Evaluating the complexity of a living organism by its algorithmic complexity
One of the greatest scientific achievements of the last century was the understanding of life in terms of information. We know today that the information for synthesizing the molecules that allow organisms to survive and replicate is encoded in the DNA. In the cell, DNA is copied to messenger RNA, and triplet codons in the messenger RNA are decoded in the process of translation to synthesize polymers of the natural 20 amino acids.
Humans have been intrigued by the origin and mechanisms underlying complexity in nature coming from information contained in repositories such as the DNA. Darwin’s theory of evolution suggests that this complexity could evolve by natural selection acting successively on numerous small, heritable modifications.
Darwin’s theory represents a great leap forward in our understanding of the fundamental processes behind life. However, there is a tendency to assume that evolution os the sole factor in designing nature while it may not actually be the main driving force behind the complexity of living organisms [If you wish to know more about the theory of evolution by means of natural selection, three respectable British institutions have set up special websites in celebration of Darwin’s 200th. anniversary: the University of Cambridge (with the original scanned text and even an audio version in mp3 format), the Open University and the BBC].
Nature seems to use a specific toolkit of body features rather than totally random shapes. Like units of Lego, Nature assembles its forms from a limited set of elements. For example, despite the variety of living forms on the Earth, they do all seem to have a front-to-back line down the center of the body, and extremities (if any) on the sides, from flies who have a head at one end and a tail at the other, to worms, snakes and humans. Despite the randomness that may undermine any shared regularity among all animals in combinatoric terms, on a certain level, from a certain perspective, we are all similar in shape and features. Why didn’t evolution attempt other, completely different forms? And if it did, why were so few of them successful? Given the improbability of several other shapes having been put into circulation without any of them winning out save the ones we all know, we could conclude that evolution never did attempt such a path, instead keeping to a small pool of tried and tested basic units whose survival has never been in jeopardy. There are some symmetries and general features that many animals share (more than can be explained by inheritance) that are not so easily explained in purely evolutionist terms. A remarkable example is the resemblance of all animals in their embryonic phase.
Two teams of biologists (Walter Jakob Gehring and colleagues at the University of Basel, Switzerland, and Matthew Scott and Amy Weiner working with Thomas Kaufman at Indiana University, Bloomington) seem to have independently discovered toolkits that Nature appears to use that they have called homeobox containing genes.
This discovery indicates that organisms use a set of very simple rules passed along to them (thus reducing the amount of randomness involved) to build a wide variety of forms from just a few basic possible body parts. To oversimplify somewhat, one can for instance imagine being able to copy/paste a code segment (the homeobox) and cause a leg to grow in the place where an antenna would normally be in an ant.
This begins to sound much more like the footprint of computation rather than a special feature characterizing life, since it turns out that a few simple rules are responsible for the assembly of complex parts. Moreoever, this is consonant with what in Wolfram’s scheme of things life’s guiding force is said to be, viz. computation. And with what Chaitin has proposed as an algorithmic approach to life and evolution, as well as with my own research, which is an attempt to discover Nature’s basic hidden algorithmic nature. All the operations involved in the replication process of organisms– replacing, copying, appending, joining, splitting–would seem to suggest the algorithmic nature of the process itself. A computational process.
Based on my own research interests it is my strong belief that though by no means wrong, Darwin’s theory of evolution belongs within a larger theory of information and computation, according to which life has managed to speed up its rate of change by channeling information efficiently between generations, together with a rich exchange of information with the outside by a process that while seemingly random, is in fact the consequence of interaction with other algorithmic processes.
Think a bit further about it. Evolution seems deeply connected to biology on Earth, but as part of a larger computation theory it might be applied anywhere in the universe just as the laws of physics do. Evolution may be formulated and explained as a problem of information transmission and channeling, pure communication between 2 points in time. If you want to efficiently gather and transmit information it may turn out that biological evolution may be not the cause but the consequence.
The theory of algorithmic information (or simply AIT) on the other hand does not require a random initial configuration (unfortunately perhaps, nor any divine intervention) to have a program, when run, produce complicated output. This is in keeping with Wolfram’s finding that all over the computational universe there are simple programs with simple inputs generating complex output, what in NKS terms is called ‘intrinsic randomness’, yet is purely deterministic. Nor does AIT require the introduction of randomness during the computation itself. In other words, it seems that randomness plays no necessary role in producing complex organisms. Evolution seems to underlie change, its pace and direction, but it does not seem to constitute the driving force behind life.
Evolution seems to be taking advantage of the algorithmic properties of living systems to fabricate new forms of life. To facilitate understanding of these body patterns the University of Utah has set up an illustrative website. Incidentally, this genetic toolkit based on the homeobox concept is surprisingly well captured in the Spore video game.
In a recent article Greg Chaitin has proposed (Speculations on biology, information and complexity) that some of the properties of DNA and the accumulation of information in DNA may be better explained from a software perspective, as a computer program in constant development. When writing software, subroutines are used here and there all the time, and one usually creates an extra module or patch rather than rewrite a subroutine from scratch. This may correspond to what we see in DNA as redundant sections and ‘unused’ sections.
In Chaitin’s opinion, DNA is essentially a programming language for building an organism and then running that organism. One may therefore be able to characterize the complexity of an organism by measuring the program-size complexity of its DNA. This seems to work well for the length of DNA, since the longest known sequence of DNA belongs to what is certainly the most sophisticated organism on this planet, i.e. homo sapiens.
Chaitin proposes the following analogy:
program -> COMPUTER -> output
DNA -> DEVELOPMENT/PREGNANCY -> organism
However, we encounter problems when attempting to view the process of animal replication in the same algorithmic terms. If, as the sophistication of homo sapiens would suggest, human DNA is the most complex repository of information, and given that DNA represents the shortest encoding capable of reproducing the organism itself, we would expect the replication runtime of human DNA to be of the same order relative to other animals’ replication times. But this is not the case. A gestation period table is available here. So what are we to make of the fact that the right complexity measure for living beings (the logical depth of an object as the actual measure of the organizational complexity of a living organism) does not produce the expected gestation times? One would expect the human gestation period to be the longest, but it is not.
Charles Bennett defined the logical depth of an object as the time required by a universal computer to produce the object from its shortest description, i.e. the decompression time taken by the DNA from the fertilized egg of an animal (seen as a universal computer) to produce another organism of the same type. There seems to be more at stake, however, when trying to apply the concept to Chaitin’s replication analogy– issues ranging from when to determine the end of the replication (the gestation period?), to better times to give birth, to gestation times inherited from ancestral species, to the average size of organisms (elephants and giraffes seem to have the longest periods). Some hypotheses on period differences can be found here for example.
If living organisms can be characterized in algorithmic terms as we think they can, we should be able to introduce all these variables and still get the expected values for the complexity measurement of an organism– seen as a computer program–reproducing another organism from its shortest encoding (the DNA being an approximation of it). A complete model encompassing the theory of evolution has yet to emerge. It seems to be on the horizon of AIT, as another application to biology, one that provides a mathematical explanation of life.
- So far, what we know is that the DNA is the place where the information for replicating an animal is to be found. What’s being proposed above is that the information content in the DNA can be actually effectively approximated by means of its program-size complexity and logical depth to define a measure of the complexity of an organism. If one can quantify these values one could, for example, actually quantify an evolutionary step in mathematical terms. This would represent a first step toward encompassing Darwin’s theory of evolution within an algorithmic mathematical theory of life. Evolution is not usually seen as part of a computational theory, but as a special feature of life. The above suggests otherwise.
- Randomness has hitherto been thought to play a major role in the evolution of species, as it is mutation that drives the evolutionary process. But I suggest that this is not the case. Rather I suggest that what appears to be random is actually part of a deterministic computation, which means that randomness plays no significant part in the process, while computation does.
- Finally, evolution has hitherto been thought of as a process that advances by very small steps, rather than one that is capable of quickly building over blocks of code, as it might be actually the case. This new understanding favors the computational view I am putting forward here as playing a main role in the process of life, because it is based on what in software technology is the practice of a subroutine orientation programming paradigm: code reuse.