@ You know

I have no idea on your motives, but I will err on the good side and assume you are full of hope and positive energy, and hope to discuss some things you believe are all talking points. Unfortunately, many in here have seen the same fallacies too many times.

I humbly suggest that before you open a can or worms, check it out first. The "watchmaker fallacy" is one. And if you propose Pascal's wager, make sure you are wearing a helmet and jocksrap.

You know, you wrote, “I’m sorry man but Historians agree (believers and non believers) that Jesus lived, died and was resurrected”

Please name ONE non believer who agrees that jesus was resurrected.

And now, it's time to flush this piece of creationist drivel down the toilet, viz:

DNA itself proves a creator

Oh wait, there are numerous scientific papers, covering not only the origin of nucleic acids via testable natural processes (apparently our latest creationist has forgotten the elementary fact that DNA is a chemical molecule, and therefore subject to the usual rules of chemical reactions with regard to synthesis and its interactions), but covering in addition, wait for it, the evolvability of the genetic code. Let's take a look at some of those papers, shall we?

Here's a little list of relevant citations from said literature (and be advised, a LOT more papers in this vein have been added, since I first compiled this list way back in 2011):

[1] A Co-Evolution Theory Of The Genetic Code by J. Tze-Fei Wong, Proceedings of the National Academy of Sciences of the USA, 72(5): 1909-1912 (May 1975)

[2] A Mechanism For The Association Of Amino Acids With Their Codons And The Origin Of The Genetic Code by Shelley D. Copley, Eric Smith & Harold J. Morowitz, Proceedings of the National Academy of Sciences of the USA, 102(12): 4442-4447 (22nd March 2005)

[3] An Expanded Genetic Code With A Functional Quadruplet Codon by J. Christopher Anderson, Ning Wu, Stephen W. Santoro, Vishva Lakshman, David S. King & Peter G. Schultz, Proceedings of the National Academy of Sciences of the USA, 101(20): 7566-7571 (18th May 2004)

[4] Collective Evolution And The Genetic Code by Kalin Vetsigian, Carl Woese and Nigel Goldenfeld, Proceedings of the National Academy of Sciences of the USA, 103(28): 10696-10701 (11th July 2006)

[5] Emergence Of A Code In The Polymerization Of Amino Acids Along RNA Templates by Jean Lehmann, Michael Cibils & Albert Libchaber, PLoS One, 4(6): e5773 (3rd June 2009) DOI:10.1371/journal.pone.0005773

[6] Encoding Multiple Unnatural Amino Acids Via Evolution Of A Quadruplet Decoding Ribosome by Heinz Neumann, Kaihang Wang, Lloyd Davis, Maria Garcia-Alai & Jason W. Chin, Nature, 464: 441-444 (18th March 2010)

[7] Evolution And Multilevel Optimisation Of The Genetic Code by Tobias Bollenbach, Kalin Vetsigian & Roy Kishony, Genome Research (Cold Spring Harbour Press), 17: 401-404 (2007)

[8] Evolution Of Amino Acid Frequencies In Proteins Over Deep Time: Inferred Order Of Introduction of Amino Acids Into The Genetic Code by Dawn J. Brooks, Jacques R. Fresco, Arthur M. Lesk & Mona Singh, Molecular & Biological Evolution, 19(10):1645-1655 (2002)

[9] Evolution Of The Aminoacyl-tRNA Synthetases And The Origin Of The Genetic Code by R. Wetzel, Journal of Molecular Evolution, 40: 545-550 (1995)

[10] Evolution Of The Genetic Code: Partial Optimization Of A Random Code For Robustness To Translation Error In A Rugged Fitness Landscape by Artem S Novozhilov, Yuri I Wolf and Eugene V Koonin, Biology Direect, 2: 24 (23rd October 2007) DOI:10.1186/1745-6150-2-24

[11] Exceptional Error Minimization In Putative Primordial Genetic Codes by Artem S Novozhilov & Eugene V. Koonin, Biology direct, 4(1): 44 (2009)

[12] Expanding The Genetic Code Of Escherichia coli by Lei Wang, Angsar Brock, Brad Herberich & Peter G. Schultz, Science, 292: 498-500 (20th April 2001)

[13] Experimental Rugged Fitness Landscape In Protein Sequence Space by Yuuki Hayashi, Takuyo Aita, Hitoshi Toyota, Yuzuru Husimi, Itaru Urabe & Tetsuya Yomo, PLoS One, 1(1): e96 (2006) DOI:10.1371/journal.pone.0000096

[14] Importance Of Compartment Formation For A Self-Encoding System by Tomoaki Matsuura, Muneyoshi Yamaguchi, Elizabeth P. Ko-Mitamura, Yasufumi Shima, Itaru Urabe & Tetsuya Yomo, Proceedings of the National Academy of Sciences of the USA, 99(11): 7514-7517 (28th May 2002)

[15] On The Origin Of The Genetic Code: Signatures Of Its Primordial Complementarity In tRNAs And Aaminoacyl-tRNA Synthetases by S. N. Rodin and A. S. Rodin, Heredity, 100: 341-355 (5th March 2008)

[16] Origin And Evolution Of The Genetic Code: The Universal Enigma by Eugene V. Koonin & Artem S. Novozhilov, IUBMB Life, 61(2): 99-111 (February 2009) (Also available at arXiv)

[17] Protein Evolution With An Expanded Genetic Code by Chang C. Liu, Antha V. Mack, Meng-Lin Tsao, Jeremy H. Mills, Hyun Soo Lee, Hyeryun Choe, Michael Farzan, Peter G. Schultz & Vaughn V. Smider, Proceedings of the National Academy of Sciences of the USA, 105(46): 17688-17693 (18th November 2008)

[18] Protein Stability Promotes Evolvability by Jesse D. Bloom, Sy T. Labthavikul, Christopher R. Otey & Frances H. Arnold, Proceedings of the National Academy of Sciences of the USA, 103(15): 5869-5874 (11th April 2006)

[19] Reassigning Cysteine In The Genetic Code Of Escherichia coli by Volker Döring and Philippe Marlière, Genetics, 150: 543-551 (October 1998)

[20] Recent Evidence For Evolution Of The Genetic Code by Syozo Osawa, Thomas H, Jukes, Kimitsuna Watanabe & Akira Muto, Microbiological Reviews, 56(1): 229-264 (March 1992)

[21] Rewiring The Keyboard: Evolvability Of The Genetic Code by Robin D. Knight, Stephen J. Freeland & Laura F. Landweber, Nature Reviews Genetics, 2: 41-58 (January 2001)

[22] Thawing The Frozen Accident by C. W. Carter Jr., Heredity, 100: 339-340 (13th February 2008)

[23] A Simple Model Based On Mutation And Selection Explains Trends In Codon And Amino-Acid Usage And GC Composition Within And Across Genomes by Robin D. Knight, Stephen J. Freeland & Laura F. Landweber, Genome Biology, 2(4): research0010.1–0010.13 (22nd March 2001)

Let's take a look at some of these papers in more detail, shall we? First, the PNAS paper by Wong:

ABSTRACT The theory is proposed that the structure of the genetic code was determined by the sequence of evolutionary emergence of new amino acids within the primordial biochemical system.

In more detail, the author opens with the following:

The genetic code for protein molecules is a triplet code, consisting of the 64 triplets of the four bases adenine, guanine, cytosine and uracil (1, 2). The cracking of the code was a monumental achievement, but it posed in turn what Monod (3) regards as one of the challenges of biology, namely the "riddle of the code's origin." Crick (4) has discussed two different theories which have been proposed regarding this origin. The Stereochemical Theory postulates that each amino acid became linked to its triplet codons on account of stereochemical reasons, whereas the Frozen Accident Theory postulates that the linkage arose purely by chance. Since neither theory has given a systematic solution to the riddle, the present purpose is to explore a third hypothesis, which postulates that:

The structure of the codon system is primarily an imprint of the prebiotic pathways of amino-acid formation, which remain recognizable in the enzymic pathways of amino-acid biosynthesis. Consequently the evolution of the genetic code can be elucidated on the basis of the precursor-product relationships between amino acids in their biosynthesis. The codon domains of most pairs of precursor-product amino acids should be contiguous, i.e., separated by only the minimum separation of a single base change.

This theory, which may be called a Co-evolution Theory, is readily tested. If many pairs of amino acids which bear a nearest (in terms of the number of enzymic steps) precursor product relationship to each other in a biosynthetic pathway fail to occupy contiguous codon domains, the theory would be untenable. The known precursor-product conversions between amino acids are (5-7):

Glu -> Gln
Glu -> Pro
Glu -> Arg
Asp -> Asn
Asp -> Thr
Asp -> Lys
Gln -> His
Thr -> Ile
Thr -> Met
Set -> Trp
Ser -> Cys
Val -> Leu
Phe -> Tyr

Of these, only the relationships of Asp to Lys and Thr to Met require some comment. Lys can be synthesized either from Asp via the diaminopimelate pathway (8), or from Glu via the a-aminoadipate pathway (9). Since the former pathway operates in prokaryotes and the latter in eukaryotes, an Asp-Lys pairing has greater prebiotic significance than a Glu-Lys pairing. The biosynthesis of Met can proceed best from Asp, but Thr is nearer to Met in terms of the number of enzymic steps involved (homoserine, which might represent a more primitive form of Thr, is even nearer still to Met). Although Ser and Cys can enter into the Met-biosynthetic
pathway subsequent to the entry of Thr, neither Ser nor Cys is a straightforward precursor of Met. Ser is not the only possible contributor of a one-carbon group to -Met, and Cys is not the only possible contributor of sulfur (10). a-Transaminations, because of their relative nonspecificity, are not regarded as useful criteria for the tracing of precursor-product relationships. Aside from the above precursor-product relationships, Glu, Asp, and Ala are known to be interconvertible via the tricarboxylate cycle, and Ala, Ser, and Gly via the metabolism of pyruvate, glycerate, and glyoxylate (6).

Evolutionary map of the genetic code

When the codons for various precursor-product amino acids (Table 1) are examined, many of the codon domains of product amino acids are found to be contiguous with those of their respective precursors. The only noncontiguities are those of the Glu-Pro, Glu-Arg, Asp-Thr, and Asp-Lys pairs. If the prebiotic derivations of Gln from Glu, and Asn from Asp, had not occurred at the earliest stages of codon distribution, CAA and CAG could be expected to form part of the early Glu codons, and AAU and AAC part of the early Asp codons. This simple secondary postulate regarding the dicarboxylic amino acids and their amides suffices to remove all noncontiguities between precursors and products. It becomes possible to construct in Fig. 1 a map of the genetic code in which the codon domains of every precursor-product pair of amino acids (connected by single-headed arrows), as well as those of other interconvertible pairs (connected by double-headed arrows) are separated by only a single base change. This confirms the prediction by the Co-evolution Theory that codon distribution is closely related to amino-acid biosynthesis. Furthermore, since the theory suggests that the enzymic pathways of amino-acid biosynthesis largely stemmed from the prebiotic pathways of amino-acid formation, the pathways of this map are regarded as co-evolutionary pathways through which new amino acids were generated within the primordial system, and through which the triplet codons became distributed to finally the 20 amino acids.

Tests for randomness

The correlation between codon distribution and amino-acid biosynthesis indicated in Fig. 1 could arise not only from coevolution, but also in principle from chance. However, the unlikelihood of the latter explanation can be demonstrated in two different ways. First, consider the widespread contiguities between the codons of precursor and product amino acids. For any precursor codon triplets, there will be a other triplets in the genetic code which are contiguous with the group, and b other triplets which are noncontiguous. If a product of this amino acid has n codons, the random probability P that as many as x of these n codons turn out to be contiguous with some precursor codon is determined by the hypergeometric distribution (equation given in full in the paper).

The calculated values of P for eight precursor-product pairs are shown in Table 2. Using the method of Fisher (11), the eight corresponding -2 1n P values can be summed to give a χ² value of 45.01 with 16 degrees of freedom; this indicates an aggregate probability of less than 0.0002 that these eight sets of contiguities could have become so numerous by chance. Amongst the eight amino-acid pairs, either Phe-Tyr or Val-Leu may represent sibling products of a common biosynthetic pathway rather than true precursor and product. Their deletion from calculation leaves a χ² value of 27.10 with 12 degrees of freedom, which still points to an aggregate probability of only 0.0075. The potential Glu-Pro, Glu-Arg, Asp-Thr, Asp-Lys, Thr-Met, Ala-Ser-Gly and Glu-Asp-Ala contiguities, plausible but less certain, have not been included in these calculations; their inclusion would lower the aggregate probability even further. Also, there are other ways to perform the statistical analysis, e.g., by taking a pair of codons such as UGU and UGC as one rather than two units in the hypergeometric distribution, but the nonrandom character of the precursor-product contiguities is far too striking to be fundamentally circumventable by statistical methodology.

Secondly, Gln, Pro, and Arg are biosynthetic siblings of the Glu family, and Asn, Thr, and Lys are siblings of the Asp family. Likewise, Cys and Trp are siblings of the Ser family, and Ile and Met are siblings of the Thr family. Of the seven pairs of amino acids in Table 1 that share the first two bases, Ile-Miet, Asn-Lys, and Cys-Trp are siblings. His-Gln are precursor-product, and Asp-Glu are either siblings or precursor-product. Only Phe-Leu and Ser-Arg are unrelated pairs. There are 190 possible amino-acid pairs amongst the 20 amino acids, and the four families of siblings generate a total of eight sibling pairs. Accordingly the probability of randomly finding as many as three out of any seven amino-acid pairs to be sibling pairs is only 0.00161 on the basis of Eq. 1 (a = 8, b = 182, n = 7, x = 3). If Ile-Met are not regarded as siblings, this probability would be raised to 0.0224, but then there are also grounds to consider Asp-Glu as siblings of the tricarboxylate cycle, whereupon it would be reverted to 0.00161. In any case the enrichment of siblings amongst amino-acid pairs sharing the same first two bases appears strongly nonrandom, and provides further evidence against a chance origin of the correlation between amino-acid biosynthesis and codon distribution.

The rest of the paper can be read in full by downloading the PDF from here.

Moving on, let's look at the Copley ,em>et al paper, which can be downloaded from here. This opens as follows:

The genetic code has certain regularities that have resisted mechanistic interpretation. These include strong correlations between the first base of codons and the precursor from which the encoded amino acid is synthesized and between the second base of codons and the hydrophobicity of the encoded amino acid. These regularities are even more striking in a projection of the modern code onto a simpler code consisting of doublet codons encoding a set of simple amino acids. These regularities can be explained if, before the emergence of macromolecules, simple amino acids were synthesized in covalent complexes of dinucleotides with α-keto acids originating from the reductive tricarboxylic acid cycle or reductive acetate pathway. The bases and phosphates of the dinucleotide are proposed to have enhanced the rates of synthetic reactions leading to amino acids in a small-molecule reaction network that preceded the RNA translation apparatus but created an association between amino acids and the first two bases of their codons that was retained when translation emerged later in evolution.

The authors continue thus:

The genetic code has many regularities (1), of which only a subset have explanations in terms of tRNA function (2) or robustness against deleterious effects of mutation (3, 4) or errors in translation (3, 5). There is a strong correlation between the first bases of codons and the biosynthetic pathways of the amino acids they encode (1, 6). Codons beginning with C, A, and U encode amino acids synthesized from α-ketoglutarate (α-KG), oxaloacetate (OAA), and pyruvate, respectively. [¶] These correlations are especially striking in light of the structural diversity of amino acids whose codons share a first base. For example, codons for Glu and Pro both begin with C, and those for Cys and Leu begin with U. Codons beginning with G encode amino acids that can be formed by direct reductive amination of a simple α-keto acid. These include glycine, alanine, aspartate, and glutamate, which can be formed by reductive amination of glyoxalate, pyruvate, OAA, and α-KG, respectively. There is also a long-recognized relationship between the hydrophobicity of the amino acid and the second base of its codon (1). Codons having U as the second base are associated with the most hydrophobic amino acids, and those having A as the second base are associated with the most hydrophilic amino acids.

We suggest that both correlations can be explained if, before the emergence of macromolecules, simple amino acids were synthesized from α-keto acid precursors covalently attached to dinucleotides that catalyzed the reactions required to synthesize specific amino acids (see Fig. 1). This is a significant departure from previous theories attempting to explain the regularities in the genetic code (3). The ‘‘stereochemical’’ hypothesis suggests that binding interactions between amino acids and their codons or anticodons dictated the structure of the genetic code (7–10). The ‘‘coevolution’’ hypothesis (6) suggests that the original genetic code specified a small number of simple amino acids, and that, as more complex amino acids were synthesized from these precursors, some codons that initially encoded a precursor were ceded to its more complex products. Finally, the genetic code has been proposed to be simply a ‘‘frozen accident’’ (11).

Recent analysis suggests that the reductive tricarboxylic acid cycle could serve as a network-autocatalytic self-sufficient source for simple α-keto acids, including glyoxalate, pyruvate, OAA, and α-KG, as well as the carbon backbones of sugars and nucleobases (12). α-Keto acids can also be generated from the reductive acetyl CoA pathway (13). Most simple amino acids can be reached from an α-keto acid precursor by a small number of relatively simple chemical transformations, and the synthetic pathway that will be followed is determined within the first three steps. We propose that the positions of functional groups in a dinucleotide–α-keto acid complex determine what reactions can be effectively catalyzed for a given α-keto acid. An example of a series of reactions leading from α-KG to five amino acids, each attached to the first two bases of its codon, is shown in Fig. 2, which can be regarded as a ‘‘decision tree’’ in which the nature of the bases in the dinucleotide determines which types of reactions occur. The pathways proposed follow closely those in extant organisms (14), differing primarily in the timing of the reductive amination leading to the final amino acid. The motivation for this approach is that modern biosynthetic pathways likely emerged by gradual acquisition of enzymes capable of catalyzing reactions that had previously occurred in the absence of macromolecular catalysts. Thus, modern pathways are ‘‘metabolic fossils’’ that provide insight into prebiotic synthetic pathways, although some refinements and permutations are expected to have occurred.

Once again, I'll let everyone read the full paper at leisure, as it's a fairly large and complex one. :)

Moving on, we have the Vetsigian et al paper, which is downloadable from here. This paper opens as follows:

A dynamical theory for the evolution of the genetic code is presented, which accounts for its universality and optimality. The central concept is that a variety of collective, but non-Darwinian, mechanisms likely to be present in early communal life generically lead to refinement and selection of innovation-sharing protocols, such as the genetic code. Our proposal is illustrated by using a simplified computer model and placed within the context of a sequence of transitions that early life may have made, before the emergence of vertical descent.

The authors continue with:

The genetic code could well be optimized to a greater extent than anything else in biology and yet is generally regarded as the biological element least capable of evolving.

There would seem to be four reasons for this paradoxical situation, all of which reflect the reductionist molecular perspective that so shaped biological thought throughout the 20th century. First, the basic explanation of gene expression appears to lie in its evolution, and not primarily in the specific structural or stereochemical considerations that are sufficient to account for gene replication. Second, the problem’s motto, ‘‘genetic code,’’ is a misnomer that makes the codon table the defining issue of gene expression. A satisfactory level of understanding of the gene should provide a unifying account of replication and expression as two sides of the same coin. The genetic code is merely the linkage between these two facets. Thus, and thirdly, the assumption that the code and the decoding mechanism are separate problems, individually solvable, is a reductionist fallacy that serves to deny the fundamental biological nature of the problem. Finally, the evolutionary dynamic that gave rise to translation is undoubtedly non-Darwinian, to most an unthinkable notion that we now need to entertain seriously. These four considerations structure the approach we take in this article.

To this point in time, biologists have seen the universality of the code as either a manifestation of the Doctrine of Common Descent or simply as a ‘‘frozen accident.’’ Viewing universality as following from common descent renders unthinkable the notion explored here that a universal code may be a necessary precondition for common ancestry, indeed even for life as we know it.We will argue in this article [a maturation of the earlier concept of the progenote (1)] that the very fact of the code’s evolvability, together with the details of its internal structure, provides strong clues to the nature of early life, and in particular its essential communal character (2).

Beyond the code’s universality we have very few clues to guide us in trying to understand its evolution and that of the underlying decoding mechanism. The principal ones again are properties of the code itself; specifically, the obvious structure of the codon table. The table possesses (at least) two types of order: synonym order and relatedness order. The first is the relatedness of codons assigned to the same amino acid; the second is the relatedness of codons assigned to related amino acids. Relatedness among the amino acids is context-dependent and in the context of the codon table could a priori reflect almost anything about the amino acids: their various properties, either individually or in combination; the several macromolecular contexts in which they are found, such as protein structure, the translation mechanism, and the evolution of translation; or the pretranslational context of the so-called RNA world. Although we do not know what defines amino acid ‘‘similarity’’ in the case of the code, we do know one particular amino acid measure that seems to express it quite remarkably in the coding context. That measure is amino acid polar requirement (3–5). Although the relatedness order of the code is marginally evident from simple inspection of the codon table (3, 4, 6–8), it is pronounced when the amino acids are represented by their respective polar requirements (4).

A major advance was provided by computer simulation studies (9–14) of the relatedness ordering of the amino acids over the codon table, which showed that the code is indeed relationally ordered and moreover is optimized to near the maximum extent possible. Compared with randomly generated codes, the canonical code is ‘‘one in a million’’ when the relatedness measure is the polar requirement. No other amino acid measure is known to possess this characteristic (14) (in our opinion, the significance of this observation has not been adequately recognized or pursued). These precisely defined relatedness constraints in the codon table were unexpected and still cry out for explanation.

As far as interpretation goes, the optimal aspect of the genetic code is surely a reflection of the last aspect of the coding problem that needs to be brought into consideration: namely, the precision or biological specificity with which translation functions. Precision, along with every aspect of the genetic code, needs to be understood as part of an evolutionary process. We would contend that at early stages in cellular evolution, ambiguous translation was tolerated (there being no alternative) and was an important and essential part of the evolutionary dynamic (see below). What we imply by ambiguity here is inherent in the concept of group codon assignments, where a group of related codons is assigned as a whole to a corresponding group of related amino acids (3). From this flows the concept of a ‘‘statistical protein,’’ wherein a given gene can be translated not into a unique protein but instead into a family of related protein sequences. Note that we do not say that these are an approximation to a perfect translation of the gene, thereby implying that these sequences are in some sense erroneous. Early life did not require a refined level of tolerance, and so there was no need for a perfect translation. Ambiguity is therefore not the same thing as ‘‘error.’’

I'll break off from here, because this paper is very heavy with respect to mathematical content, and some of the relevant expressions are extremely difficult to render in board tags. However, this paper should prove interesting to read.

Next, we have the Lehmann et al paper, which can be downloaded from here. This opens as follows:

AbstractThe origin of the genetic code in the context of an RNA world is a major problem in the field of biophysical chemistry. In this paper, we describe how the polymerization of amino acids along RNA templates can be affected by the properties of both molecules. Considering a system without enzymes, in which the tRNAs (the translation adaptors) are not loaded selectively with amino acids, we show that an elementary translation governed by a Michaelis-Menten type of kinetics can follow different polymerization regimes: random polymerization, homopolymerization and coded polymerization. The regime under which the system is running is set by the relative concentrations of the amino acids and the kinetic constants involved. We point out that the coding regime can naturally occur under prebiotic conditions. It generates partially coded proteins through a mechanism which is remarkably robust against non-specific interactions (mismatches) between the adaptors and the RNA template. Features of the genetic code support the existence of this early translation system.

The authors continue with:

Introduction

A major issue about the origin of the genetic system is to understand how coding rules were generated before the appearance of a family of coded enzymes, the aminoacyl-tRNA synthetases. Each of these ~20 different enzymes has a binding pocket specific for one of the 20 encoded amino acids, and also displays an affinity for a particular tRNA, the adaptor for translation [Fig. 1(a)]. These adaptors are characterized by their anticodons, a triplet of base located on a loop. The synthetases establish the code by attaching specific amino acids onto the 39 ends of their corresponding tRNAs, a two-step process called aminoacylation [1]. The first step (activation) involves an ATP, and leads to the formation of a highly reactive intermediate, aa–AMP (aa= amino acid). The second step consists of the transfer of the amino acid from AMP onto the 39 end of the tRNA. Those tRNAs can subsequently participate in the translation of RNA templates, during which codons about to be translated are tested by the anticodons of incoming tRNAs. When anticodon-codon complementarity occurs, an amino acid is added onto the nascent protein through the formation of a new peptide bond [2].

How could a translation system operate in the absence of the synthetases? Recent works have shown that particular RNA stemloops of ~25 bases can self-catalyze the covalent binding of amino acids onto their own 39 ends [3,4]. These RNAs however require aa–AMP as a substrate because they cannot manage the activation step in their present form. In addition, they show little specificity for the amino acids, raising the question of how a code could be generated by them. Some answers will likely be provided by the activation step if possible to implement on these small RNAs. This issue is not examined in the present paper.

Based on an earlier investigation [5], the present analysis shows that the translation process itself can contribute to the establishment of coding rules. Consider an elementary translation system constituted by RNA templates made up of two types of codons {I, II}, tRNAs with anticodons complementary to these codons, and two types of amino acids {1, 2}. Suppose that the tRNAs are not selectively loaded with amino acids (i.e. the rates of loading only depend on the relative concentrations of the amino acids). Our analysis shows that it is possible to observe a coded polymerization. We calculate the probability of codon I being translated by amino acid 1 and the probability of codon II being translated by amino acid 2, the coding regime occurring when both probabilities are simultaneously higher than 0.5. These probabilities are functions of the anticodon-codon association and dissociation rate constants, the amino acids concentrations and their respective kinetic constants of peptide bond formation. One general configuration allows a coding regime to occur: the amino acid with the slow kinetics (i.e. the ‘‘slow’’ amino acid) is more concentrated in solution than the ‘‘fast’’ amino acid. Given two appropriate codons, the competition for the translation of the codon dissociating quickly from its cognate tRNA (i.e. the ‘‘weak’’ codon) is won by the fast amino acid. As for the ‘‘strong’’ codon, for which the amino acid kinetics are equal or higher than the anticodon-codon dissociation rate constant, the higher concentration of the slow amino acid makes it a better competitor in that case. Although other types of polymerization are possible, we show that this coding regime is favored under prebiotic conditions. It is furthermore remarkably robust against anticodon-codon mismatches. We conclude our analysis by showing that this model can naturally be implemented by a system of four codons and four amino acids thought to be a plausible original genetic code.

Next, we have the Brooks et al paper, which can be downloaded in full from here. The authors begin with:

To understand more fully how amino acid composition of proteins has changed over the course of evolution, a method has been developed for estimating the composition of proteins in an ancestral genome. Estimates are based upon the composition of conserved residues in descendant sequences and empirical knowledge of the relative probability of conservation of various amino acids. Simulations are used to model and correct for errors in the estimates. The method was used to infer the amino acid composition of a large protein set in the Last Universal Ancestor (LUA) of all extant species. Relative to the modern protein set, LUA proteins were found to be generally richer in those amino acids that are believed to have been most abundant in the prebiotic environment and poorer in those amino acids that are believed to have been unavailable or scarce. It is proposed that the inferred amino acid composition of proteins in the LUA probably reflects historical events in the establishment of the genetic code.

I'll move quickly on, and cover in slightly more detail the Novozhilov et al (2007) paper, which opens as follows:

Abstract

Background: The standard genetic code table has a distinctly non-random structure, with similar amino acids often encoded by codons series that differ by a single nucleotide substitution, typically, in the third or the first position of the codon. It has been repeatedly argued that this structure of the code results from selective optimization for robustness to translation errors such that translational misreading has the minimal adverse effect. Indeed, it has been shown in several studies that the standard code is more robust than a substantial majority of random codes. However, it remains unclear how much evolution the standard code underwent, what is the level of optimization, and what is the likely starting point.

Results: We explored possible evolutionary trajectories of the genetic code within a limited domain of the vast space of possible codes. Only those codes were analyzed for robustness to translation error that possess the same block structure and the same degree of degeneracy as the standard code. This choice of a small part of the vast space of possible codes is based on the notion that the block structure of the standard code is a consequence of the structure of the complex between the cognate tRNA and the codon in mRNA where the third base of the codon plays a minimum role as a specificity determinant. Within this part of the fitness landscape, a simple evolutionary algorithm, with elementary evolutionary steps comprising swaps of four-codon or two-codon series, was employed to investigate the optimization of codes for the maximum attainable robustness. The properties of the standard code were compared to the properties of four sets of codes, namely, purely random codes, random codes that are more robust than the standard code, and two sets of codes that resulted from optimization of the first two sets. The comparison of these sets of codes with the standard code and its locally optimized version showed that, on average, optimization of random codes yielded evolutionary trajectories that converged at the same level of robustness to translation errors as the optimization path of the standard code; however, the standard code required considerably fewer steps to reach that level than an average random code. When evolution starts from random codes whose fitness is comparable to that of the standard code, they typically reach much higher level of optimization than the standard code, i.e., the standard code is much closer to its local minimum (fitness peak) than most of the random codes with similar levels of robustness. Thus, the standard genetic code appears to be a point on an evolutionary trajectory from a random point (code) about half the way to the summit of the local peak. The fitness landscape of code evolution appears to be extremely rugged, containing numerous peaks with a broad distribution of heights, and the standard code is relatively unremarkable, being located on the slope of a moderate-height peak.

Conclusion: The standard code appears to be the result of partial optimization of a random code for robustness to errors of translation. The reason the code is not fully optimized could be the trade-off between the beneficial effect of increasing robustness to translation errors and the deleterious effect of codon series reassignment that becomes increasingly severe with growing complexity of the evolving system. Thus, evolution of the code can be represented as a combination of adaptation and frozen accident.

Again, this paper involves some heavy mathematics, and a rather involved computer simulation, so I'll jump straight to the discussion and conclusion:

Discussion and Conclusion

In this work, we examined possible evolutionary paths of the genetic code within a restricted domain of the vast parameter space that is, in principle, available for a mapping of 20 amino acids over 64 nucleotide triplets. Specifically, we examined only those codes that possess the same block structure and the same degree of degeneracy as the standard code. It should be noticed, however, that this choice of a small part of the overall, vast code space for further analysis is far from being arbitrary. Indeed, the block structure of the standard code appears to be a direct consequence of the structure of the complex between the cognate tRNA and the codon in mRNA where the third base of the codon plays a minimum role as a specificity determinant. Within this limited – and, presumably, elevated – part of the fitness landscape, we implemented a very simple evolutionary algorithm by taking as an elementary evolutionary step a swap of four-codon or two-codon series. Of course, one has to realize that the model of code's evolution considered here is not necessarily realistic and, technically, should be viewed as a "toy" model. It is conceivable that codon series swaps were not permissible at the stage in the code's evolution when all 20 amino acids have been already recruited. Nevertheless, we believe that the idealized scheme examined here allows for meaningful comparison between the standard code and various classes of random codes.

The evolution of the standard code was compared to the evolution of four sets of codes, namely, purely random codes (r), random codes with robustness greater than that of the standard code (R), and two sets of codes that resulted from optimization of the first two sets (o and O, respectively). With the above caveats, the comparison of these sets of codes with the standard code and its locally optimized version yielded several salient observations that held for both measures of amino acid replacements (the PRS and the Gilis matrix) that we employed.

1. The code fitness landscape is extremely rugged such that almost any random initial point (code) tends to its own local optimum (fitness peak).

2. The standard genetic code shows a level of optimization for robustness to errors of translation that can be achieved easily and exceeded by minimization procedure starting from almost any random code.

3. On average, optimization of random codes yielded evolutionary trajectories that converged at the same level of robustness as the optimization path of the standard code; however, the standard code required considerably fewer steps to reach that level than an average random code.

4. When evolutionary trajectories start from random codes whose fitness is comparable to the fitness of the standard code, they typically reach much higher level of optimization than that achieved by optimization of the standard code as an initial condition, and the same holds true for the minimization percentage. Thus, the standard code is much closer to its local minimum (fitness peak) than most of the random codes with similar levels of robustness (Fig. 9).

5. Principal component analysis of the between amino acids distance vectors indicates that the standard code is very different from the sets r (all random codes) and O (highly optimized codes produced by error cost minimization for random codes that are better than the standard code), and more similar to the codes from o (optimized random codes) and R (the robust subset of random codes). More importantly, the optimized code produced by minimization of the standard code is much closer to the set of optimized random codes (o) than to any other of the analyzed sets of codes.

6. In this fitness landscape, it takes only 15–30 evolutionary steps (codon series swaps) for a typical code to reach the nearest local peak. Notably, the average number of steps that are required for a random code to reach the peak minus the number of steps necessary for the standard code to reach its own peak takes a random code to the same level of robustness as that of the standard code.

Putting all these observations together, we conclude that, in the fitness landscape explored here, the standard genetic code appears to be a point on an evolutionary trajectory from a random point (code) about half the way to the summit of the local peak. Moreover, this peak appears to be rather mediocre, with a huge number of taller peaks existing in the landscape. Of course, it is not known how the code actually evolved but it does seem likely that swapping of codon series was one of the processes involved, at least, at a relatively late stage of code's evolution, when all 20 amino acids have already been recruited. If so, perhaps, the most remarkable thing that we learned, from these modeling exercises, about the standard genetic code is that the null hypothesis on code evolution, namely, that it is a partially optimized random code, could not be rejected. Why did the code's evolution stop where is stopped, i.e., in the middle of the slope of a local fitness peak (Fig. 9), rather than taking it all the way to the summit, especially, as the number of steps required to get there is relatively small? It appears reasonable to view the evolution of the code as a balance of two forces, the positive selection for increasing robustness to errors of translation and the negative selection against any change, i.e., the drive to "freeze an accident". Indeed, codon series swapping is, obviously, a "macromutation" that simultaneously affects all proteins in an organism and would have a deleterious effect that would become increasingly more severe as the complexity of the evolving system increases. This is why, in all likelihood, no such events occurred during advanced stages of life's evolution, i.e., after the cellular organization was established. Conceivably, such an advanced stage in the evolution of life forms was reached before the code reached its local fitness peak, in support of a scenario of code evolution that combines selection for translational robustness with Crick's frozen accident.

Needless to say, the rest of the papers in my above list are freely downloadable via Google Scholar, and also contain much of interest to the serious student of this topic.

Again ... Game Over.

And while I'm still awake ...

Is there any actual evidence for one KIND changing into another KIND?

The creationist "kinds" fiction is precisely that - FICTION.

No two creationists have ever been able to agree what constitutes a "kind", or provide a proper, rigorous definition for this ludicrous term.

On the other hand, biologists have several rigorous definitions for "species", encompassing different aspects of the nature thereof, of which the biological species concept has been the most rigorously applicable to modern biology. So until there is something other than hot air from creationists on this matter, I'll treat "kinds" as another ex recto creationist assertion.

Imagine if you put this kind of effort into proving Gods existence

Then that effort would have been as wasted as the 5,000 years of navel gazing on the part of supernaturalists that has failed to deliver on the matter.

It’s cool cuz we all use the same evidence to come up with different theories

Bullshit. You don't have a "theory", you have blind adherence to a made up shit mythology and its crass assertions.

Which might have something to do with the fact that in the world of science, a theory is about as far removed from "made up shit guess" as it's possible to be. In case you failed to learn this elementary concept in the requisite classes, a theory in science, is an integrated explanation for a class of entities and interactions of interest, which has been tested experimentally to determine its accord with observational data, and found via such testing to be thus in accord.

Since several mythological assertions are untestable even in principle, let alone in practice, that rules out your mythology as the basis for any genuine theory. As does the fact that the testable assertions contained in your mythology are mostly plain, flat, wrong, and in many cases, fatuous and absurd into the bargain.

Oh, and trying to force-fit data to mythological assertions isn't a use of evidence, but a misuse thereof. Quite simply, every time you and your ilk treat science as a branch of apologetics, you're being wilfully discoursively dishonest.

Christianity has withstood the test of time, at least the main point of it (nicene creed)

Bullshit. Your mythology contains numerous assertions that are not merely wrong in the light of modern scientific knowledge, but fatuous and absurd in said light. Your mythology was written by piss-stained Bronze Age incels who were too stupid to count correctly the number of legs that an insect possesses, and who thought genetics was controlled by coloured sticks. Furthermore, your mythology only achieved the hegemony it did, because of the willingness of its past adherents to lie and to kill. Its assertions are increasingly revealed to be a mixture of the absurd, the asinine and the iniquitous, and its adherents all too frequently exhibit a flord aetiology that combines hubris, entitlement, intellectual indolence, complacency and in some cases, a sociopathic level of absence of basic empathy, in a nauseatingly bubotic package.

I believe that people like you (scientists) are eventually going to prove Gods existence.

Keep smoking those hallucinogens.

Here's a clue for you, in the form of an elementary concept that you manifestly never learned. When scientists establish that testable natural processes are sufficient to explain a given class of entities and interactions, then from that point on, so-called "supernatural" entities are superfluous to requirements and irrelevant. This has already happened for vast classes of entities and interactions, including classes thereof that the authors of your sad mythology were incapable of even fantasising about, but which scientists alighted upon and placed into usefully predictive, quantitative frameworks of knowledge. Scientists have been systematically tossing your worthless mythology, it's risible assertions and its imaginary magic man into the bin for 300 years.

Oh, and before you're tempted to travel down the requisite avenue of canards, the only reason some scientists in the past made unctuous noises about your magic man, was to avoid being burned at the stake like Giordano Bruno. In the modern era, free from the threat of being murdered by raving mythology fanboys, scientists are treating your mythology more and more as a pathetic irrelevance, as this article in Nature clearly demonstrates. The tiny number of prominent scientists who still make silly noises about an invisible magic man, are usually American, and in some cases doing so only because of the toxic, pathological influence mythology fanboyism wields over that nation. In properly constituted secular developed nations, this sort of behaviour is regarded, quite properly, as an amusing anomaly, though the malignant influence exerted by religion across the Atlantic is frequently anything but amusing.

If you believe in the Bible and Jesus, all the horrible and crazy things that happen in the world today make sense and can be explained.

Complete and utter poppycock. First of all, your mythology doesn't "explain" anything. It asserts much, but like every other mythology fanboy I've encountered, you manifestly need to learn the difference between mere assertion and genuine explanation.

Second, given that many of the requisite assertions have already been found to be complete hooey, the idea that this collection of assertions contains any useful knowledge about the operation of reality, is again a fantasy that only mythology fanboys could possibly entertain. Your mythology constitutes evidence for the following, and the following alone:

[1] The capacity of pre-scientific humans to engage in parochial, but ultimately unimaginative, fantasising;

[2] The gullibility of adherents of said mythology;

[3] The Machiavellian utility of said mythology as a tool of political control.

Though to be fair, your mythology isn't unique in any of these respects.

I’m praying for all of you.

Ah, the synthetic pretence that talking to your imaginary magic man is going to achieve anything of substance.

Don't waste your time. Instead, start learning some actual facts, instead of filling your head with mythological drivel.

@You Know: "How a cell was created or what creates a cell." I can put an end to it right now. WHERE IN THE HELL DID YOU EVER GET THE IDEA A CELL WAS CREATED? Please demonstrate that a cell can be created,. Really stupid assumption.