t only need information to build genes and proteins, it turns out to build Body-Plans you need higher levels of information; Higher order assembly instructions. DNA codes for the building of proteins, but proteins must be arranged into distinctive circuitry to form distinctive cell types. Cell types have to be arranged into tissues. Tissues have to be arranged into organs. Organs and tissues must be specifically arranged to generate whole new Body-Plans, distinctive arrangements of those body parts. We now know that DNA alone is not responsible for those higher orders of organization. DNA codes for proteins, but by itself it does not insure that proteins, cell types, tissues, organs, will all be arranged in the body. And what that means is that the Body-Plan morphogenesis, as it is called, depends upon information that is not encoded on DNA. Which means you can mutate DNA indefinitely. 80 million years, 100 million years, til the cows come home. It doesn’t matter, because in the best case you are just going to find a new protein some place out there in that vast combinatorial sequence space. You are not, by mutating DNA alone, going to generate higher order structures that are necessary to building a body plan. So what we can conclude from that is that the neo-Darwinian mechanism is grossly inadequate to explain the origin of information necessary to build new genes and proteins, and it is also grossly inadequate to explain the origination of novel biological form.’ - Stephen Meyer - (excerpt taken from Meyer/Sternberg vs. Shermer/Prothero debate - 2009) HOW BIOLOGISTS LOST SIGHT OF THE MEANING OF LIFE — AND ARE NOW STARING IT IN THE FACE - Stephen L. Talbott - May 2012 Excerpt: “If you think air traffic controllers have a tough job guiding planes into major airports or across a crowded continental airspace, consider the challenge facing a human cell trying to position its proteins”. A given cell, he notes, may make more than 10,000 different proteins, and typically contains more than a billion protein molecules at any one time. “Somehow a cell must get all its proteins to their correct destinations — and equally important, keep these molecules out of the wrong places”. And further: “It’s almost as if every mRNA [an intermediate between a gene and a corresponding protein] coming out of the nucleus knows where it’s going” (Travis 2011),,, Further, the billion protein molecules in a cell are virtually all capable of interacting with each other to one degree or another; they are subject to getting misfolded or “all balled up with one another”; they are critically modified through the attachment or detachment of molecular subunits, often in rapid order and with immediate implications for changing function; they can wind up inside large-capacity “transport vehicles” headed in any number of directions; they can be sidetracked by diverse processes of degradation and recycling... and so on without end. Yet the coherence of the whole is maintained. The question is indeed, then, “How does the organism meaningfully dispose of all its molecules, getting them to the right places and into the right interactions?” The same sort of question can be asked of cells, for example in the growing embryo, where literal streams of cells are flowing to their appointed places, differentiating themselves into different types as they go, and adjusting themselves to all sorts of unpredictable perturbations — even to the degree of responding appropriately when a lab technician excises a clump of them from one location in a young embryo and puts them in another, where they may proceed to adapt themselves in an entirely different and proper way to the new environment. It is hard to quibble with the immediate impression that form (which is more idea-like than thing-like) is primary, and the material particulars subsidiary. Two systems biologists, one from the Max Delbrück Center for Molecular Medicine in Germany and one from Harvard Medical School, frame one part of the problem this way: "The human body is formed by trillions of individual cells. These cells work together with remarkable precision, first forming an adult organism out of a single fertilized egg, and then keeping the organism alive and functional for decades. To achieve this precision, one would assume that each individual cell reacts in a reliable, reproducible way to a given input, faithfully executing the required task. However, a growing number of studies investigating cellular processes on the level of single cells revealed large heterogeneity even among genetically identical cells of the same cell type. (Loewer and Lahav 2011)",,, And then we hear that all this meaningful activity is, somehow, meaningless or a product of meaninglessness. This, I believe, is the real issue troubling the majority of the American populace when they are asked about their belief in evolution. They see one thing and then are told, more or less directly, that they are really seeing its denial. Yet no one has ever explained to them how you get meaning from meaninglessness — a difficult enough task once you realize that we cannot articulate any knowledge of the world at all except in the language of meaning.,,, http://www.netfuture.org/2012/May1012_184.html#2
bornagain77
November 5, 2013
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