Overlapping genetic information in eukaryotes

Overlapping genetic information in eukaryotes

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In my research, I look at a lot of gene predictions / annotations. Frequently, I see loci where multiple gene models overlap. I haven't taken a systematic approach to analyzing these cases, but I do remember seeing quite a bit of variation in the direction of the overlapping genes (same vs different directions), the amount of overlap, and even the number of overlapping genes.

I know enough about gene prediction to take any computational predictions with a grain of salt--even those supported by transcript and peptide alignments. However, these cases have me thinking--does overlap of genetic information really occur in eukaryotes? I seem to remember learning (or hearing anecdotally) that it can happen in prokaryotes, and that seems to be understandable given the compactness of prokaryotic genomes. But can this happen in eukaryotes? Has this been studied, and are there cases that have been confirmed experimentally?

You might be interested in the INK4A locus (chromosome 9p), encoding both p19 and p16 genes, very close to p15. You can read a description here. All three proteins are known experimentally to exist.

Now, whether these are two different genes or the same gene with alternative splicing and start sites leading to different reading frames it's up to discussion. The point is that p19 and p16 share DNA coding sequence but not protein sequence nor function.

In general, the compactnes of genomes is a characteristic of prokaryotes, but there are several eykaryotes that have overlapping genes: many parasites and endosymbionts. The best studied of these are the fungal parasites of the phylum microsporidia and the nucleomorphs (remnant nuclei of algal endosymbionts in cryptophytes and chlorarachniophytes).

cDNA library was constructed from the microsporidian Antonospora locustae and 1,146 cDNA clones were sequenced. Here is part of the expression profile (1):

Of the 871 clones found to encode recognizable genes, 97 transcripts (11%) from 70 distinct loci encoded sequence from more than one gene (Fig. 1 A ; see also Table 1, which is published as supporting information on the PNAS web site). The polyA sites of these clones do not correspond to polyA tracts in the genome, so they are unlikely to derive from DNA contamination (see also below), but instead come from polyA RNA. In prokaryotes, polycistronic mRNAs commonly code for multiple proteins (11), but with few exceptions (12) eukaryotic mRNAs encode a single gene. A. locustae multigene transcripts encode two or three genes or gene fragments in various orientations (Fig. 1 B-I ), but they cannot all be polycistronic messages because there is no bias for genes being on the same strand.

Here is part of a review paper on the nucleomorphs genome (2):

As in other reduced genomes, the G. theta nucleomorph genome possesses a very high A+T content (75%) and gene density is extremely high: 1 gene per 977 bp and 44 genes overlap by as many as 76 nucleotides. Williams et al. (84) showed that transcription of the G. theta nucleomorph genome is affected by this compaction, with nucleomorph-derived messenger RNAs often possessing coding sequence for more than one gene, albeit with no strand bias. It appears that during the process of genome compaction, transcription regulatory elements (e.g., promoters, terminators) have moved from the intergenic spacers into the coding regions themselves (84).

I also have to point out that there are a few examples of overlapping genes in yeast: CCT6 overlaps with YDR187C and CCT8 overlaps with YJL009W (3).

  1. A high frequency of overlapping gene expression in compacted eukaryotic genomes
  2. Nucleomorph Genomes, Annual Review of Genetics
  3. The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes

3.4.1 DNA, genes and chromosomes

In prokaryotic cells, DNA molecules are short, circular and not associated with proteins.

In the nucleus of eukaryotic cells, DNA molecules are very long, linear and associated with proteins, called histones. Together a DNA molecule and its associated proteins form a chromosome.

The mitochondria and chloroplasts of eukaryotic cells also contain DNA which, like the DNA of prokaryotes, is short, circular and not associated with protein.

A gene is a base sequence of DNA that codes for:

  • the amino acid sequence of a polypeptide
  • a functional RNA (including ribosomal RNA and tRNAs).

A gene occupies a fixed position, called a locus, on a particular DNA molecule.

A sequence of three DNA bases, called a triplet, codes for a specific amino acid. The genetic code is universal, non-overlapping and degenerate.

In eukaryotes, much of the nuclear DNA does not code for polypeptides. There are, for example, non-coding multiple repeats of base sequences between genes. Even within a gene only some sequences, called exons, code for amino acid sequences. Within the gene, these exons are separated by one or more non-coding sequences, called introns.

Characteristics of the genetic code

Triplet nature

A triplet code could make a genetic code for 64 different combinations (4 X 4 X 4) genetic code and provide plenty of information in the DNA molecule to specify the placement of all 20 amino acids. When experiments were performed to crack the genetic code it was found to be a code that was triplet. These three letter codes of nucleotides (AUG, AAA, etc.) are called codons.


The code is degenerate which means that the same amino acid is coded by more than one base triplet. For example, the three amino acids arginine, alanine and leucine each have six synonymous codons.


The genetic code is nonoverlapping, i.e.,the adjacent codons do not overlap. A nonoverlapping code means that the same letter is not used for two different codons. In other words, no single base can take part in the formation of more than one codon.


There is no signal to indicate the end of one codon and the beginning of the next. The genetic code is commaless (or comma-free).


A particular codon will always code for the same amino acid. While the same amino acid can be coded by more than one codon (the code is degenerate), the same codon shall not code for two or more different amino acids (non-ambiguous).


Although the code is based on work conducted on the bacterium Escherichia coli but it is valid for other organisms. This important characteristic of the genetic code is called its universality. It means that the same sequences of 3 bases encode the same amino acids in all life forms from simple microorganisms to complex, multicelled organisms such as human beings.


The genetic code has polarity, that is, the code is always read in a fixed direction, i.e., in the 5′ → 3′ direction.

Chain Initiation Codons

The triplets AUG and GUG play double roles in E. coli. When they occur in between the two ends of a cistron (intermediate position), they code for the amino acids methionine and valine, respectively in an intermediate position in the protein molecule.

Chain Termination Codons

The 3 triplets UAA, UAG, UGA do not code for any amino acid. They were originally described as non-sense codons, as against the remaining 61 codons, which are termed as sense codons

3.4.2 DNA and protein synthesis

Opportunities for skills development

The concept of the genome as the complete set of genes in a cell and of the proteome as the full range of proteins that a cell is able to produce.

The structure of molecules of messenger RNA (mRNA) and of transfer RNA (tRNA).

Transcription as the production of mRNA from DNA. The role of RNA polymerase in joining mRNA nucleotides.

  • In prokaryotes, transcription results directly in the production of mRNA from DNA.
  • In eukaryotes, transcription results in the production of pre-mRNA this is then spliced to form mRNA.

Translation as the production of polypeptides from the sequence of codons carried by mRNA. The roles of ribosomes, tRNA and ATP.

Students should be able to:

  • relate the base sequence of nucleic acids to the amino acid sequence of polypeptides, when provided with suitable data about the genetic code
  • interpret data from experimental work investigating the role of nucleic acids.

Students will not be required to recall in written papers specific codons and the amino acids for which they code.

Genetic Code : Definition, Nature & Characteristics, genetic code table and genetic bias

Central dogma of molecular biology describes the two step process by which information in genes flow into proteins.
DNA ➞ RNA ➞ Protein
DNA to RNA by Transcription and RNA to Protein by Translation.

As the language of nucleotide sequence on mRNA is translated to language of an amino acid sequence.
Translation requires a genetic code through which information contained in nucleic acid is expressed in specific sequence of amino acid and this collection of codons as we known as Genetic codon.

The letters A,G,T,C correspond to nucleotides in DNA they are organised into codons.
For 20 Amino acid (standard) requires at least 20 codons.
• If 1 nucleotide act as a codon there will be 4 combinations.
• If 2 nucleotide act as a codon there will be (4)² = 16 combination.
• If 3 nucleotide act as a codon there will be (4)³ = 64 combination.

George Gamow postulated that 3 letter codon must be employed to encode 20 standard amino acid used by living cells for protein synthesis.

Definition :

Genetic code is a set of rules (defined by 64 triplet codons) by which information encoded in genetic material (DNA or mRNA sequences) is translocated into protein by living cells.
Codon is a set of 3 letters combination of nucleotide bases(A,G,C,T).
Genetic code defines how codons specify which amino acid will be added next during protein synthesis.

B. How is the Genetic Code 'Read' to Account for All of an Organisms' Gene?

George Gamow (a Russian Physicist working at George Washington University) was the first to propose triplet codons to encode the twenty amino acids, the simplest hypothesis to account for the colinearity of gene and protein, and for encoding 20 amino acids. One concern that was raised was whether there is enough DNA in an organism&rsquos genome to fit the all codons it needs to make all of its proteins? Assuming genomes did not have a lot of extra DNA laying around, how might genetic information be compressed into short DNA sequences in a way that is consistent with the colinearity of gene and polypeptide. One idea assumed 44 meaningless and 20 meaningful 3-base codons (one for each amino acid) and 44 meaningless codons, and that the meaningful codons in a gene (i.e., an mRNA) would be read and translated in an overlapping manner.

A code where codons overlap by one base is shown below.

You can figure out how compressed a gene could get with codons that overlapped by two bases. However, as attractive as an overlapping codon hypothesis was in achieving genomic economies, it sank of its own weight almost as soon as it was floated! If you look carefully at the example above, you can see that each succeeding amino acid would have to start with a specific base. A look back at the table of 64 triplet codons quickly shows that only one of 16 amino acids, those that begin with a C can follow the first one in the illustration. Based on amino acid sequences accumulating in the literature, virtually any amino acid could follow another in a polypeptide. Therefore, overlapping genetic codes are untenable. The genetic code must be non-overlapping!

Sidney Brenner and Frances Crick performed elegant experiments that directly demonstrated the non-overlapping genetic code. They showed that bacteria with a single base deletion in the coding region of a gene failed to make the expected protein. Likewise, deleting two bases from the gene. On the other hand, bacteria containing a mutant version of the gene in which three bases were deleted were able to make the protein. The protein it made was slightly less active than bacteria with genes with no deletions.

The next issue was whether there were only 20 meaningful codons and 44 meaningless ones. If only 20 triplets actually encoded amino acids, how would the translation machinery recognize the correct 20 codons to translate? What would prevent the translational machinery from &lsquoreading the wrong&rsquo triplets, i.e., reading an mRNA out of phase? If for example, if the translation machinery began reading an MRNA from the second or third bases of a codon, it would likely encounter a meaningless 3-base sequence in short order.

One speculation was that the code was punctuated. That is, perhaps there were the chemical equivalent of commas between the meaningful triplets. The commas would be of course, additional nucleotides. In such a punctuated code, the translation machinery would recognize the &lsquocommas&rsquo and would not translate any meaningless 3- base triplet, avoiding out-of-phase translation attempts. Of course, a code with nucleotide &lsquocommas&rsquo would increase the amount of DNA needed to specify a polypeptide by a third!

Then, Crick proposed the Commaless Genetic Code. He divided the 64 triplets into 20 meaningful codons that encoded the amino acids, and 44 meaningless ones that did not. The result was such that when the 20 meaningful codons are placed in any order, any of the triplets read in overlap would be among the 44 meaningless codons. In fact, he could arrange several different sets of 20 and 44 triplets with this property! Crick had cleverly demonstrated how to read the triplets in correct sequence without nucleotide &lsquocommas&rsquo.

As we know now, the genetic code is indeed &lsquocommaless&rsquo&hellip but not in the sense that Crick had envisioned. What&rsquos more, Thanks to the experiments described next, we know that ribosomes read the correct codons in the right order because they know exactly where to start!


The positions and sequences of each gene were obtained from the National Center for Biotechnology Information (NCBI) database (build 31 published January 15, 2003 Each locus was defined using both LocusLink and RefSeq, using gene symbols and names established by the nomenclature committee for the genome (

In the LocusLink report, symbols and names were reported under the banner ( Exons were defined as DNA sequences coding mRNA, rather than considering functions within specific genes or locations within specific genes. This definition allowed for analysis of all possible cases of overlapping. Official gene symbols and names were used as follows: For RefSeq records, symbols were assigned using the LOCUS system. If a symbol had not yet officially been assigned, an interim symbol and name were arbitrarily selected. Arbitrarily selected symbols and names are included at this website

All loci and exons registered in NCBI build 31 were examined, using the data describing the position of genes on the chromosome. The information of nucleotide sequences and the positions of each nucleotide in the whole human genome were downloaded and stored in EXCEL file format (Microsoft Corporation, Redmond, Washington). All overlapping loci and overlapping exons could be defined according to the start and end positions of each locus and exon. Data for overlapping loci were produced using data of registered loci, while the data for overlapping exons was produced using data distinct coding regions and mRNA sequences.


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Universal Genetic Code? No!

I am still reading Shadow of Oz by Dr. Wayne Rossiter, and I definitely plan to post a review of it when I am finished. However, I wanted to write a separate blog post about one point that he makes in Chapter 6, which is entitled “Biological Evolution.” He says:

To date, the National Center for Biotechnology Information (NCBI), which houses all published DNA sequences (as well as RNA and protein sequences), currently acknowledges nineteen different coding languages for DNA…

This was a shock to me. As an impressionable young student at the University of Rochester, I was taught quite definitively that there is only one code for DNA, and it is universal * . This, of course, is often cited as evidence for evolution. Consider, for example, this statement from The Biology Encyclopedia:

For almost all organisms tested, including humans, flies, yeast, and bacteria, the same codons are used to code for the same amino acids. Therefore, the genetic code is said to be universal. The universality of the genetic code strongly implies a common evolutionary origin to all organisms, even those in which the small differences have evolved. These include a few bacteria and protozoa that have a few variations, usually involving stop codons.

Dr. Rossiter points out that this isn’t anywhere close to correct, and it presents serious problems for the idea that all life descended from a single, common ancestor.

To understand the importance of Dr. Rossiter’s point, you need to know how a cell makes proteins. The basic steps of the process are illustrated in the image at the top of this post. The “recipe” for each protein is stored in DNA, and it is coded by four different nucleotide bases (abbreviated A, T, G, and C). That “recipe” is copied to a different molecule, RNA, in a process called transcription. During that process, the nucleotide base “U” is used instead of “T,” so the copy has A, U, G, and C as its four nucleotide bases. The copy then goes to the place where the proteins are actually made, which is called the ribosome. The ribosome reads the recipe in units called codons. Each codon, which consists of three nucleotide bases, specifies a particular amino acid. When the amino acids are strung together in the order given by the codons, the proper protein is made.

The genetic code tells the cell which codon specifies which amino acid. Look, for example, at the illustration at the top of the page. The first codon in the RNA “recipe” is AUG. According to the supposedly universal genetic code, those three nucleotide bases in that order are supposed to code for one specific amino acid:methionine (abbreviated as “Met” in the illustration). The next codon (CCG) is supposed to code for the amino acid proline (abbreviated as Pro). Each possible three-letter sequence (each possible codon) codes for a specific amino acid, and the collection of all those possible codons and what they code for is often called the genetic code.

Now, once again, according to The Biology Encyclopedia (and many, many other sources), the genetic code is nearly universal. Aside from a few minor exceptions, all organisms use the same genetic code, and that points strongly to the idea that all organisms evolved from a common ancestor. However, according to the NCBI, that isn’t even close to correct. There are all sorts of exceptions to this “universal” genetic code, and I would think that some of them result in serious problems for the hypothesis of evolution.

Consider, for example, the vertebrate mitochondrial code and the invertebrate mitochondrial code. In case you didn’t know, many cells actually have two sources of DNA. The main source of DNA is in the cell’s nucleus, so it is called nuclear DNA. However, the kinds of cells that make up vertebrates (animals with backbones) and invertebrates (animals without backbones) also have DNA in their mitochondria, small structures that are responsible for making most of the energy the cell uses to survive. The DNA found in mitochondria is called mitochondrial DNA.

Now, according to the hypothesis of evolution, the kinds of cells that make up vertebrates and invertebrates (called eukaryotic cells) were not the first to evolve. Instead, the kinds of cells found in bacteria (called prokaryotic cells) supposedly evolved first. Then, at a later time, one prokaryotic cell supposedly engulfed another, but the engulfed cell managed to survive. Over generations, these two cells somehow managed to start working together, and the engulfed cell became the mitochondrion for the cell that engulfed it. This is the hypothesis of endosymbiosis, and despite its many, many problems, it is the standard tale of how prokaryotic cells became eukaryotic cells.

However, if the mitochondria in invertebrates use a different genetic code from the mitochondria in vertebrates, and both of those codes are different from the “universal” genetic code, what does that tell us? It means that the eukaryotic cells that eventually evolved into invertebrates must have formed when a cell that used the “universal” code engulfed a cell that used a different code. However, the eukaryotic cells that eventually evolved into vertebrates must have formed when a cell that used the “universal” code engulfed a cell that used yet another different code. As a result, invertebrates must have evolved from one line of eukaryotic cells, while vertebrates must have evolved from a completely separate line of eukaryotic cells. But this isn’t possible, since evolution depends on vertebrates evolving from invertebrates.

Now, of course, this serious problem can be solved by assuming that while invertebrates evolved into vertebrates, their mitochondria also evolved to use a different genetic code. However, I am not really sure how that would be possible. After all, the invertebrates spent millions of years evolving, and through all those years, their mitochondrial DNA was set up based on one code. How could the code change without destroying the function of the mitochondria? At minimum, this adds another task to the long, long list of unfinished tasks necessary to explain how evolution could possibly work. Along with explaining how nuclear DNA can evolve to produce the new structures needed to change invertebrates into vertebrates, evolutionists must also explain how, at the same time, mitochondria can evolve to use a different genetic code!

In the end, it seems to me that this wide variation in the genetic code deals a serious blow to the entire hypothesis of common ancestry, at least the way it is currently constructed. Perhaps that’s why I hadn’t heard about it until reading Dr. Rossiter’s excellent book.

*Addition (4/3/2017): After speaking with a biology professor for whom I have a lot of respect, I need to make an addendum. She says that nowadays, the term “universal genetic code” doesn’t necessarily mean that every organism uses the same set of codons for the same amino acids. One could say that the genetic code is universal in the sense that all organisms use three nucleotide bases to define an amino acid, the codes can all be translated at the ribosome, etc. I still think that these alternate genetic codes argue against evolution, but it is important to note that some evolutionists use the term “universal” without implying that the codons are all the same among all organisms.
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Biological Information

In the spring of 2011, a diverse group of scientists gathered at Cornell University to discuss their research into the nature and origin of biological information. This symposium brought together experts in information theory, computer science, numerical simulation, thermodynamics, evolutionary theory, whole organism biology, developmental biology, molecular biology, genetics, physics, biophysics, mathematics, and linguistics. This volume presents new research by those invited to speak at the conference.

The contributors to this volume use their wide-ranging expertise in the area of biological information to bring fresh insights into the many explanatory difficulties associated with biological information. These authors raise major challenges to the conventional scientific wisdom, which attempts to explain all biological information exclusively in terms of the standard mutation/selection paradigm.

Several clear themes emerged from these research papers: 1) Information is indispensable to our understanding of what life is 2) Biological information is more than the material structures that embody it 3) Conventional chemical and evolutionary mechanisms seem insufficient to fully explain the labyrinth of information that is life. By exploring new perspectives on biological information, this volume seeks to expand, encourage, and enrich research into the nature and origin of biological information.

  • Session One — Information Theory & Biology: Introductory Comments (Robert J Marks II):
    • Biological Information — What is It? (Werner Gitt, Robert Compton and Jorge Fernandez)
    • A General Theory of Information Cost Incurred by Successful Search (William A Dembski, Winston Ewert and Robert J Marks II)
    • Pragmatic Information (John W Oller, Jr)
    • Limits of Chaos and Progress in Evolutionary Dynamics (William F Basener)
    • Tierra: The Character of Adaptation (Winston Ewert, William A Dembski and Robert J Marks II)
    • Multiple Overlapping Genetic Codes Profoundly Reduce the Probability of Beneficial Mutation (George Montañez, Robert J Marks II, Jorge Fernandez and John C Sanford)
    • Entropy, Evolution and Open Systems (Granville Sewell)
    • Information and Thermodynamics in Living Systems (Andy C McIntosh)
    • Not Junk After All: Non-Protein-Coding DNA Carries Extensive Biological Information (Jonathan Wells)
    • Can Purifying Natural Selection Preserve Biological Information? (Paul Gibson, John R Baumgardner, Wesley H Brewer and John C Sanford)
    • Selection Threshold Severely Constrains Capture of Beneficial Mutations (John C Sanford, John R Baumgardner and Wesley H Brewer)
    • Using Numerical Simulation to Test the “Mutation-Count” Hypothesis (Wesley H Brewer, John R Baumgardner and John C Sanford)
    • Can Synergistic Epistasis Halt Mutation Accumulation? Results from Numerical Simulation (John R Baumgardner, Wesley H Brewer and John C Sanford)
    • Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection (Chase W Nelson and John C Sanford)
    • Information Loss: Potential for Accelerating Natural Genetic Attenuation of RNA Viruses (Wesley H Brewer, Franzine D Smith and John C Sanford)
    • DNA.EXE: A Sequence Comparison between the Human Genome and Computer Code (Josiah Seaman)
    • Biocybernetics and Biosemiosis (Donald Johnson)
    • An Ode to the Code: Evidence for Fine-Tuning in the Standard Codon Table (Jed C Macosko and Amanda M Smelser)
    • A New Model of Intracellular Communication Based on Coherent, High-Frequency Vibrations in Biomolecules (L Dent)
    • Getting There First: An Evolutionary Rate Advantage for Adaptive Loss-of-Function Mutations (Michael J Behe)
    • The Membrane Code: A Carrier of Essential Biological Information That is Not Specified by DNA and is Inherited Apart from It (Jonathan Wells)
    • Explaining Metabolic Innovation: Neo-Darwinism Versus Design (Douglas D Axe and Ann K Gauger)
    • Evolution Beyond Entailing Law: The Roles of Embodied Information and Self Organization (Stuart Kauffman)
    • Towards a General Biology: Emergence of Life and Information from the Perspective of Complex Systems Dynamics (Bruce H Weber)

    Updated contents, pp & price on 10/6/2013

    Information Theory & Biology: Introductory Comments
    • Shannon Information
    • Solomonov-Kolmogorov-Chaitin Information
    • The Meaning of Information
    • Papers
    • A Final Thought
    • References
    Biological Information — What is It?

    Scientific discoveries, especially over the last six decades, have left no doubt that ‘information’ plays a central role in biology. Specialists have thus sought to study the information in biological systems using the same definitions of information as have been traditionally used in engineering, computer science, mathematics and in other disciplines. Unfortunately, all of these traditional definitions lack aspects that even non-specialists recognize as being essential attributes of information — qualities such as meaning and purpose. To remedy that deficiency, we define another type of information — Universal Information — that more accurately embodies the full measure of information. We then examine the DNA/RNA protein synthesizing system with this definition of Universal Information and conclude that Universal Information is indeed present and that it is essential for all biological life. Furthermore, other types of information, such as Mental Imaging Information, also play a key role in life. It thus seems inevitable that the biological sciences (and science in general) must consider other-than-the-traditional definitions of information if we are to answer some of the fundamental questions about life.

    A General Theory of Information Cost Incurred by Successful Search

    This paper provides a general framework for understanding targeted search. It begins by defining the search matrix, which makes explicit the sources of information that can affect search progress. The search matrix enables a search to be represented as a probability measure on the original search space. This representation facilitates tracking the information cost incurred by successful search (success being defined as finding the target). To categorize such costs, various information and efficiency measures are defined, notably, active information. Conservation of information characterizes these costs and is precisely formulated via two theorems, one restricted (proved in previous work of ours), the other general (proved for the first time here). The restricted version assumes a uniform probability search baseline, the general, an arbitrary probability search baseline. When a search with probability q of success displaces a baseline search with probability p of success where q > p , conservation of information states that raising the probability of successful search by a factor of q/p(>1) incurs an information cost of at least log (q/p) . Conservation of information shows that information, like money, obeys strict accounting principles.

    Pragmatic Information

    The goal of this paper is to define pragmatic information with a view toward measuring it. Here, pragmatic information means the content of valid signs — the key that unlocks language acquisition by babies and to human communication through language — also the content that enables biological “codes” in genetics, embryology, and immunology to work. In such systems, the inter-related layers appear to be ranked as in a hierarchy. Sounds are outranked by syllables, in turn outranked by words, and so on. In DNA, nucleotide pairs are outranked by codons, which are outranked by genes, and so on. As signs of lower rank combine to form signs of any higher rank, combinatorial “explosions” occur. With each increase in rank, the number of possible combinations grows exponentially, but the constraints on valid strings and, thus, their pragmatic value, sharpens their focus. As a result with each explosive increase in the number of possible combinations the relative proportion of meaningful ones diminishes. Consequently, random processes of forming strings or changing them must tend increasingly toward meaninglessness (invalid and nonviable) strings. The consequent outcome of random mutations is mortality of individuals and in deep time an increasing number of disorders, diseases, and the eventual extinction of populations.

    Limits of Chaos and Progress in Evolutionary Dynamics

    There are a number of standard models for the evolutionary process of mutation and selection as a mathematical dynamical system on a fitness space. We apply basic topology and dynamical systems results to prove that every such evolutionary dynamical system with a finite spatial domain is asymptotic to a recurrent orbit to an observer the system will appear to repeat a known state infinitely often. In a mathematical evolutionary dynamical system driven by increasing fitness, the system will reach a point after which there is not observable increase in fitness.

    Tierra: The Character of Adaptation

    Tierra is a digital simulation of evolution for which the stated goal was the development of open-ended complexity and a digital “Cambrian Explosion.” However, Tierra failed to produce such a result. A closer inspection “ Tierran evolution's adaptations show very few instances of adaptation through the production of new information. Instead, most changes result from removing or rearranging the existing pieces within a Tierra program. The open-ended development of complexity depends on the ability to generate new information, but this is precisely what Tierra struggles to do. The character of Tierran adaptation does not allow for open-ended complexity but is similar to the character of adaptations found in the biological world.

    Multiple Overlapping Genetic Codes Profoundly Reduce the Probability of Beneficial Mutation

    There is growing evidence that much of the DNA in higher genomes is poly-functional, with the same nucleotide contributing to more than one type of code. Such poly-functional DNA should logically be multiply-constrained in terms of the probability of sequence improvement via random mutation. We describe a model of this relationship, which relates the degree of poly-functionality and the degree of constraint on mutational improvement. We show that: a) the probability of beneficial mutation is inversely related to the degree that a sequence is already optimized for a given code b) the probability of beneficial mutation drastically diminishes as the number of overlapping codes increases. The growing evidence for a high degree of optimization in biological systems, and the growing evidence for multiple levels of poly-functionality within DNA, both suggest that mutations that are unambiguously beneficial must be especially rare. The theoretical scarcity of beneficial mutations is compounded by the fact that most of the beneficial mutations that do arise should confer extremely small increments of improvement in terms of total biological function. This makes such mutations invisible to natural selection. Beneficial mutations that are below a population's selection threshold are effectively neutral in terms of selection, and so should be entirely unproductive from an evolutionary perspective. We conclude that beneficial mutations that are unambiguous (not deleterious at any level), and useful (subject to natural selection), should be extremely rare.

    Entropy, Evolution and Open Systems

    It is commonly argued that the spectacular increase in order which has occurred on Earth is consistent with the second law of thermodynamics because the Earth is not an isolated system, and anything can happen in a non-isolated system as long as the entropy increases outside the system compensate the entropy decreases inside the system. However, if we define “X-entropy” to be the entropy associated with any diffusing component X (for example, X might be heat), and, since entropy measures disorder, “X-order” to be the negative of X-entropy, a closer look at the equations for entropy change shows that they not only say that the X-order cannot increase in an isolated system, but that they also say that in a non-isolated system the X-order cannot increase faster than it is imported through the boundary. Thus the equations for entropy change do not support the illogical “compensation” idea instead, they illustrate the tautology that “if an increase in order is extremely improbable when a system is isolated, it is still extremely improbable when the system is open, unless something is entering (or leaving) which makes it not extremely improbable.” Thus unless we are willing to argue that the influx of solar energy into the Earth makes the appearance of spaceships, computers and the Internet not extremely improbable, we have to conclude that at least the basic principle behind the second law has in fact been violated here.

    Information and Thermodynamics in Living Systems

    Are there laws of information exchange? And how do the principles of thermodynamics connect with the communication of information?

    We consider first the concept of information and examine the various alternatives for its definition. The reductionist approach has been to regard information as arising out of matter and energy. In such an approach, coded information systems such as DNA are regarded as accidental in terms of the origin of life, and it is argued that these then led to the evolution of all life forms as a process of increasing complexity by natural selection operating on mutations on these first forms of life. However scientists in the discipline of thermodynamics have long been aware that organisational systems are inherently systems with low local entropy, and have argued that the only way to have consistency with an evolutionary model of the universe and common descent of all life forms is to posit a flow of low entropy into the earth's environment and in this second approach they suggest that islands of low entropy form organisational structures found in living systems.

    A third alternative proposes that information is in fact non-material and that the coded information systems (such as, but not restricted to the coding of DNA in all living systems) is not defined at all by the biochemistry or physics of the molecules used to store the data. Rather than matter and energy defining the information sitting on the polymers of life, this approach posits that the reverse is in fact the case. Information has its definition outside the matter and energy on which it sits, and furthermore constrains it to operate in a highly non-equilibrium thermodynamic environment. This proposal resolves the thermodynamic issues and invokes the correct paradigm for understanding the vital area of thermodynamic/organisational interactions, which despite the efforts from alternative paradigms has not given a satisfactory explanation of the way information in systems operates.

    Starting from the paradigm of information being defined by non-material arrangement and coding, one can then postulate the idea of laws of information exchange which have some parallels with the laws of thermodynamics which undergird such an approach. These issues are explored tentatively in this paper, and lay the groundwork for further investigative study.

    Biological Information and Genetic Theory: Introductory Comments

    In the 21 st century, biological information has become the over-arching theme which unifies the life sciences. In the 19 th century, Charles Darwin and his colleagues did not yet have the notion of biological information. Indeed Darwin completely misunderstood the nature of inheritance, which he pictured to be Lamarckian in nature. One of Darwin's contemporaries, Gregor Mendel, discovered that the determinants of certain biological traits are transmitted from generation to generation in discrete packages (this work was ignored for a generation). Mendel probably had some vague notion that these genetic packages somehow might contain a very simple type of “biological information”. But he could never have guessed that these genetic units which he observed were actually precisely-specified instructions, encoded by language, with each gene being comparable in complexity to a book. When the early population geneticists developed their models, they employed over-simplified mathematical models to try to describe their understanding of genetic change, but at that time genes were considered to be merely “beads on a string.”…

    Not Junk After All: Non-Protein-Coding DNA Carries Extensive Biological Information

    In the 1950s Francis Crick formulated the Central Dogma of molecular biology, which states (in effect) that DNA makes RNA makes protein makes us. By 1970, however, biologists knew that the vast majority of our genome does not encode proteins, and the non-protein-coding fraction became known as “junk DNA.” Yet data from recent genome projects show that most nuclear DNA is transcribed into RNAs, many of which perform important functions in cells and tissues. Like protein-coding DNA, non-protein-coding regions carry multiple overlapping codes that profoundly affect gene expression and other cellular processes. Although there are still many gaps in our understanding, new functions of non-protein-coding DNA are being reported every month. Clearly, the notion of “junk DNA” is obsolete, and the amount of biological information in the genome far exceeds the information in protein-coding regions.

    Can Purifying Natural Selection Preserve Biological Information?

    Most deleterious mutations have very slight effects on total fitness, and it has become clear that below a certain fitness effect threshold, such low-impact mutations fail to respond to natural selection. The existence of such a selection threshold suggests that many low-impact deleterious mutations should accumulate continuously, resulting in relentless erosion of genetic information. In this paper, we use numerical simulation to examine this problem of selection threshold.

    The objective of this research was to investigate the effect of various biological factors individually and jointly on mutation accumulation in a model human population. For this purpose, we used a recently-developed, biologically-realistic numerical simulation program, Mendel's Accountant. This program introduces new mutations into the population every generation and tracks each mutation through the processes of recombination, gamete formation, mating, and transmission to the new offspring. This method tracks which individuals survive to reproduce after selection, and records the transmission of each surviving mutation every generation. This allows a detailed mechanistic accounting of each mutation that enters and leaves the population over the course of many generations. We term this type of analysis genetic accounting.

    Across all reasonable parameters settings, we observed that high impact mutations were selected away with very high efficiency, while very low impact mutations accumulated just as if there was no selection operating. There was always a large transitional zone, wherein mutations with intermediate fitness effects accumulated continuously, but at a lower rate than would occur in the absence of selection. To characterize the accumulation of mutations of different fitness effect we developed a new statistic, selection threshold (STd), which is an empirically determined value for a given population. A population's selection threshold is defined as that fitness effect wherein deleterious mutations are accumulating at exactly half the rate expected in the absence of selection. This threshold is mid-way between entirely selectable, and entirely unselectable, mutation effects.

    Our investigations reveal that under a very wide range of parameter values, selection thresholds for deleterious mutations are surprisingly high. Our analyses of the selection threshold problem indicate that given even modest levels of noise affecting either the genotype-phenotype relationship or the genotypic fitness-survival-reproduction relationship, accumulation of low-impact mutations continually degrades fitness, and this degradation is far more serious than has been previously acknowledged. Simulations based on recently published values for mutation rate and effect-distribution in humans show a steady decline in fitness that is not even halted by extremely intense selection pressure (12 offspring per female, 10 selectively removed). Indeed, we find that under most realistic circumstances, the large majority of harmful mutations are essentially unaffected by natural selection and continue to accumulate unhindered. This finding has major theoretical implications and raises the question, “What mechanism can preserve the many low-impact nucleotide positions that constitute most of the information within a genome?”

    Selection Threshold Severely Constrains Capture of Beneficial Mutations

    Background. In a companion paper, careful numerical simulation was used to demonstrate that there is a quantifiable selection threshold, below which low-impact deleterious mutations escape purifying selection and, therefore, accumulate without limit. In that study we developed the statistic, STd , which is the mid-point of the transition zone between selectable and un-selectable deleterious mutations. We showed that under most natural circumstances, STd values are surprisingly high, such that the large majority of all deleterious mutations are un-selectable. Does a similar selection threshold exist for beneficial mutations?

    Methods. As in our companion paper we here employ what we describe as genetic accounting to quantify the selection threshold ( STb ) for beneficial mutations, and we study how various biological factors combine to determine its value.

    Results. In all experiments that employ biologically reasonable parameters, we observe high STb values and a general failure of selection to preferentially amplify the large majority of beneficial mutations. High-impact beneficial mutations strongly interfere with selection for or against all low-impact mutations.

    Conclusions. A selection threshold exists for beneficial mutations similar in magnitude to the selection threshold for deleterious ones, but the dynamics of that threshold are different. Our results suggest that for higher eukaryotes, minimal values for STb are in the range of 10 −4 to 10 −3 . It appears very likely that most functional nucleotides in a large genome have fractional contributions to fitness much smaller than this. This means that, given our current understanding of how natural selection operates, we cannot explain the origin of the typical functional nucleotide.

    Using Numerical Simulation to Test the “Mutation-Count” Hypothesis

    There is now abundant evidence that the continuous accumulation of deleterious mutations within natural populations poses a major problem for neo-Darwinian theory. It has been proposed that a viable evolutionary mechanism for halting the accumulation of deleterious mutations might arise if fitness depends primarily on an individual's “mutation-count”. In this paper the hypothetical “ mutation-count mechanism” (MCM) is tested using numerical simulation, to determine the viability of the hypothesis and to determine what biological factors affect the relative efficacy of this mechanism.

    The MCM is shown to be very strong when given all the following un-natural conditions: all mutations have an equal effect, low environmental variance, and full truncation selection. Conversely, the MCM effect essentially disappears given any of the following natural conditions: asexual reproduction, or probability selection, or accumulating mutations having a natural distribution of fitness effects covering several orders of magnitude. Realistic levels of environmental variance can also abolish or greatly diminish the MCM effect.

    Equal mutation effects when combined with partial truncation (quasi-truncation) can create a moderate MCM effect, but this disappears in the presence of less uniform mutation effects and reasonable levels of environmental variance.

    MCM does not appear to occur under most biologically realistic conditions, and so is not a generally applicable evolutionary mechanism. MCM is not generally capable of stopping deleterious mutation accumulation in most natural populations.

    Can Synergistic Epistasis Halt Mutation Accumulation? Results from Numerical Simulation

    The process of deleterious mutation accumulation is influenced by numerous biological factors, including the way in which the accumulating mutations interact with one another. The phenomenon of negative mutation-to-mutation interactions is known as synergistic epistasis (SE). It is widely believed that SE should enhance selective elimination of mutations and thereby diminish the problem of genetic degeneration. We apply numerical simulation to test this commonly expressed assertion.

    We find that under biologically realistic conditions, synergistic epistasis exerts little to no discernible influence on mutation accumulation and genetic degeneration. When the synergistic effect is greatly exaggerated, mutation accumulation is not significantly affected, but genetic degeneration accelerates markedly. As the synergistic effect is exaggerated still more, degeneration becomes catastrophic and leads to rapid extinction. Even when conditions are optimized to enhance the SE effect, selection efficiency against deleterious mutation accumulation is not appreciably influenced.

    We also evaluated SE using parameters that result in extreme and artificially high selection efficiency (truncation selection and perfect genotypic fitness heritability). Even under these conditions, synergistic epistasis causes accelerated degeneration and only minor reductions in the rate of mutation accumulation.

    When we included the effect of linkage within chromosomal segments in our SE analyses, it made degeneration still worse and even interfered with mutation elimination. Our results therefore strongly suggest that commonly held perceptions concerning the role of synergistic epistasis in halting mutation accumulation are not correct.

    Computational Evolution Experiments Reveal a Net Loss of Genetic Information Despite Selection

    Computational evolution experiments using the population genetics simulation Mendel's Accountant have suggested that deleterious mutation accumulation may pose a threat to the long-term survival of many biological species. By contrast, experiments using the program Avida have suggested that purifying selection is extremely effective and that novel genetic information can arise via selection for high-impact beneficial mutations. The present study shows that these approaches yield seemingly contradictory results only because of disparate parameter settings. Both agree when similar settings are used, and both reveal a net loss of genetic information under biologically relevant conditions. Further, both approaches establish the existence of three potentially prohibitive barriers to the evolution of novel genetic information: (1) the selection threshold and resulting genetic decay (2) the waiting time to beneficial mutation and (3) the pressure of reductive evolution, i.e., the selective pressure to shrink the genome and disable unused functions. The adequacy of mutation and natural selection for producing and sustaining novel genetic information cannot be properly assessed without a careful study of these issues.

    Information Loss: Potential for Accelerating Natural Genetic Attenuation of RNA Viruses

    Loss of information is not always bad. In this paper, we investigate the potential for accelerating the genetic degeneration of RNA viruses as a means for slowing/containing pandemics. It has previously been shown that RNA viruses are vulnerable to lethal mutagenesis (the concept of inducing mutational degeneration in a given pathogen). This has led to the use of lethal mutagenesis as a clinical treatment for eradicating RNA virus from a given infected patient. The present study uses numerical simulation to explore the concept of accelerated mutagenesis as a way to enhance natural genetic attenuation of RNA viral strains at the epidemiological level. This concept is potentially relevant to improved management of pandemics, and may be applicable in certain instances where eradication of certain diseases is sought.

    We propose that mutation accumulation is a major factor in the natural attenuation of pathogenic strains of RNA viruses, and that this may contribute to the disappearance of old pathogenic strains and natural cessation of pandemics. We use a numerical simulation program, Mendel's Accountant, to support this model and determine the primary factors that can enhance such degeneration. Our experiments suggest that natural genetic attenuation can be greatly enhanced by implementing three practices. (1) Strategic use of antiviral pharmaceuticals that increase RNA mutagenesis. (2) Improved hygiene to reduce inoculum levels and hence increase genetic bottlenecking. (3) Strategic use of broad-spectrum vaccines that induce partial immunity. In combination, these three practices should profoundly accelerate loss of biological information (attenuation) in RNA viruses.

    DNA.EXE: A Sequence Comparison between the Human Genome and Computer Code

    This study presents evidence that executable computer programs and human genomes contain similar patterns of repetitive code. When viewed with sequence visualization tools, these similarities are both striking and pervasive. The primary similarities are listed in order of scale: (1) homopolymers, (2) tandem repeats, (3) distributed repeats, (4) isochores, (5) and entire chromosome/file organization. Most strikingly, data visualization reveals that executable codes regularly make extensive use of tandem repeats which exhibit similar visual patterns as seen in higher genomes. In biology these tandem repeat patterns are normally attributed to replication errors, insertions, deletions, and substitutions. Similarly, on a larger scale, executable codes display regions with different ratios of 1's and 0's which parallel the isochore patterns within chromosomes, caused by local variation in the number of A/T vs. G/C. Further, blocks of data are stored at the beginning or end of a file, while the primary instructions occupy the middle of a file. This creates the same organizational patterns observed in human chromosome arms, where repetitive sequences are grouped near the telomeres and centromeres.

    I propose that these similarities can be explained by universal constraints in efficient information encoding and execution. The genome may be viewed as the executable program that encodes life. Given the evidence that computer programs and genomes use many of the same patterns of organization, despite having very different context, it should be informative to explore the ways in which knowledge of computer architecture can be applied to biology and vice versa.

    Biocybernetics and Biosemiosis

    Biocybernetics is the study of life's hardware and software systems, which control the chemistry and physics of all of life's processes, including metabolism, manufacturing, control, and feedback. Unlike chemistry and physics, which are physical sciences, biology is an information science since what differentiates biology from complex organic chemistry is its information processing systems. Semiosis connects two independent worlds of signs and meaning by the conventional rules of a code. Many arbitrary coded symbol systems, with over 20 discovered in the past decade, play very important roles in communicating information between life's components. Life's networked computers and computer programs instantiated into DNA and RNA memory devices are discussed. A prescriptive algorithm can be implemented in either hardware or software. The “artificial genome” manufactured by Venter et al. demonstrates experimentally the reality of computer hardware and software in each cell.

    Any serious origin-of-life or origin-of-species scenario must explain the origin of the required biological information. It is argued that each protein arises as the result of the execution of a genuine computer program. The creation of a functional protein via the mutation/selection paradigm lacks support from information science. Those who understand the reality of bioinformation, especially the prescriptive information of biocybernetics, will be able to incorporate that understanding into new models that will lead to a more complete understanding of life.

    Theoretical Molecular Biology: Introductory Comments

    Biological information must be expressed to be consequential. In the past half century, science has discovered that expression often takes the form of sophisticated molecular machinery. Information resides in the very shape of the machinery itself, as well as in the instructions to build the machinery, to regulate it, to allow separate systems to communicate with it, and more. In all these cases the information must be physically instantiated to be effective. This section focuses on systems that are known, or speculated, to instantiate information, and how they may be affected by evolutionary forces…

    An Ode to the Code: Evidence for Fine-Tuning in the Standard Codon Table

    The Standard Codon Table (SCT) records the correlation observed in nature between the complete set of 64 trinucleotide codons and the 20 amino acids plus 3 nonsense (i.e. stop or termination) signals. This table was called a frozen accident by Francis Crick, yet current evidence points to optimization that minimizes harmful effects of mutations and mistranslations while maximizing the encoding of multiple messages into a single sequence. For example, a recent article with the running title “The best of all possible codes?” concluded that “evidence is clear” for the optimized nature of the SCT, and another study found that difficult-to-encode secondary signals are minimized in the SCT. Additionally, the initiating amino acid methionine has been found to minimize the nascent peptide chain's barrier to exit the ribosome. Moreover, the symmetry in the SCT between 4- fold-synonymous and <4-fold synonymous codons has been explained in terms of minimizing mistranslation. In this paper, the hypothesis that the finely tuned optimization of the SCT originates in external intelligence is compared to the hypothesis that its fine tuning is due to the adaptive selection of earlier codes. It is concluded that, in the absence of metaphysical biases against this hypothesis, external intelligence better explains the origin of the SCT. Additionally, this hypothesis prompts lines of inquiry that, 50 years ago, would have accelerated the discovery of the now-known features of the SCT and that, today, can lead to new discoveries.

    A New Model of Intracellular Communication Based on Coherent, High-Frequency Vibrations in Biomolecules

    Chemistry has been the ruling paradigm for understanding the communication network that integrates a living cell. However, biochemistry alone is insufficient to explain how widely-separated biomolecules locate and move toward one another with accuracy and speed. We propose a new model wherein cytoplasmic motion is vibrationally-directed due to a community of oscillating biomolecules. DNA vibrations have been predicted in the 2-GHz range, thus we used high-frequency laser-Doppler vibrometry to test the hypothesis that resonance-driven molecular motion would be detectable as picometer surface displacements in live onion epidermal cells and fish eggs but would be absent in dead cells. Although, no surface vibrations were detected under these conditions, we discuss implications for the vibrational model of intracellular communication and suggest future experiments.

    Getting There First: An Evolutionary Rate Advantage for Adaptive Loss-of-Function Mutations

    Over the course of evolution organisms have adapted to their environments by mutating to gain new functions or to lose pre-existing ones. Because adaptation can occur by either of these modes, it is of basic interest to assess under what, if any, evolutionary circumstances one of them may predominate. Since mutation occurs at the molecular level, one must look there to discern if an adaptation involves gain- or loss-of-function. Here I present a simple, deterministic model for the occurrence and spread of adaptive gain-of-function versus loss-of-function mutations, and compare the results to laboratory evolution experiments and studies of evolution in nature. The results demonstrate that loss-of-function mutations generally have an intrinsic evolutionary rate advantage over gain-of-function mutations, but that the advantage depends radically on population size, ratio of selection coefficients of competing adaptive mutations, and ratio of the mutation rates to the adaptive states.

    The Membrane Code: A Carrier of Essential Biological Information That Is Not Specified by DNA and Is Inherited Apart from It

    According to the most widely held modern version of Darwin's theory, DNA mutations can supply raw materials for morphological evolution because they alter a genetic program that controls embryo development. Yet a genetic program is not sufficient for embryogenesis: biological information outside of DNA is needed to specify the body plan of the embryo and much of its subsequent development. Some of that information is in cell membrane patterns, which contain a two-dimensional code mediated by proteins and carbohydrates. These molecules specify targets for morphogenetic determinants in the cytoplasm, generate endogenous electric fields that provide spatial coordinates for embryo development, regulate intracellular signaling, and participate in cell–cell interactions. Although the individual membrane molecules are at least partly specified by DNA sequences, their two-dimensional patterns are not. Furthermore, membrane patterns can be inherited independently of the DNA. I review some of the evidence for the membrane code and argue that it has important implications for modern evolutionary theory.

    Explaining Metabolic Innovation: Neo-Darwinism versus Design

    Like all life, bacterial life depends on a complex, integrated network of precise metabolic processes. These processes are carried out by more than a thousand enzymes — genetically encoded proteins with information-rich three-dimensional structures that catalyze specific chemical reactions. Can neo-Darwinian theory explain the origin of this network of enzymes that orchestrates metabolic complexity? Building on previous experimental and theoretical work, we argue here that it cannot. But instead of merely listing the theory's shortcomings, we attempt to construct a full and coherent picture of how it has failed to explain metabolic innovation, from the level of single enzymes all the way up to the network of enzymatic pathways that composes metabolism as a whole. Then, from this critical synthesis we identify six key principles of a new theory of biological innovation. Although these principles only hint at the substance of the new theory, they show clearly that it will be strikingly unlike neo-Darwinism. Whereas the old theory focuses on the simple material processes of mutation and selection in the hope that these can drive innovation, the new one focuses on innovation itself — on the concepts that guide effective designs. Consequently, the new theory will look more like the systematic concepts of an engineering discipline than a set of causal laws.

    Biological Information and Self-Organizational Complexity Theory: Introductory Comments

    No discussion of new perspectives on biological information would be complete without consideration of the anti-reductionist approach of the self-organizational school of thought. The reductionist approach focuses on systematically taking apart complex systems and analyzing their individual components, seeking to explain the behavior of the whole in terms of its parts. This strategy has been very fruitful and such research undoubtedly will continue, but, like the intelligent design scientists and researchers exemplified by the editors and other contributors to this volume, self-organizational theorists believe that new theoretical approaches are necessary to understand the hierarchically integrated information networks that undergird morphogenesis in developmental biology and evolution. How do systems of genes and proteins integrate into holistic information structures? How do dynamic organelle structures form in cells? What controls cell growth, division and differentiation in organisms? How is genomic information regulated in the construction of an organism? How do selective environmental pressures integrate through time with organismal development to affect the evolution of species? How do integrated ecosystems form and evolve? Both self-organizational theorists and intelligent design (ID) theorists believe that natural selection operating on random genetic mutation is an insufficient basis on which to explain the origins of biological complexity and irrelevant to the origin of life. ID theorists also believe that the self-organizational capacities of physical systems are limited, falling far short of the order we observe, so the ultimate source of information for the origin of life and hierarchically integrated morphogenesis in both organismal development and speciation must be extrinsic to biological systems and their physical environments. In contrast, self-organizational researchers argue that global pattern development, including the highly complex hierarchical information structures characteristic of life, can emerge solely from the interactions of lower-level components and part whole dynamics without ultimate or proximate goal-directed input. Whether biological information is somehow self-originating is thus a central point of disagreement between intelligent design theorists and self-organizational complexity theorists…

    Evolution Beyond Entailing Law: The Roles of Embodied Information and Self Organization

    It is argued that no law entails the evolution of the biosphere. Biological evolution rests on both quantum random and classical non-random natural selection and whole-part interactions that render the sample space of adjacent biological possibilities unknowable. This would seem to create an insurmountable problem for intelligent design in biology. Nonetheless, the evolution of ensembles of interacting systems can be modeled by statistical laws that have strong self-organizational properties. Some compelling examples modeling evolutionary self-organization in biology are presented and it is concluded that a new science of order and organization beyond entailing law is required.

    Towards a General Biology: Emergence of Life and Information from the Perspective of Complex Systems Dynamics

    I argue that Darwinism is best described as a research tradition in which specific theories of how natural selection acts to produce common descent and evolutionary change are instantiated by specific dynamical assumptions. The current Darwinian research program is the genetical theory of natural selection, or the Modern Evolutionary Synthesis. Presently, however, there is ferment in the Darwinian Research Tradition as new knowledge from molecular and developmental biology, together with the deployment of complex systems dynamics, suggests that an expanded and extended evolutionary synthesis is possible, one that could be particularly robust in explaining the emergence of evolutionary novelties and even of life itself. Critics of Darwinism need to address such theoretical advances and not just respond to earlier versions of the research tradition.

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