Do you seriously think evolution sits ontop of the mountain of logic?

Did you even consider that evolution has done nothing for present day science?

Expected Progeny Differences (EPDs)
40th Annual Illinois
Performance Tested Bull Sale
EPDs offer beef cattle producers the best predictor of genetic value of a bull. EPDs combine a
bull’s individual performance with that of his ancestors and related progeny into a single estimate of
how a bull’s progeny should perform compared to the average of his breed. There will be a total of six
different traits evaluated for EPDs in the sale.
EPDs are given for the production traits of birth, weaning, yearling and maternal milk. In addition
there are two different carcass traits evaluated with EPDs with these including marbling score/%IMF,
and Ribeye Area.
In addition to EPDs there will also be accuracy levels (from .00 to .99) which express how much a
trait can deviate from a specific number. The accuracy level is dependent upon the amount of
performance information available to make the estimate. All yearling and two-year-old bulls will have
similar accuracy levels. Research studies have shown an EPD to be up to six times more accurate in
predicting progeny performance that adjusted weights. EPDs are within breed comparisons. Producers
should not compare the EPD values across breeds.

Science should not be revolving around the past but in the present.

All of you atheists are defending this theory like it is the most important one ever to be discovered. Evolution has been around for 140 years and they still cannot find solid 100% evidence for it. Ever consider throwing away the theory already because it is obviously a poorly hypothesized one? Did you know that the geologic column is based upon circular reasoning? The fossils are dated from the certain layer they lie in and the layers are dated from what types of fossils are within them. Also, did you know that all of the dating methods are subjectable to corruptable data readings? Scientists have found that something as simple as water can destory the data. The most commonly of these dating methods is carbon dating. Carbon dating in the first place only works when the atmosphere is at equilibrium. It also only works for living things. They have found just dead carcasses and dated the rear thousands of years difference than the front end of the animal. Lastly, i am sure you know, carbon dating only works for a few thousand years.

In biology, phylogenetics (Greek: phyle (φυλ&eta = tribe, race and genetikos (γενετικο&sigmaf = relative to birth, from genesis = birth) is the study of evolutionary relatedness among various groups of organisms (e.g., species, populations). Also known as phylogenetic systematics or cladistics, phylogenetics treats each species as a group of lineage-connected individuals[1]. Taxonomy, the classification of organisms according to similarity, has been richly informed by phylogenetics but remains methodologically and logically distinct.[2]

Evolution is regarded as a branching process, whereby populations are altered over time and may speciate into separate branches, hybridize together, or terminate by extinction. This may be visualized as a multidimensional character-space that a population moves through over time. The problem posed by phylogenetics is that genetic data are only available for the present, and fossil records (osteometric data) are sporadic and less reliable. Our knowledge of how evolution operates is used to reconstruct the full tree.[3]

Cladistics provides a simplified method of understanding phylogenetic trees. There are some terms that describe the nature of a grouping. For instance, all birds and reptiles are believed to have descended from a single common ancestor, so this taxonomic grouping (yellow in the diagram) is called monophyletic. "Modern reptile" (cyan in the diagram) is a grouping that contains a common ancestor, but does not contain all descendents of that ancestor (birds are excluded). This is an example of a paraphyletic group. A grouping such as warm-blooded animals would include only mammals and birds (red/orange in the diagram) and is called polyphyletic because the members of this grouping do not include the most recent common ancestor.

The most commonly used methods to infer phylogenies include parsimony, maximum likelihood, and MCMC-based Bayesian inference. Distance-based methods construct trees based on overall similarity which is often assumed to approximate phylogenetic relationships. All methods depend upon an implicit or explicit mathematical model describing the evolution of characters observed in the species included, and are usually used for molecular phylogeny where the characters are aligned nucleotide or amino acid sequences.

Organisms can generally inherit genes in two ways: from parent to offspring (vertical gene transfer), or by horizontal or lateral gene transfer, in which genes jump between unrelated organisms, a common phenomenon in prokaryotes.

Lateral gene transfer has complicated the determination of phylogenies of organisms since inconsistencies have been reported depending on the gene chosen.

Carl Woese came up with the three-domain theory of life (eubacteria, archaea and eukaryotes) based on his discovery that the genes encoding ribosomal RNA are ancient and distributed over all lineages of life with little or no lateral gene transfer. Therefore rRNA are commonly recommended as molecular clocks for reconstructing phylogenies.

This has been particularly useful for the phylogeny of microorganisms, to which the species concept does not apply and which are too morphologically simple to be classified based on phenotypic traits.

Owing to the development of advanced sequencing techniques in molecular biology, it has become feasible to gather large amounts of data (DNA or amino acid sequences) to estimate phylogenies. For example, it is not rare to find studies with character matrices based on whole mitochondrial genomes. However, it has been proposed that it is more important to increase the number of taxa in the matrix than to increase the number of characters, because the more taxa, the more robust is the resulting phylogeny. This is partly due to the breaking up of long branches. It has been argued that this is an important reason to incorporate data from fossils into phylogenies where possible. Using simulations, Derrick Zwickl and Hillis[6] found that increasing taxon sampling in phylogenetic inference has a positive effect on the accuracy of phylogenetic analyses.

Another important factor that affects the accuracy of tree reconstruction is whether the data analyzed actually contain useful phylogenetic signal, a term that is used generally to denote whether related organisms tend to resemble each other with respect to their genetic material or phenotypic traits.[7]

Introduction to Phylogenetics

escent from a common ancestor entails a process of branching and divergence of species, in common with any genealogical process. Genealogies can be graphically illustrated by tree-like diagrams, and this is why you will hear biologists refer to the genealogy of species as the "tree of life". Diagrams such as these are known as phylogenetic trees or phylogenies. The consensus model which evolutionary biologists use to represent the well-supported branches of the universal tree of life I will refer to as the "standard phylogenetic tree". Figure 1 shows a simplified example of some of the more familiar branches of the universal phylogenetic tree. The macroevolutionary prediction of a unique, historical universal phylogenetic tree is the most important, powerful, and basic conclusion from the hypothesis of universal common descent. A thorough grasp of this concept is necessary for understanding macroevolutionary deductions.

In the following section is a brief overview of phylogenetic trees and of how biologists determine them. This overview becomes increasingly technical as it proceeds. The material up until the maximum parsimony heading is essential for understanding the rest of this FAQ. The remaining phylogenetic discussion is given for completeness and to allow the interested reader the opportunity to delve as far as is desired.

Figure 1. The Consensus Phylogenetic Tree of All Life.

Phylogenetic trees represent evolutionary relationships

Figure 2: The parts of a phylogenetic tree. The taxa in this tree are "human", "mouse", and "fly" (all of which have had their full genomes sequenced). Several nodes are indicated, such as the "fly" taxon node and an internal node that represents the common ancestor of mice and humans. The root is indicated at left, representing the common ancestor of all three taxa listed.

Phylogenetics is the scientific discipline concerned with describing and reconstructing the patterns of genetic relationships among species and among higher taxa. Phylogenetic trees are a convenient way of visually representing the evolutionary history of life. These diagrams illustrate the inferred relationships between organisms and the order of speciation events that led from earlier common ancestors to their diversified descendants.

A phylogenetic tree has several parts, shown in Figure 2. Nodes represent taxonomic units, such as an organism, a species, a population, a common ancestor, or even an entire genus or other higher taxonomic group. Branches connect nodes uniquely and represent genetic relationships. The specific pattern of branching determines the tree's topology. Scaled trees have branch lengths that are proportional to some important biological property, such as the number of amino acid changes between nodes on a protein phylogeny (see Figure 3). Trees may also be rooted or unrooted. Rooted trees have a special node, known as the root, that represents a common ancestor of all taxa shown in the tree. Rooted trees are thus directional, since all taxa evolved from the root. Unrooted trees illustrate relationships only, without reference to common ancestors.

Figure 3: Various representations of a 5-taxa phylogenetic tree. Each of these trees represents the same five modern taxa: A, B, C, D, and E. The tree at upper left is rooted and scaled according to evolutionary distance. The root is at left. Taxa C and E have both undergone relatively large changes since divergence from the root, in contrast to taxa B and D. The tree at lower left is rooted and unscaled. Here the branch lengths are relative indicators of time since divergence. The tree at right is scaled but unrooted. In this tree, while the root is unkown, the relationships between taxa are identical to that shown in the other two trees.

A common misconception is that some modern species are ancestral to other modern species. However, all modern species are found at the tips of the tree's branches, and one modern species is as "evolved" as any other. That is, although mammals are thought to have evolved from something that resembled modern reptiles, modern reptiles are just as "old" evolutionarily as modern mammals (Brooks 1991, p.68; Futuyma 1998, p.113).

Methods for determining phylogenetic trees: Cladistics and numerical phylogenetics

Of all clean birds ye shall eat. But these are they of which ye shall not eat: The eagle, and the ossifrage, and the ospray, And the glede, and the kite, and the vulture after his kind, And every raven after his kind, And the owl, and the night hawk, and the cuckow, and the hawk after his kind, The little owl, and the great owl, and the swan, And the pelican, and the gier eagle, and the cormorant, And the stork, and the heron after her kind, and the lapwing, and the bat. Deuteronomy 14:11-18, KJV

If modern species have descended from ancestral ones in this tree-like, branching manner, it should be possible to infer the true historical tree that traces their paths of descent. Phylogenies have been inferred by biologists ever since Darwin first proposed that life was united by common descent over 140 years ago. Rigorous algorithmic methodologies for inferring phylogenetic trees have been in use for over the past 50 years.

In 1950, taxonomist Willi Hennig proposed a method for determining phylogenetic trees based on morphology by classifying organisms according to their shared derived characters, which are called synapomorphies (Hennig 1966). This method, now called cladistics, does not assume genealogical relatedness a priori, since it can be used to classify anything in principle, even things like books, cars, or chairs that are obviously not genealogically related in a biological sense (Kitching et al. 1998, Ch. 1, p. 26; ). Using firm evolutionary arguments, however, Hennig justified this method as the most appropriate classification technique for estimating evolutionary relationships generated by lineal descent. In fact, Hennig's cladistic method is nothing more than a formalization of the methods systematic biologists had been using intuitively ever since Linnaeus penned Systema Naturae. Biologists today construct their phylogenetic trees based on Hennig's method, and because of cladistics these phylogenetic trees are reproducible and independently testable (Brooks 1991, Ch. 2; Kitching et al. 1998).

Phylogenetic Jargon

apomorphy: A derived character of a group of organisms, not shared with ancestors of a group of organisms. Apomorphies are unique to the group, and are therefore group-defining. bootstrap: A technical statistical procedure for estimating the variability of a measurement. In phylogenetics, bootstrapping involves the production of a new, pseudo-dataset by randomly pulling data points from the original dataset. For each pseudo-dataset, a new phylogeny is inferred. Rounds of this provide an estimation of the well- and poorly-supported regions of the original phylogeny. character: An observable feature of an organism useful for distinguishing it from another. For example, a nucleotide in a DNA sequence, an amino acid in a protein sequence, or morphological characters like hair, feathers, or the presence or absence of certain bones. cladistics: A class of phylogenetic techniques that construct trees (cladograms) by grouping taxa into nested hierarchies according to shared derived characters (synapomorphies). Cladistics is closely associated with the parsimony criterion. cladogram: A hierarchical classification of taxa represented as a tree. Cladograms formally are independent of evolutionary theory, though in practice they are usually interpreted as phylogenies. derived character: See apomorphy. least squares: A phylogenetic distance matrix criterion. The best tree is the one with the smallest squared difference between the observed pairwise distances and the distances calculated from the inferred tree. It has a strong statistical justification, as it is based upon the common linear least squares statistical technique. Least squares is guaranteed by the Gauss-Markov theorem to converge on the correct answer as more data is included in the analysis if a proper distance metric is used, i.e. least squares is statistically consistent. Weighted versions correct for random variability and bias due to longer branch lengths. maximum likelihood: A cladistic criterion for inferring trees with character conflict. The best tree and evololutionary model maximize the probability of the observed data. Maximum likelihood has a strong statistical foundation. Given a correct model of evolutionary change, it is guaranteed to be statistically consistent, i.e. it will converge on the correct tree as more data is added. Maximum likelihood generally performs the best of all methods in simulations, but it is very computationally expensive. Unlike parsimony, it explicitly relies upon a specific evolutionary model. minimum evolution: A phylogenetic distance matrix criterion. The best tree is the one in which the sum of the branch lengths is smallest. neighbor-joining: A distance matrix algorithm for inferring trees. It is an approximation to the least-squares and minimum evolution methods. node: A point in a phylogeny where branches meet or end. Nodes at the tip or end of a branch represent taxa. In rooted trees, internal nodes represent common ancestors. parsimony: A phylogenetic criterion for inferring trees with character conflict. Parsimony requires that the best tree is the one with the least character conflict. It is known to produce the incorrect phylogeny in certain cases, such as when evolutionary rates are high or certain branches are long. phenetics: Sometimes known as numerical taxonomy, phenetic methods classify and group organisms based on overall similarity, usually without explicit reference to their phylogenetic relationships. phylogeny: A branching, tree-like diagram representing genealogical relationships among taxa. Rooted phylogenies specify common ancestors and have a time axis. plesiomorphy: A primitive character, shared with the ancestors of a group of organisms. Since it is common to more than just the group being considered, a plesiomorphy is not group-defining. primitive character: See plesiomorphy. root: A common ancestor of all taxa in a phylogeny. Chronologically, the root is the oldest node. synapomorphy: A derived character that is shared between two groups of organisms. UPGMA: A distance matrix-based clustering method for constructing trees. Rarely used, it is very fast but assumes constant evolutionary rates throughout the tree (a property called ultrametricity).

Cladistic methods are often contrasted with "phenetic" methods. Phenetic methods cluster and classify species based upon the number of identical characters that they share, that is, based upon overall similarity. Such methods can run into trouble with organisms like dolphins and tuna, which have many superficial similarities. These organisms, however, are not closely related and should not be classified together if one expects classification to reflect phylogeny.

In contrast, cladistic-based phylogenies group taxa into nested hierarchies, and they are determined using only shared derived characters of organisms, not shared primitive characters (Brooks 1991, pp. 35-36; Kitching et al. 1998, Ch. 1; Maddison and Maddison 1992, p. 49). In technical phylogenetic jargon, primitive characters are called plesiomorphies, and derived characters are called apomorphies. In cladistics, related species are grouped together because they share derived characters (i.e., apomorphies) that originated in a common ancestor of the group, but were not present in other, earlier ancestors of the group. These shared, derived features are called synapomorphies. Primitive and derived are therefore relative terms, depending upon the specific group being considered. For example, backbones are primitive characters of vertebrates, while hair is a derived character particular to mammalian vertebrates. However, when considering mammals only, hair is primitive, whereas an opposable thumb is derived.

In real-life phylogenetic analyses, shared derived characters may be in conflict with other derived characters. Thus, objective methods are required for resolving this character conflict (Kitching et al. 1998, Ch. 1; Maddison and Maddison 1992, p. 49). For instance, wings are a derived character of birds and of bats. Based upon this character alone, the cladistic method would group bats and birds together, which is how the author of Deuteronomy grouped them in the Biblical quote above. However, other shared derived characters indicate that bats should be grouped with wingless mammals, and that birds should be grouped with wingless dinosaurs.

In the past 40 years, several algorithmic methods have been devised to resolve such instances of character conflict and to infer correct phylogenetic trees (Felsenstein 2004, Ch. 10). The following sections outline some of the most successful of these methods. Each method attempts to infer a phylogeny from existing data, and each has its respective strengths and weaknesses. Years of empirical testing and simulation have shown that, in general, these different algorithms, each with very different underlying assumptions, converge on trees that are highly similar when judged statistically (Li 1997, Chs 5 and 6; Nei and Kumar 2000, Chs 6, 7, and .

Maximum parsimony

One of the oldest, most basic, and most frequently used methods for character resolution is the maximum parsimony (MP) criterion (Edwards and Cavalli-Sforza 1963; Kitching et al. 1998). The parsimony criterion mandates that the best tree describing the data is the tree that minimizes the amount of character conflict. For example, consider a dataset containing 10 shared derived characters that group bats with apes (rather than with birds), and with one character that groups bats with birds (rather than apes). According to the parsimony criterion, the tree giving the first grouping should be preferred.

Currently, parsimony is the method of choice for reconstructing morphological trees (Kitching et al. 1998). It is very fast computationally, and it can be robust to differences in evolutionary rate among characters. However, maximum parsimony consistently finds the correct phylogeny only when we expect character conflict to be low or evolution to proceed parsimoniously (Felsenstein 2004, Ch. 9; Kitching et al. 1998, p. 17). If rates of evolution are slow and branches are short, character conflict will be low and parsimony will work well (Felsenstein 2004, Ch. 9; Felsenstein 1981a; Li 1997, p. 128). If character conflict is moderate or high in reality, then it is very unlikely that the true tree will have the least amount of character conflict. When rates of evolution are high, or when some branches are very long, or when the number of possible character states is limited, character conflict can be common. This is often true for nucleotide sequences, which have only four possible character states (A, C, T, or G). In cases such as these, other phylogenetic methods can be more accurate than parsimony.

Maximum likelihood

Another commonly used phylogenetic criterion is maximum likelihood (ML), an effective and robust statistical technique now used in all scientific fields (Edwards and Cavalli-Sforza 1964; Felsenstein 1981b; Fisher 1912). Many well-known statistical estimators are actually maximum likelihood estimators. For example, the common sample average as an estimate of the mean of a Gaussian distribution and the least-squares fit of a line to a set of points are both maximum likelihood estimators. Using ML, one can infer rates of evolution directly from the data and determine the tree that best describes that data given those inferred rates. In other words, ML finds the tree and evolutionary parameters that produce the observed data with the highest probability. Unlike parsimony, ML finds trees with the expected amount of character conflict given the evolutionary rates inferred from the data, even if those rates are high. ML is a computationally intensive method that can be very time-consuming.

Distance methods

Due to their computational speed, distance matrix methods are some of the most popular for inferring phylogenies (Nei and Kumar 2000, Ch. 6). All distance methods transform character data into a matrix of pairwise distances, one distance for each possible pairing of the taxa under study. Distance matrix methods are not cladistic, since the information about derived and primitive characters has been lost during this transformation. Distance methods approach phylogenetic inference strictly as a statistical problem, and they are used almost exclusively with molecular data. Although they are not cladistic, distance methods can be thought of as approximations to cladistic methods, and several of the methods are guaranteed mathematically to converge on the correct tree as more data is included.

The most simple distance metric is merely the number of character differences between two taxa, such as the number of nucleotide differences between two DNA sequences. Many other ways of calculating molecular sequence distances exist, and most attempt to correct for the possibility of multiple changes at a single site during evolution. Methods for calculating distances between sequences are usually named for their originators, such as Kimura's two-parameter (K2P), Jukes-Cantor (JC), Tamura-Nei (TN), Hasegawa, Kishino, and Yano (HKY), and Felsenstein 1984 (F84). Other important distance metrics are General Time Reversible (GTR) and LogDet (Felsenstein 2004, pp. Chs 11 and 13; Nei and Kumar 2000, Chs 2 and 3; Li 1997, Chs 3 and 4).

Once a distance matrix for the taxa being considered is in hand, there are several distance-based criteria and algorithms that may be used to estimate the phylogenetic tree from the data (Felsenstein 2004, Ch. 11; Li 1997, Ch. 5). The minimum evolution (ME) criterion finds the tree in which the sum of all the branch lengths is the smallest. Weighted and unweighted least squares criteria calculate the discrepancy between the observed pairwise distances and the pairwise distances calculated from the branch lengths of the inferred tree. Least squares then finds the tree that minimizes the square of that discrepancy. Least squares methods are some of the most statistically justified and will converge on the correct tree as more data are included in the analysis (given a mathematically proper distance metric). The neighbor-joining (NJ) algorithm is extremely fast and is an approximation of the least squares and minimum evolution methods. If the distance matrix is an exact description of the true tree, then neighbor-joining is guaranteed to reconstruct the correct tree. The UPGMA clustering algorithm (a confusing acronym) is also extremely fast, but it is based upon the unlikely assumption that evolutionary rates are equal in all lineages. UPGMA is rarely used today except as an instructional tool.

Statistical Support for Phylogenies

A phylogeny is a best approximation of the correct, historical tree using a given phylogenetic method. Some phylogenetic analyses are strongly supported by the data, some are weakly supported, and different parts of a tree may have more support than others. When comparing two independently determined phylogenies, one must take into account the statistical support assigned to each branch of the phylogenies. As with all scientific analyses, the details of a phylogenetic tree may change as new information and data are incorporated (Maddison and Maddison 1992, pp. 112-123; Li 1997, pp. 36-146; Felsenstein 1985; Futuyma 1998, p. 99; Hillis and Bull 1993; Huelsenbeck et al. 2001; Swofford et al. 1996, pp. 504-509).

Bootstrapping is the most popular statistical method for assessing the reliability of the branches in a phylogenetic tree (Felsenstein 1985). Bootstrapping is a statistical technique for empirically estimating the variability of a parameter (Efron 1979; Efron and Gong 1983). In a bootstrap analysis, a fictional dataset is created by randomly sampling data from the real dataset until a new dataset is created of the same size. This process is done repeatedly (hundreds or thousands of times), and the parameter of interest is estimated from each fictional dataset. The variability of these bootstrapped estimations is itself an estimate of the variability of the parameter of interest.

In phylogenetics, a new phylogeny is inferred from each bootstrapped dataset (Felsenstein 1985). These bootstrapped phylogenies will likely have different topologies. From these different bootstrapped trees, the variability in the inferred tree can be estimated. The parts of the bootstrapped trees that are in common are ascribed a high confidence, while the parts that vary extensively are assigned a low confidence. Trees constructed from random data do not result in high confidence trees or branches when bootstrapped. Thus, bootstrapping provides one way to test whether a phylogenetic tree is genuine.

Does Phylogenetic Inference Find Correct Trees?

In order to establish their validity in reliably determining phylogenies, phylogenetic methods have been empirically tested in cases where the true phylogeny is known with certainty, since the true phylogeny was directly observed.

• Bacteriophage T7 was propagated and split sequentially in the presence of a mutagen, where each lineage was tracked. Out of 135,135 possible phylogenetic trees, the true tree was correctly determined by phylogenetic methods in a blind analysis. Five different phylogenetic methods were used independently, and each one chose the correct tree (Hillis et al.1992 ).

• In another study, 24 strains of mice were used in which the genealogical relationships were known. Cladistic analysis reproduced almost perfectly the known phylogeny of the 24 strains (Atchely and Fitch 1991).

• Bush et al. used phylogenetic analysis to retrospectively predict the correct evolutionary tree of human Influenza A virus 83% of the time for the flu seasons spanning 1983 to 1994.

• In 1998, researchers used 111 modern HIV-1 (AIDS virus) sequences in a phylogenetic analysis to predict the nucleotide sequence of the viral ancestor of which they were all descendants. The predicted ancestor sequence closely matched, with high statistical probability, an actual ancestral HIV sequence found in an HIV-1 seropositive African plasma sample collected and archived in the Belgian Congo in 1959 (Zhu et al.1998 ).

• In the past decade, phylogenetic analyses have played a significant role in successful convictions in several criminal court cases (Albert et al. 1994; Arnold et al. 1995; Birch et al. 2000; Blanchard et al. 1998; Goujon et al. 2000; Holmes et al. 1993; Machuca et al. 2001; Ou et al. 1992; Veenstra et al. 1995; Vogel 1997; Yirrell et al. 1997), and phylogenetic reconstructions have now been admitted as expert legal testimony in the United States (97-KK- 2220 State of Louisiana v. Richard J. Schmidt [PDF]). The legal test in the U. S. for admissibility of expert testimony is the Daubert guidelines (U. S. Supreme Court Case Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579, 587-89, 113 S. Ct. 2786, 2794, 125 L. Ed. 2d 469, 1993). The Daubert guidelines state that a trial court should consider five factors in determining "whether the testimony's underlying reasoning or methodology is scientifically valid": (1) whether the theory or technique in question can be and has been tested; (2) whether it has been subjected to peer review and publication; (3) its known or potential error rate; (4) the existence and maintenance of standards controlling its operation; and (5) whether it has attracted widespread acceptance within the relevant scientific community (quoted nearly verbatim). Phylogenetic analysis has officially met these legal requirements.

Caveats with Phylogenetic Inference

As with any investigational scientific method, certain conditions must hold in order for the results to be reliable. A common premise of all molecular phylogenetic methods is that genes are transmitted via vertical, lineal inheritance, i.e. from ancestor to descendant. If this premise is violated, gene trees will never recapitulate an organismic phylogeny. This assumption is violated in instances of horizontal transfer, e.g. in transformation of a bacterium by a DNA plasmid, or in retroviral insertion into a host's genome. During the early evolution of life, before the advent of multicellular organisms, horizontal transfer was likely very frequent (as it is today in the observed evolution of bacteria and other unicellular organisms). Thus, it is questionable whether molecular methods are applicable, even in principle, to resolving the phylogeny of the early evolution of life near the most recent common ancestor of all living organisms (Doolittle 1999; Doolittle 2000; Woese 1998).

The list below gives some of the more important caveats that scientists must keep in mind when interpreting the results of a phylogenetic analysis (Swofford 1996, pp. 493-509). In general, the contribution of each of these concerns will be "averaged out" by including more independent characters in the phylogenetic analysis, such as more genes and longer sequences.

• Correlated characters: each character used in the analysis optimally should be genetically independent. Characters that are strongly functionally correlated are better thought of as a single character. There are statistical tests that can help control for unrecognized character correlation, such as the block bootstrap and jackknife.

• True structural convergence: structures that have undergone convergent evolution can artificially result in incorrect tree topologies. Including more characters in the analysis also aids in overcoming convergent effects.

• Character reversals: characters that revert to an ancestral state pose a challenge similar to convergence. Because DNA and RNA only have four different character states, they are especially prone to reversals during evolution.

• Lost characters: lineages that have lost characters (such as whales and their hindlimbs) can also pose cladistic problems. Often, if a cladistic analysis indicates strongly that a certain character has been lost during evolution, it is best to omit this character in higher resolution analyses of that lineage.

• Missing characters: incomplete fossils are problematic, since they may lack important characters. Better fossils are the answer.

• Intractable number of possible phylogenetic trees: for computational reasons, this is one of the most important phylogenetic challenges to overcome. The goal of a phylogenetic reconstruction is to determine the best tree that the data supports. For an analysis of only five species, there are 15 possible trees. For an analysis of 50 species, there are over 10⁷⁴ possible trees that must be searched—which is computationally impossible. This problem is not as bad as it first sounds, since narrowing down the number of reasonable trees can be trivial in many cases (for instance, using the branch and bound algorithm). Several methods have been developed to work around this issue successfully, and ultimately more powerful computers are better.

• Maximum Likelihood assumptions: the maximum likelihood method makes explicit assumptions about the pattern of nucleotide substitutions based upon a given model of nucleotide evolution. These assumptions are based upon a solid statistical foundation; however, the validity of the models must be considered when evaluating the results.

• Long branch attraction: lineages that diverged relatively long ago will tend to "cluster" together in a phylogenetic reconstruction under the appropriate conditions. The mathematical reasons are somewhat complicated, but using more slowly evolving genes (or regions of genes) helps overcome the problem.

• Rate variation between lineages: rates of nucleotide substitution may differ between lineages; this can contribute to long branch attraction and result in incorrect tree topologies. However, maximum likelihood and least squares methods are particularly useful here.

• Rate variation within a single gene: rates of nucleotide substitution can vary along the length of a single gene—this also exacerbates long branch attraction.

• Gene trees are not equivalent to species trees: from simple Mendelian genetics we know that genes segregate individually, and that throughout time individual genes do not necessarily follow organismic genealogy (Avise and Wollenberg 1997; Fitch 1970; Hudson 1992; Nichols 2001; Wu 1991). An obvious example is the fact that while you may have brown eyes, your child may have the genes for blue eyes—but that does not mean your child is not your descendent, or that your brown-eyed children are more closely related to you than your blue-eyed children. Including multiple genes in the analysis is a solution to this conundrum. Based upon simple genetic calculations, an analysis of more than five genes is usually necessary to accurately reconstruct a species phylogeny (Wu 1991).

For more information on cladistics, you can consult one of several excellent online cladistic resources, such as the SASB Introduction to Phylogenetics, UC Berkeley's Integrative Biology Phylogenetics Lab, or Diana Lipscomb's stellar Basics of Cladistic Analysis, downloadable in Adobe Acrobat PDF format. A good, concise description for the layperson can be found at the Journal of Avocational Paleontology. Finally, you can read Charles Darwin's explanation in The Origin of Species of the "Tree of Life", where the concept of a phylogenetic tree was first introduced.

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References

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Introduction Universal Common Descent Defined Evidence for Common Descent is Independent of Mechanism What Counts as Scientific Evidence Other Explanations for the Biology How to Cite This Document Scientific Evidence and the Scientific Method Phylogenetics introduction Figure 1: A consensus universal phylogeny Cladistics and phylogenetic reconstruction Maximum parsimony Maximum likelihood Distance matrix methods Statistical support for phylogenies Does phylogenetic inference find correct trees? Caveats with determining phylogenetic trees	Part I. A unique, historical phylogenetic tree Unity of life Nested hierarchies Convergence of independent phylogenies Statistics of incongruent phylogenies Transitional forms Reptile-birds Reptile-mammals Ape-humans Legged whales Legged seacows Chronology of common ancestors	Part 2. Past history Anatomical vestiges Atavisms Whales and dolphins with hindlimbs Humans tails Molecular vestiges Ontogeny and developmental biology Mammalian ear bones, reptilian jaws Pharyngeal pouches, branchial arches Snake embryos with legs Embryonic human tail Marsupial eggshell and caruncle Present biogeography Past biogeography Marsupials Horses Apes and humans
Part 3. Evolutionary opportunism Anatomical parahomology Molecular parahomology Anatomical convergence Molecular convergence Anatomical suboptimal function Molecular suboptimal function	Part 4. Molecular evidence Protein functional redundancy DNA functional redundancy Transposons Redundant pseudogenes Endogenous retroviruses	Part 5. Change Genetic Morphological Functional The strange past Stages of speciation Speciation events Morphological rates Genetic rates Closing remarks

Let's describe all of these different shaped skulls you evolutionists are finding from a biblical perspective.

Now, i will describe how the grand canyon formed instead of saying that it has eroded from one little river over millions of years.

Another embarrasing problem with the evolutionist's theory is that the earth is slowing down each year in rotation and so that must mean the earth was once faster. Well if you reverse the process millions of years, everything would be flung off the earth and destroyed in outerspace.

Not only that but the sun is getting smaller two inches a year so that must mean it was once bigger. Again if you reverse the process only 25,000 years back then the earth would've been burnt to nothing.

Lastly, the moon is moving away from us atleast an inch a year so that must mean it was once closer. If you reverse this again then that means the moon would be either nonexistent or the gravitational pull would've been to harsh on the earth's surface yanking everything out into space.

The Giant Impact, as pictured in a painting by William K. Hartmann on the cover of Natural History Magazine in 1981. Copyright William K. Hartmann

The idea in a nutshell: At the time Earth formed 4.5 billion years ago, other smaller planetary bodies were also growing. One of these hit earth late in Earth's growth process, blowing out rocky debris. A fraction of that debris went into orbit around the Earth and aggregated into the moon.

---

Half an Hour After the Giant Impact, based on computer modeling by A. Cameron, W. Benz, J. Melosh, and others. Copyright William K. Hartmann

Why this is a good hypothesis:

• The Earth has a large iron core, but the moon does not. This is because Earth's iron had already drained into the core by the time the giant impact happened. Therefore, the debris blown out of both Earth and the impactor came from their iron-depleted, rocky mantles. The iron core of the impactor melted on impact and merged with the iron core of Earth, according to computer models.

• Earth has a mean density of 5.5 grams/cubic centimeter, but the moon has a density of only 3.3 g/cc. The reason is the same, that the moon lacks iron.

• The moon has exactly the same oxygen isotope composition as the Earth, whereas Mars rocks and meteorites from other parts of the solar system have different oxygen isotope compositions. This shows that the moon formed form material formed in Earth's neighborhood.

• If a theory about lunar origin calls for an evolutionary process, it has a hard time explaining why other planets do not have similar moons. (Only Pluto has a moon that is an appreciable fraction of its own size.) Our giant impact hypothesis had the advantage of invoking a stochastic catastrophic event that might happen only to one or two planets out of nine.

Evolution automatically assumes 150 specific different amino acids got together to from a little microorganism in a hostile environment with oxygen that would've caused it to oxidize.

Every so often, someone comes up with the statement "the formation of any enzyme by chance is nearly impossible, therefore abiogenesis is impossible". Often they cite an impressive looking calculation from the astrophysicist Fred Hoyle, or trot out something called "Borel's Law" to prove that life is statistically impossible. These people, including Fred, have committed one or more of the following errors.

Glossary Acyl transferase: An enzyme or ribozyme that synthesizes peptides. Ligase: An enzyme or ribozyme that adds a monomer to a polymer, or links two shorter polymers together. Monomer: Any single subunit of a polymer. An amino acid is a monomer of a peptide or protein, a nucleotide is a monomer of an oligonucleotide or polynucleotide. Nucleotide: Adenine, Guanine, Cytosine and Uracil. These are the monomers that make up oligo- or polynucleotides such as RNA. Oligonucleotide: A short polymer of nucleotide subunits. Polymerase: A enzyme or ribozyme that makes a polymer out of monomers. For example, RNA polymerase makes RNA out of single nucleotides. Ribozyme: A biological catalyst made from RNA. Self-replicator: A molecule which can make an identical or near-identical copy of itself from smaller subunits. At least four self-replicators are known.

Problems with the creationists' "it's so improbable" calculations

1) They calculate the probability of the formation of a "modern" protein, or even a complete bacterium with all "modern" proteins, by random events. This is not the abiogenesis theory at all.

2) They assume that there is a fixed number of proteins, with fixed sequences for each protein, that are required for life.

3) They calculate the probability of sequential trials, rather than simultaneous trials.

4) They misunderstand what is meant by a probability calculation.

5) They seriously underestimate the number of functional enzymes/ribozymes present in a group of random sequences.

I will try and walk people through these various errors, and show why it is not possible to do a "probability of abiogenesis" calculation in any meaningful way.

A primordial protoplasmic globule

So the calculation goes that the probability of forming a given 300 amino acid long protein (say an enzyme like carboxypeptidase) randomly is (1/20)³⁰⁰ or 1 chance in 2.04 x 10³⁹⁰, which is astoundingly, mind-beggaringly improbable. This is then cranked up by adding on the probabilities of generating 400 or so similar enzymes until a figure is reached that is so huge that merely contemplating it causes your brain to dribble out your ears. This gives the impression that the formation of even the smallest organism seems totally impossible. However, this is completely incorrect.

Firstly, the formation of biological polymers from monomers is a function of the laws of chemistry and biochemistry, and these are decidedly not random.

Secondly, the entire premise is incorrect to start off with, because in modern abiogenesis theories the first "living things" would be much simpler, not even a protobacteria, or a preprotobacteria (what Oparin called a protobiont [8] and Woese calls a progenote [4]), but one or more simple molecules probably not more than 30-40 subunits long. These simple molecules then slowly evolved into more cooperative self-replicating systems, then finally into simple organisms [2, 5, 10, 15, 28]. An illustration comparing a hypothetical protobiont and a modern bacteria is given below.

The first "living things" could have been a single self replicating molecule, similar to the "self-replicating" peptide from the Ghadiri group [7, 17], or the self replicating hexanucleotide [10], or possibly an RNA polymerase that acts on itself [12].

Another view is the first self-replicators were groups of catalysts, either protein enzymes or RNA ribozymes, that regenerated themselves as a catalytic cycle [3, 5, 15, 26, 28]. An example is the SunY three subunit self-replicator [24]. These catalytic cycles could be limited in a small pond or lagoon, or be a catalytic complex adsorbed to either clay or lipid material on clay. Given that there are many catalytic sequences in a group of random peptides or polynucleotides (see below) it's not unlikely that a small catalytic complex could be formed.

These two models are not mutually exclusive. The Ghadiri peptide can mutate and form catalytic cycles [9].

No matter whether the first self-replicators were single molecules, or complexes of small molecules, this model is nothing like Hoyle's "tornado in a junkyard making a 747". Just to hammer this home, here is a simple comparison of the theory criticised by creationists, and the actual theory of abiogenesis.

Note that the real theory has a number of small steps, and in fact I've left out some steps (especially between the hypercycle-protobiont stage) for simplicity. Each step is associated with a small increase in organisation and complexity, and the chemicals slowly climb towards organism-hood, rather than making one big leap [4, 10, 15, 28].

Where the creationist idea that modern organisms form spontaneously comes from is not certain. The first modern abiogenesis formulation, the Oparin/Haldane hypothesis from the 20's, starts with simple proteins/proteinoids developing slowly into cells. Even the ideas circulating in the 1850's were not "spontaneous" theories. The nearest I can come to is Lamarck's original ideas from 1803! [8]

Given that the creationists are criticising a theory over 150 years out of date, and held by no modern evolutionary biologist, why go further? Because there are some fundamental problems in statistics and biochemistry that turn up in these mistaken "refutations".

The myth of the "life sequence"

Another claim often heard is that there is a "life sequence" of 400 proteins, and that the amino acid sequences of these proteins cannot be changed, for organisms to be alive.

This, however, is nonsense. The 400 protein claim seems to come from the protein coding genome of Mycobacterium genetalium, which has the smallest genome currently known of any modern organism [20]. However, inspection of the genome suggests that this could be reduced further to a minimal gene set of 256 proteins [20]. Note again that this is a modern organism. The first protobiont/progenote would have been smaller still [4], and preceded by even simpler chemical systems [3, 10, 11, 15].

As to the claim that the sequences of proteins cannot be changed, again this is nonsense. There are in most proteins regions where almost any amino acid can be substituted, and other regions where conservative substitutions (where charged amino acids can be swapped with other charged amino acids, neutral for other neutral amino acids and hydrophobic amino acids for other hydrophobic amino acids) can be made. Some functionally equivalent molecules can have between 30 - 50% of their amino acids different. In fact it is possible to substitute structurally non-identical bacterial proteins for yeast proteins, and worm proteins for human proteins, and the organisms live quite happily.

The "life sequence" is a myth.

Coin tossing for beginners and macromolecular assembly

So let's play the creationist game and look at forming a peptide by random addition of amino acids. This certainly is not the way peptides formed on the early Earth, but it will be instructive.

I will use as an example the "self-replicating" peptide from the Ghadiri group mentioned above [7]. I could use other examples, such as the hexanucleotide self-replicator [10], the SunY self-replicator [24] or the RNA polymerase described by the Eckland group [12], but for historical continuity with creationist claims a small peptide is ideal. This peptide is 32 amino acids long with a sequence of RMKQLEEKVYELLSKVACLEYEVARLKKVGE and is an enzyme, a peptide ligase that makes a copy of itself from two 16 amino acid long subunits. It is also of a size and composition that is ideally suited to be formed by abiotic peptide synthesis. The fact that it is a self replicator is an added irony.

The probability of generating this in successive random trials is (1/20)³² or 1 chance in 4.29 x 10⁴⁰. This is much, much more probable than the 1 in 2.04 x 10³⁹⁰ of the standard creationist "generating carboxypeptidase by chance" scenario, but still seems absurdly low.

However, there is another side to these probability estimates, and it hinges on the fact that most of us don't have a feeling for statistics. When someone tells us that some event has a one in a million chance of occuring, many of us expect that one million trials must be undergone before the said event turns up, but this is wrong.

Here is a experiment you can do yourself: take a coin, flip it four times, write down the results, and then do it again. How many times would you think you had to repeat this procedure (trial) before you get 4 heads in a row?

Now the probability of 4 heads in a row is is (1/2)⁴ or 1 chance in 16: do we have to do 16 trials to get 4 heads (HHHH)? No, in successive experiments I got 11, 10, 6, 16, 1, 5, and 3 trials before HHHH turned up. The figure 1 in 16 (or 1 in a million or 1 in 10⁴⁰) gives the likelihood of an event in a given trial, but doesn't say where it will occur in a series. You can flip HHHH on your very first trial (I did). Even at 1 chance in 4.29 x 10⁴⁰, a self-replicator could have turned up surprisingly early. But there is more.

1 chance in 4.29 x 10⁴⁰ is still orgulously, gobsmackingly unlikely; it's hard to cope with this number. Even with the argument above (you could get it on your very first trial) most people would say "surely it would still take more time than the Earth existed to make this replicator by random methods". Not really; in the above examples we were examining sequential trials, as if there was only one protein/DNA/proto-replicator being assembled per trial. In fact there would be billions of simultaneous trials as the billions of building block molecules interacted in the oceans, or on the thousands of kilometers of shorelines that could provide catalytic surfaces or templates [2,15].

Let's go back to our example with the coins. Say it takes a minute to toss the coins 4 times; to generate HHHH would take on average 8 minutes. Now get 16 friends, each with a coin, to all flip the coin simultaneously 4 times; the average time to generate HHHH is now 1 minute. Now try to flip 6 heads in a row; this has a probability of (1/2)⁶ or 1 in 64. This would take half an hour on average, but go out and recruit 64 people, and you can flip it in a minute. If you want to flip a sequence with a chance of 1 in a billion, just recruit the population of China to flip coins for you, you will have that sequence in no time flat.

So, if on our prebiotic earth we have a billion peptides growing simultaneously, that reduces the time taken to generate our replicator significantly.

Okay, you are looking at that number again, 1 chance in 4.29 x 10⁴⁰, that's a big number, and although a billion starting molecules is a lot of molecules, could we ever get enough molecules to randomly assemble our first replicator in under half a billion years?

Yes, one kilogram of the amino acid arginine has 2.85 x 10²⁴ molecules in it (that's well over a billion billion); a tonne of arginine has 2.85 x 10²⁷ molecules. If you took a semi-trailer load of each amino acid and dumped it into a medium size lake, you would have enough molecules to generate our particular replicator in a few tens of years, given that you can make 55 amino acid long proteins in 1 to 2 weeks [14,16].

So how does this shape up with the prebiotic Earth? On the early Earth it is likely that the ocean had a volume of 1 x 10²⁴ litres. Given an amino acid concentration of 1 x 10^-6 M (a moderately dilute soup, see Chyba and Sagan 1992 [23]), then there are roughly 1 x 10⁵⁰ potential starting chains, so that a fair number of efficent peptide ligases (about 1 x 10³¹) could be produced in a under a year, let alone a million years. The synthesis of primitive self-replicators could happen relatively rapidly, even given a probability of 1 chance in 4.29 x 10⁴⁰ (and remember, our replicator could be synthesized on the very first trial).

Assume that it takes a week to generate a sequence [14,16]. Then the Ghadiri ligase could be generated in one week, and any cytochrome C sequence could be generated in a bit over a million years (along with about half of all possible 101 peptide sequences, a large proportion of which will be functional proteins of some sort).

Although I have used the Ghadiri ligase as an example, as I mentioned above the same calculations can be performed for the SunY self replicator, or the Ekland RNA polymerase. I leave this as an exercise for the reader, but the general conclusion (you can make scads of the things in a short time) is the same for these oligonucleotides.

Search spaces, or how many needles in the haystack?

So I've shown that generating a given small enzyme is not as mind-bogglingly difficult as creationists (and Fred Hoyle) suggest. Another misunderstanding is that most people feel that the number of enzymes/ribozymes, let alone the ribozymal RNA polymerases or any form of self-replicator, represent a very unlikely configuration and that the chance of a single enzyme/ribozyme forming, let alone a number of them, from random addition of amino acids/nucleotides is very small.

However, an analysis by Ekland suggests that in the sequence space of 220 nucleotide long RNA sequences, a staggering 2.5 x 10¹¹² sequences are efficent ligases [12]. Not bad for a compound previously thought to be only structural. Going back to our primitive ocean of 1 x 10²⁴ litres and assuming a nucleotide concentration of 1 x 10^-7 M [23], then there are roughly 1 x 10⁴⁹ potential nucleotide chains, so that a fair number of efficent RNA ligases (about 1 x 10³⁴) could be produced in a year, let alone a million years. The potential number of RNA polymerases is high also; about 1 in every 10²⁰ sequences is an RNA polymerase [12]. Similar considerations apply for ribosomal acyl transferases (about 1 in every 10¹⁵ sequences), and ribozymal nucleotide synthesis [1, 6, 13].

Similarly, of the 1 x 10¹³⁰ possible 100 unit proteins, 3.8 x 10⁶¹ represent cytochrome C alone! [29] There's lots of functional enyzmes in the peptide/nucleotide search space, so it would seem likely that a functioning ensemble of enzymes could be brewed up in an early Earth's prebiotic soup.

So, even with more realistic (if somewhat mind beggaring) figures, random assemblage of amino acids into "life-supporting" systems (whether you go for protein enzyme based hypercycles [10], RNA world systems [18], or RNA ribozyme-protein enzyme coevolution [11, 25]) would seem to be entirely feasible, even with pessimistic figures for the original monomer concentrations [23] and synthesis times.

Conclusions

The very premise of creationists' probability calculations is incorrect in the first place as it aims at the wrong theory. Furthermore, this argument is often buttressed with statistical and biological fallacies.

At the moment, since we have no idea how probable life is, it's virtually impossible to assign any meaningful probabilities to any of the steps to life except the first two (monomers to polymers p=1.0, formation of catalytic polymers p=1.0). For the replicating polymers to hypercycle transition, the probability may well be 1.0 if Kauffman is right about catalytic closure and his phase transition models, but this requires real chemistry and more detailed modelling to confirm. For the hypercycle->protobiont transition, the probability here is dependent on theoretical concepts still being developed, and is unknown.

However, in the end life's feasibility depends on chemistry and biochemistry that we are still studying, not coin flipping.

References

[1] Unrau PJ, and Bartel DP, RNA-catalysed nucleotide synthesis. Nature, 395: 260-3, 1998

[2] Orgel LE, Polymerization on the rocks: theoretical introduction. Orig Life Evol Biosph, 28: 227-34, 1998

[3] Otsuka J and Nozawa Y. Self-reproducing system can behave as Maxwell's demon: theoretical illustration under prebiotic conditions. J Theor Biol, 194, 205-221, 1998

[4] Woese C, The universal ancestor. Proc Natl Acad Sci USA, 95: 6854-6859.

[5] Varetto L, Studying artificial life with a molecular automaton. J Theor Biol, 193: 257-85, 1998

[6] Wiegand TW, Janssen RC, and Eaton BE, Selection of RNA amide synthases. Chem Biol, 4: 675-83, 1997

[7] Severin K, Lee DH, Kennan AJ, and Ghadiri MR, A synthetic peptide ligase. Nature, 389: 706-9, 1997

[8] Ruse M, The origin of life, philosophical perspectives. J Theor Biol, 187: 473-482, 1997

[9] Lee DH, Severin K, Yokobayashi Y, and Ghadiri MR, Emergence of symbiosis in peptide self-replication through a hypercyclic network. Nature, 390: 591-4, 1997

[10] Lee DH, Severin K, and Ghadri MR. Autocatalytic networks: the transition from molecular self-replication to molecular ecosystems. Curr Opinion Chem Biol, 1, 491-496, 1997

[11] Di Giulio M, On the RNA world: evidence in favor of an early ribonucleopeptide world. J Mol Evol, 45: 571-8, 1997

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[16] Ertem G, and Ferris JP, Synthesis of RNA oligomers on heterogeneous templates. Nature, 379: 238-40, 1996

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[18] Joyce GF, Building the RNA world. Ribozymes. Curr Biol, 6: 965-7, 1996

[19] Ishizaka M, Ohshima Y, and Tani T, Isolation of active ribozymes from an RNA pool of random sequences using an anchored substrate RNA. Biochem Biophys Res Commun, 214: 403-9, 1995

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Evolution is stating that life arose from nonlife which violates biogenesis.

Another thing is that living and nonliving things alike only tend toward disorder over time so there is no way you can even come close to going from microorganism to full-fledged human no matter how many years they add onto the theory of evolution.

Another thing evolutionists argue about is since most of our DNA is similar to apes then most likely we originated from them. Just because we are similar in DNA does not mean we are related in any way,shape,or form.

Now I will describe why i think some very scarcely populated dinosaurs still walk the earth today.

I watched your debate with The Way of The Master and i was disturbed with you and your wife pointing fingers at religion for starting many problems within the United States.

Well there are multiple things that are not rational in this world that rationality cannot explain.

Take a look at this next excerpt carefully from the King James Bible Romans 1:20...

Evolution is purely a religion.

There is no real correct evidence to support the theory but only very loose evidence that has no good ground at all.

The following was compiled by Yellow_Number_Five:
On Observed Speciation and Speciation Models:

Salamanders and Songbirds

More details on the salamanders, with additional links

London mosquitos

Another article on Himalayan song birds

Speciation by reinforcement



Lots of examples here

More examples

Speciation models

Links on examples and models

More on the London mosquitos

Ringed-speciation model and examples, plus links

In Drosophila (fruit flies)




On Behavior, Reciprocal Altruism and the Evolution of Behavior:

Behavior models (registration required)

The Origins of Right and Wrong in Humans and Other Animals

Chimps show sense of fair play

Genes, altruism and evolution (with more links)

Reciprocal altruism

Kin selection and reciprocal altruism



On the Evolution of Complexity



Biochemistry

Evolution of human intellect and diet

Evolution of language

Music and the relation to language (registration required)

Evolution of religious memes

Bacteria flagella

Avida Digital evolution

More on the evolution of religion

Complex evolution in the laboratory

The eye

The brain

You have to beleive there is no God and you have to beleive we came from nothing.

I don't want you sitting in hell someday forever realizing that God was offering his loving hands out to you all along and you ignored that.

Let me just give you a small idea of this place called hell if God exists.

Well just for so you will know, once the rapture has taken place and God has taken all of his faithful children back home, Satan will arise from the deep and set up a peace agreement between every country and God's wrath will be poured out throughout the entire world.

Stop laughing off the Christian faith because God cares about you and doesn't want you to carry on like this. Jesus is the WAY the TRUTH and the LIFE. He is real. I GAURANTEE.

PorkChop wrote:

Wait.

What's that?

Do you hear something?

It's the sound of Hamby saying "Give me a 'Hunts houseflies with a bazooka' badge."

 

Nice work Hamby.