Simiarities between computer code and genetic code

[David_A_Reed]

I spend my days writing the computer code (Java, Perl, HTML, XML, etc., etc.) that powers web applications. The more I read about the DNA that determines the structure and behavior of living things, the more I am struck by the similarity between the genetic code and the computer code that I work with.

It is easy to see how the code for an eyeball could be adapted and modified to make the human eye, the eye of the blue whale, and the eye of the hawk -- through the skilled hand of a master Coder.

But to say that this code popped into existence through a series of accidents is just as absurd as to claim that Windows 7, or the latest Linux kernel, fell into place accidentally when a sliver of carbon ran into some static electricity.

David

[Tiggy]

I spend my days writing the computer code (Java, Perl, HTML, XML, etc., etc.) that powers web applications. The more I read about the DNA that determines the structure and behavior of living things, the more I am struck by the similarity between the genetic code and the computer code that I work with.

Except computer code is an abstract code. it uses abstract symbols to represent values and operations. That's why so many different programming languages can be used to program the same problem and produce the same result.

Genetic code is not abstract. It is just physical molecules following the laws of chemistry and physics in a complicate self-replicating reaction. You can't use any other materials for DNA besides the actual chemical components and get the same result.

Abstract codes require a designer. Self-replicating chemical reactions do not.

Computer code is often used as an analogy for the complicated chemical reactions of DNA,, but analogies are not reality.

But to say that this code popped into existence through a series of accidents is just as absurd as to claim that Windows 7, or the latest Linux kernel, fell into place accidentally when a sliver of carbon ran into some static electricity.

Good thing no one in the scientific community claims the currently observed genetic code popped into existence through a series of accidents.

It is a proven fact that imperfect self replicators in a selection driven feedback loop (like DNA molecules) will tend to develop increased complexity over time. All you need are the first self replicating molecules competing for resources, and the process proceeds entirely naturally with no outside intervention required.

- T

[Jorge]

But to say that this code popped into existence through a series of accidents is just as absurd
as to claim that Windows 7, or the latest Linux kernel, fell into place accidentally when a sliver of
carbon ran into some static electricity.

David

***********************************************************

The above is pretty-darn close to what the Evo-Faithful believe. Except that, for them,
it took "billions and billions of years" and lots of "fortuitous accidents" (a.k.a. beneficial
mutations). Other than those 'minor' details, the Evo-Faithful believe and promote that
something that is orders of magnitude more complex than Windows 7 "just happened
on its own with no guidance". Such is the faith of these people. I've long said that if I
ever needed a lesson in Blind Faith, my first stop would be to visit one of these people.

Jorge

[FreezBee]

***********************************************************

The above is pretty-darn close to what the Evo-Faithful believe. Except that, for them,
it took "billions and billions of years" and lots of "fortuitous accidents" (a.k.a. beneficial
mutations). Other than those 'minor' details, the Evo-Faithful believe and promote that
something that is orders of magnitude more complex than Windows 7 "just happened
on its own with no guidance". Such is the faith of these people. I've long said that if I
ever needed a lesson in Blind Faith, my first stop would be to visit one of these people.

Jorge

How do you measure complexity?

- FreezBee

[Jorge]

How do you measure complexity?

- FreezBee

*************************************************************************

You, again?

For the umpteenth time, it's not just complexity.
It's specified, interrelated, functional complexity.

Many things are complex but they don't have the attributes
mentioned and, thus, do not play a key role in this matter.

Try harder to l-i-s-t-e-n to what is being said.

Jorge

[Tiggy]

*************************************************************************

You, again?

For the umpteenth time, it's not just complexity.
It's specified, interrelated, functional complexity.

Many things are complex but they don't have the attributes
mentioned and, thus, do not play a key role in this matter.

Try harder to l-i-s-t-e-n to what is being said.

Jorge

Then tell us how to measure specified, interrelated, functional complexity.

Toss in a definition of specified, interrelated, and functional as it relates to complexity while you're at it too.

You're good at running your big mouth, so run it some more and give us some actual answers for once.

- T

[FreezBee]

For the umpteenth time, it's not just complexity.
It's specified, interrelated, functional complexity.

Many things are complex but they don't have the attributes
mentioned and, thus, do not play a key role in this matter.

Try harder to l-i-s-t-e-n to what is being said.

Was that supposed to be an answer?

You wrote "orders of magnitude more complex "; that is, no special qualification of 'complex'.

I am listening to, what is being it said; but maybe there's a difference between, what you say and what you mean, and for some reason you think it is up to the rest of us to guess, what you mean, rather than up to you to be more precise


- FreezBee

[technomage]

But to say that this code popped into existence through a series of accidents is just as absurd as to claim that Windows 7, or the latest Linux kernel, fell into place accidentally when a sliver of carbon ran into some static electricity.

While you note the similarities between genetic code and computer code, you fail to note the differences--including the (very major) difference that chemistry reactions will occur without intervention, while computer coding will not. Ignoring that difference is the only way your analogy even begins to be rational, but ignoring that difference is rather like ignoring that the sun comes up in the east while arguing against the dawn.

[Tiggy]

But to say that this code popped into existence through a series of accidents is just as absurd as to claim that Windows 7, or the latest Linux kernel, fell into place accidentally when a sliver of carbon ran into some static electricity.

While you note the similarities between genetic code and computer code, you fail to note the differences--including the (very major) difference that chemistry reactions will occur without intervention, while computer coding will not. Ignoring that difference is the only way your analogy even begins to be rational, but ignoring that difference is rather like ignoring that the sun comes up in the east while arguing against the dawn.

Another thing Mr.Reed seems not to be aware of is evolving computer code - artificial digital life. From the simplest self-replicating code for the original Core Wars programs, through Tierra, and all the way up to Avida, digital life has show remarkably well just how much complexity a self-replicating feedback system can produce.

From the Caltech Digital Life lab:

Overview

Simple living systems are populations of self-replicating entities that harbor information in the form of coded symbols, subject to external and internal noise. Canonical examples are replicating macromolecules of RNA, viruses in a host environment, bacteria replicating in a Petri dish, or special-purpose computer programs replicating in core memory. For the last instance (pioneered by Ray with the tierra software) it is assumed that the basic principles governing simple living systems are independent of the substrate, and can be recreated in a computational medium. Ray's system was developed further by the avida group to the avida system: a full-fledged research platform with the flexibility to address diverse questions relating to the physics of the living state.

The principles governing natural evolving systems as laid down forcefully by Darwin are, in the scientific arena, uncontested. However, a comprehensive account of these principles from the point of view of physics is lacking, as are many of the microscopic details that give rise to Darwin's ``effective'' theory. From this point of view the situation can, with respect to our understanding of the principles underlying the evolution of complexity, be likened to the development of the theory of chaotic systems prior to the discovery of the logistic map. Many instances of chaos in natural and artificial systems were known, but the absence of a unifying point of view obscured the importance of the concept. Following this analogy, it is thought that the impact of simple artificial living systems such as avida on the development of a microscopic understanding of the physics of the living state can parallel the impact that the discovery of the logistic map has had on the development of the theory of chaos.

Our research focuses on the spatial and temporal dynamics of simple living systems and should provide important insights on the nature of the evolutionary and adaptive process, as well as the properties of the environment that the adaptation takes place in. From the point of view of a physicist, simple living systems offer the opportunity to study life in its abstraction, devoid of the complications that obscure the fundamental processes which govern the evolution of complexity and the maintenance of information, over periods of time exceeding by many orders of magnitude the natural coherence of the information-bearing substrate. The latter qualities distinguish simple living systems from non-living statistical ensembles.

Research Areas

Adaptation

Adaptation is a process that can be viewed from a strictly information-theoretical point of view--the establishment of correlations between code and the environment it evolves in--or from a dynamical point of view in which the fitness of the dominant type increases in leaps and bounds. We investigated this dynamics of "learning" first in the tierra system, by studying how these self-replicating programs evolve computational capabilities if the code evolves in a world in which computational capabilities are rewarded ("you get what you select for"). In [ADAMI 95a], programs developed the ability to perform integer addition, some with algorithms so abstruse that they could not have been written by a human programmer. Also within tierra, we studied adaptation in terms of self-organized "avalanches" [ADAMI 95b]. Similar dynamics was observed in populations of E. coli bacteria adapting under controlled circumstances by the Bacterial Evolution group at MSU [ELENA 96]. We are now collaborating with this group to compare their results with more extensive experiments using Avida, to pinpoint the role of epistasis in the development of sex [LENSKI 99], and the role of chance, history, and contingency in evolution.

Evolution of Biological Complexity

Even though it used to be an accepted notion that complexity increases in evolution, this fact has come under attack for various reasons, mostly however because complexity seems as hard to define as it is to measure. In [ADAMI 99a], we introduced a new measure of physical complexity for the complexity of symbolic sequences such as genomes, based on Information Theory and Automata Theory. Physical complexity measures the amount of information that a sequence stores about its environment, and thus is conditional on it. This measure is subsequently applied to tRNA nucleotide sequence data obtained from the EMBL sequence library as a test. With such a measure in hand, the question about the evolution of complexity can be addressed in earnest, and we show in [ADAMI 99b] that complexity must increase in evolution given a fixed environment, simply because mutations under natural selection operate like a natural Maxwell Demon, keeping mutations that increase the information about the environment, but discarding those that don't. In a companion paper [OFRIA 99c], we show that an information theoretic treatment of the pressures underlying code evolution reveals a third evolutionary pressure besides r- and K-selection, which is the selection for neutrality. This selection can be oberved for example in the evolution of genetic organization [OFRIA 99a] and of differentiation [OFRIA 99b].

Ecology and evolution

The avida system allows us not only to study adaptation globally, but also how it affects certain clusters of similar creatures, the genotypes and species. In [ADAMI 95d], we measured the "historical" abundance distribution of genotypes (programs that share the same exact sequence) as well as the "ecological" one, and compared with a simple stochastic theory. We were also able to display that an "Age-Area" relationship holds in this artificial system, as well as a McArthur-Wilson law. We were also able to measure how fast a species spreads in real space by measuring the diffusion coefficient of information, as well as the speed of the wavefront of better adapted species. This analysis can be compared to a theoretical description, and we obtain complete agreement [ADAMI 96] This analysis pertains to populations living in a single homogenous niche. In the near future, we will create several niches for the programs, so that co-evolution can take place, and an ecology can develop when there was none before.

Robustness and Evolvability

One of the key questions in the search for the origins of life addresses the characteristics of our biochemistry that have resulted in robust and evolvable code. Equally importantly, we may ask whether the design criteria adopted in "biochemical microcode" (nucleic and amino acids) can be applied to digital life, to address the robustness and evolvability of computer languages. Supported by a gift from Microsoft Research, we have investigated the influence of certain design criteria for instruction sets, such as the usage of operands, template-based addressing, direct or complementary template matching, the presence or absence of labels, as well as the number of monomers (read: size of instruction set) on the robustness and evolvability of populations of programs. To study robustness of the code with respect to mutations, we studied the percentage of neutral, beneficial and lethal mutations, and the distribution of these percentages across many runs. For evolvability we measure the rate at which a population absorbs information from its environment: the capacity of the genetic channel. [ADAMI 98c]

Critical dynamics

In [ADAMI 95b] we addressed for the first time the possibililty that adaptation in living systems may proceed via self-similar avalanches, giving rise to ubiquitous power laws in abundance and age distributions. Such experiments have been carried out in avida also [ADAMI 95d] [ADAMI 98a], and the critical exponent obtained in such distributions was obtained as a function of the mutation rate in [ADAMI 98d]. There, we find that there are violations of scale-free behavior both at high and low mutation rates, which prompts us to investigate finite-size scaling corrections and experiments with different population sizes in the future. From a more theoretical ponit of view, we have developed a theory of branching processes [CHU 99a] which predicts these geometric laws under very general circumstances, and where a single parameter, the neutrality of the fitness landscape, determines the extent of violation of scale-free bahavior. By fitting this model to actual abundance distributions (obtained from the fossil record [CHU 99b], catalogued Flora and Fauna, ecological distributions, or populations of digital organisms) the neutrality, or level of competition within an ecological niche, can be estimated even if the taxon has long been extinct. Finally, the branching model provides a qualitative explanation for the power laws observed in the sandpile of Bak, Tang, and Wiesenfeld within a conventional picture of critical second-order phase transitions with an unstable critical point [CHU 99c].

Theory of molecular evolution

The theory of evolution, while mature in principle, is not a quantitative one. Not surpringly, there still appears to be consternation in some circles about how evolution manages not to contradict the second law of thermodynamics. A fundamental quantitative theory of molecular evolution is statistical in nature, such as Eigen's (1971). However, such theories are even more difficult to test experimentally as they are to solve analytically. With the help of the avida system, we are able to test theories of molecular evolution, and influence theory formation itself. In [ADAMI 95c], we describe what are the basic building blocks of a theory of simple living systems. A version of Eigen's theory but inspired by results from avida is presented in [ADAMI 97], which shows how evolution leads to longer and longer genomes, up to the limit imposed by Eigen's error threshold. In particular, it implies that there is an optimal relationship between sequence length and mutation rate in simple organisms [ADAMI 00].

Most of the work done by our group is also described in detail (along with many other aspects of Artificial Life) in the book Introduction to Artificial Life by Chris Adami.

source with much more info

- T

[grmorton]

I spend my days writing the computer code (Java, Perl, HTML, XML, etc., etc.) that powers web applications. The more I read about the DNA that determines the structure and behavior of living things, the more I am struck by the similarity between the genetic code and the computer code that I work with.

It is easy to see how the code for an eyeball could be adapted and modified to make the human eye, the eye of the blue whale, and the eye of the hawk -- through the skilled hand of a master Coder.

But to say that this code popped into existence through a series of accidents is just as absurd as to claim that Windows 7, or the latest Linux kernel, fell into place accidentally when a sliver of carbon ran into some static electricity.

David

I actually developed a program by making mistakes. It was a higher level program, not machine language, but here is what happened.

Years ago at lunch one day I was bored so I decided to code Sierpinski's gasket. I did it from memory (which was stupid) and I messed up. When nothing appeared on the screen, I enlarged the field of view and saw some quite interesting figures, that were not Sierpinski Gaskets. I saved the program so that I could examine it later. The next day, I did the same thing, trying again to program Sierpinski by memory. Same thing happened. I saved that program. It was different than the first one but both created incredible mutatable forms. You can see what I did with it at http://home.entouch.net/dmd/god-evol.htmhttp://home.entouch.net/dmd/god-evol.htm

HEre is the first picture from that page. YOu can see the punctuated equilibrium. Each small box is numbered according to the iteration of the program. Things change suddenly then slowly, then suddenly, then slowly.

[NeilUnreal]

One thing to keep in mind is, although computer code is similar in some ways to genetic code, the ways in which the two differ are significant.

I particular, genetic code involves a genotype to phenotype mapping that is done via a developmental phase. Among other things, this makes genetic code more robust in terms of producing viable soma, especially where the phenotypic changes come later in the development cycle.

(Among the things computer and genetic code share is a degree of modularity, although exactly how is modularity is achieved differs between the two schemes.)

-Neil

[NeilUnreal]

You can see the punctuated equilibrium.

Excellent post, Glenn. As I have recounted elsewhere, one of the trigger events that helped exorcise my own "Morton's demon" was the observation of punctuated equilibrium in a computer genetic algorithm.

-Neil

[technomage]

Another thing Mr.Reed seems not to be aware of is evolving computer code - artificial digital life

I think Mr. Reed would quibble, solely because some person wrote the first generation of the code. That's not a reasonable excuse to object, but so far it doesn't sound like there's a whole lot of "reason" to his objections anyway.

[Dr.GH]

Well, the inane analogy between computers and life ought to have been dumped a long time ago. If computers self assembled, reproduced, and were subject to natural selection, there could be some utility to the analogy. And, to conclude that there is a "master programer" on the basis of a poor analogy is just absurd.

We have most of the molecular antecedents identified, and they all follow the principles of chemistry.

[grmorton]

One thing to keep in mind is, although computer code is similar in some ways to genetic code, the ways in which the two differ are significant.

I particular, genetic code involves a genotype to phenotype mapping that is done via a developmental phase. Among other things, this makes genetic code more robust in terms of producing viable soma, especially where the phenotypic changes come later in the development cycle.

(Among the things computer and genetic code share is a degree of modularity, although exactly how is modularity is achieved differs between the two schemes.)

-Neil

Neil, you have aged terribly in your picture.