Satellite DNA is Essential and Species-Specific in Drosophila melanogaster

Seems Incompatible

This week’s “we thought it was junk but it turned out to be crucial” study comes with the added bonus that the so-called “junk” is also species-specific / taxonomically restricted. The general topic is tandemly repeated satellite DNA in the much studied fruit fly, Drosophila melanogaster. These satellite DNA regions comprise 15-20% of D. melanogaster’s genome, and one of the regions, AAGAG(n), is transcribed across many of D. melanogaster’s cell types.

While evolutionists have hoped and argued that transcription (not to mention mere presence) does not imply function (after all biology is one big hack-job, so RNA polymerase doesn’t always know what it is doing), D. melanogaster is once again not cooperating. Not only is the satellite DNA ubiquitous and widely transcribed, the AAGAG RNA was found to be important for male fertility. Kind of important.

But it gets worse. Much worse.

Not only is D. melanogaster’s satellite DNA ubiquitous, widely transcribed across many cell types, and of crucial importance, it is species-specific. The levels of AAGAG satellite DNA is orders of magnitude lower in D. simulans and D. sechellia, and nearly absent in other species within the Drosophila genus.

This makes no sense on evolution. Now we must say that not only does a massive quantity of AAGAG satellite DNA abruptly appear in a particular fly species, but it immediately takes on an absolutely crucial role. A role which, of course, was somehow already fulfilled in the putative evolutionary ancestor.

In other words, the function in question (male fertility) was rumbling along just fine, and then with a new species, and not in many of its sister species, the crucial function was somehow rewired and reassigned to a relatively new, massive, DNA satellite sequence.

This is absurd.

Even the paper admits that, “Finally, it is worth noting that the expression of simple satellites for essential functions seems incompatible with the fast evolution of satellite DNAs, reflected in dramatic changes in both sequence types and copy numbers across species.”

Ya think?

The next step will be for evolutionists to convert this spectacular failure into compelling evidence that evolution can produce DNA that is both (i) species-specific, and (ii) functionally essential.

And why is that true?

Because, after all, the satellite DNA evolved, of course. And since it is species-specific and essential, we now have evidence evolution can produce such an unexpected outcome.

That’s just good, solid, scientific research.

Religion drives science, and it matters.

Finally, the Details of How Proteins Evolve

A Step-By-Step Description

How did proteins evolve? It is a difficult question because, setting aside many other problems, the very starting point—the protein-coding gene—is highly complex. A large number of random mutations would seem to be required before you have a functional protein that helps the organism. Too often such problems are solved with vague accounts of “adaptations” and “selection pressure” doing the job. But this week researchers at the University of Illinois announced ground-breaking research that provides a step-by-step, detailed, description of the evolution of a new protein-coding gene and associated regulatory DNA sequences. The protein in question is a so-called “antifreeze” protein that keeps the blood of Arctic codfish from freezing, and the new research provides the specific sequence of mutations, leading to the new gene. It would be difficult to underestimate the importance of this research. It finally provides scientific details answering the age-old question of how nature’s massive complexity could have arisen. As the paper triumphantly declares, “Here, we report clear evidence and a detailed molecular mechanism for the de novo formation of the northern gadid (codfish) antifreeze glycoprotein (AFGP) gene from a minimal noncoding sequence.” Or as lead researcher, professor Christina Cheng, explained, “This paper explains how the antifreeze protein in the northern codfish evolved.” This is a monumental finding. Having the scientific details, down to the level of specific mutations, of how a new protein-coding gene evolved—not from a related gene but from non-coding DNA—is something evolutionists could only dream of only a few short years ago. There’s only one problem: it is all junk science.

The first problem is that this new “research” is, in actuality, a just-so story:

In science and philosophy, a just-so story is an unverifiable narrative explanation for a cultural practice, a biological trait, or behavior of humans or other animals. The pejorative nature of the expression is an implicit criticism that reminds the hearer of the essentially fictional and unprovable nature of such an explanation. Such tales are common in folklore and mythology.

For example, the antifreeze protein is of relatively low complexity chiefly consisting a repeating sequence of three amino acids (threonine-alanine-alanine), and the evolutionists claim that these repeating sequences “strongly suggest” that the protein-coding gene “evolved from repeated duplications of an ancestral 9-nucleotide threonine-alanine-alanine-coding element.”

Why is that true?

Why does a repeating genetic sequence “strongly suggest” that it “evolved from repeated duplications?” What experiment revealed this truth? What evidence gives us this profound principle? The answer, of course, is that there is none. Nowhere do the evolutionists justify this claim because there is no empirical justification.

There is no scientific evidence for it. Zero.

The paper continues with yet more non-empirical claims. Those nine nucleotides “likely originated within a pair of conserved 27-nucleotide” segments that flank each side of the repetitive region. And these four 27-nucleotide segments are similar to each other, “indicating they resulted from the duplication of an initial copy.” As the paper concludes, “chance duplications” of an ancestral 27-nucleotide segment “produced four tandem copies.”

But why are those claims true? Why do such similarities imply an origin via evolutionary mechanisms? The problem is, they don’t. There is no empirical evidence for any of this. This is completely evidence-free.

The evolutionists next explain that the 9-nucleotide segment duplicated a large number of times because it worked well:

We hypothesize that, upon the onset of selective pressure from cold polar marine conditions, duplications of a 9-nt ancestral element in the midst of the four GCA-rich duplicates occurred.

The above quote is an example of the non-empirical, teleology that pervades evolutionary thought. It was upon the onset of cold conditions that the needed genetic duplications occurred. This is not empirical; this is story-telling.

The paper continues with a series of one-time, contingent events crucial to their story and non-empirical claims. The genetic sequence “was appropriately delimited by an existing in-frame termination codon.”

Appropriately delimited?

The presence of a region in two of the species “indicates that it existed in the gadid ancestor before the emergence of the AFGP.” The absence of a thymine nucleotide at a location in some of the species “very likely resulted from a deletion event,” causing a fortuitous frameshift which supplied the crucial signal peptide segment, telling cellular machinery that the protein should be secreted to the bloodstream. As the paper concludes, “the emerging AFGP gene was thus endowed with the necessary secretory signal.”

Endowed with the necessary signal?

There is no empirical evidence for any of this.

Another problem with this just-so account, is the substantial level of serendipity required. The new antifreeze protein did not arise from some random DNA sequence, but rather from crucial, preexisting segments of DNA that just happened to be lying around. In other words, the fish were facing a colder environment, they needed some antifreeze in their blood, and the pieces needed for such an antifreeze gene were fortuitously available.

The authors hint at this serendipity when they conclude that their story of how this protein evolved is an example of “evolutionary ingenuity.”

Evolutionary ingenuity?

The press release is even more revealing. Cheng admits that the evolution of this gene “occurred as a result of a series of seemingly improbable, serendipitous events.” For “not just any random DNA sequence can produce a viable protein.” Furthermore, in addition to the gene itself, “several other serendipitous events occurred.”

The DNA was “edited in just the right way,” and “somehow, the gene also obtained the proper control sequence that would allow the new gene to be transcribed into RNA.”

Even the evolutionists admit to the rampant serendipity. Nonetheless they are triumphant, for “the findings offer fresh insights into how a cell can invent ‘a new, functional gene from scratch.’”

Fresh insights?

In actuality the findings arose from a series of non-empirical claims.

Religion drives science, and it matters.

What is a Dependency Graph?

Information Organization

A recent paper, authored by Winston Ewert, uses a dependency graph approach to model the relationships between the species. This idea is inspired by computer science which makes great use of dependency graphs.

Complicated software applications typically use a wealth of lower level software routines. These routines have been developed, tested, and stored in modules for use by higher level applications. When this happens the application inherits the lower-level software and has a dependency on that module.

Such applications are written in human-readable languages such as Java. They then need to be translated into machine language. The compiler tool performs the translation, and the build tool assembles the result, along with the lower level routines, into an executable program. These tools use dependency graphs to model the software, essentially building a design diagram, or blueprint which shows the dependencies, specifying the different software modules that will be needed, and how they are connected together.

Dependency graphs also help with software design. Because they provide a blueprint of the software architecture, they are helpful in designing decoupled architectures and promoting software reuse.

Dependency graphs are also used by so-called “DevOps” teams to assist at deployment time in sequencing and installing the correct modules.

What Ewert has shown is that, as with computer applications which inherit software from a diverse range of lower-level modules, and those lower-level modules likewise feed into a diverse range of applications, biology’s genomes likewise reveal such patterns. Genomes may inherit molecular sequence information from a wide range of genetic modules, and genetic modules may feed into a diverse range of genomes.

Superficially, from a distance, this may appear as the traditional evolutionary tree. But that model has failed repeatedly as scientists have studied the characters of species more closely. Dependency graphs, on the other hand, provide a far superior model of the relationships between the species, and their genetic information flow.

New Paper Demonstrates Superiority of Design Model

Ten Thousand Bits?

Did you know Mars is going backwards? For the past few weeks, and for several weeks to come, Mars is in its retrograde motion phase. If you chart its position each night against the background stars, you will see it pause, reverse direction, pause again, and then get going again in its normal direction. And did you further know that retrograde motion helped to cause a revolution? Two millennia ago, Aristotelian physics dictated that the Earth was at the center of the universe. Aristarchus’ heliocentric model, which put the Sun at the center, fell out of favor. But what Aristotle’s geocentrism failed to explain was retrograde motion. If the planets are revolving about the Earth, then why do they sometimes pause, and reverse direction? That problem fell to Ptolemy, and the lessons learned are still important today.

Ptolemy explained anomalies such as retrograde motion with additional mechanisms, such as epicycles, while maintaining the circular motion that, as everyone knew, must be the basis of all motion in the cosmos. With less than a hundred epicycles, he was able to model, and predict accurately the motions of the cosmos. But that accuracy came at a cost—a highly complicated model.

In the Middle Ages William of Occam pointed out that scientific theories ought to strive for simplicity, or parsimony. This may have been one of the factors that drove Copernicus to resurrect Aristarchus’ heliocentric model. Copernicus preserved the required circular motion, but by switching to a sun-centered model, he was able to reduce greatly the number of additional mechanisms, such as epicycles.

Both Ptolemy’s and Copernicus’ models accurately forecast celestial motion. But Copernicus was more parsimonious. A better model had been found.

Kepler proposed ellipses, and showed that the heliocentric model could become even simpler. It was not well accepted though because, as everyone knew, celestial bodies travel in circles. How foolish to think they would travel along elliptical paths. That next step toward greater parsimony would have to wait for the likes of Newton, who showed that Kepler’s ellipses were dictated by his new, highly parsimonious, physics. Newton described a simple, universal, gravitational law. Newton’s gravitational force would produce an acceleration, which could maintain orbital motion in the cosmos.

But was there really a gravitational force? It was proportional to the mass of the object which was then cancelled out to compute the acceleration. Why not have gravity cause an acceleration straightaway?

Centuries later Einstein reported on a man in Berlin who fell out of a window. The man didn’t feel anything until he hit the ground! Einstein removed the gravitational force and made the physics even simpler yet.

The point here is that the accuracy of a scientific theory, by itself, means very little. It must be considered along with parsimony. This lesson is important today in this age of Big Data. Analysts know that a model can always be made more accurate by adding more terms. But are those additional terms meaningful, or are they merely epicycles? It looks good to drive the modeling error down to zero by adding terms, but when used to make future forecasts, such models perform worse.

There is a very real penalty for adding terms and violating Occam’s Razor, and today advanced algorithms are available for weighing the tradeoff between model accuracy and model parsimony.

This brings us to common descent, a popular theory for modeling relationships between the species. As we have discussed many times here, common descent fails to model the species, and a great many additional mechanisms—biological epicycles—are required to fit the data.

And just as cosmology has seen a stream of ever improving models, the biological models can also improve. This week a very important model has been proposed in a new paper, authored by Winston Ewert, in the Bio-Complexity journal.

Inspired by computer software, Ewert’s approach models the species as sharing modules which are related by a dependency graph. This useful model in computer science also works well in modeling the species. To evaluate this hypothesis, Ewert uses three types of data, and evaluates how probable they are (accounting for parsimony as well as fit accuracy) using three models.

Ewert’s three types of data are: (i) Sample computer software, (ii) simulated species data generated from evolutionary / common descent computer algorithms, and (iii) actual, real species data.

Ewert’s three models are: (i) A null model in which entails no relationships between
any species, (ii) an evolutionary / common descent model, and (iii) a dependency graph model.

Ewert’s results are a Copernican Revolution moment. First, for the sample computer software data, not surprisingly the null model performed poorly. Computer software is highly organized, and there are relationships between different computer programs, and how they draw from foundational software libraries. But comparing the common descent and dependency graph models, the latter performs far better at modeling the software “species.” In other words, the design and development of computer software is far better described and modeled by a dependency graph than by a common descent tree.

Second, for the simulated species data generated with a common descent algorithm, it is not surprising that the common descent model was far superior to the dependency graph. That would be true by definition, and serves to validate Ewert’s approach. Common descent is the best model for the data generated by a common descent process.

Third, for the actual, real species data, the dependency graph model is astronomically superior compared to the common descent model.

Let me repeat that in case the point did not sink in. Where it counted, common descent failed compared to the dependency graph model. The other data types served as useful checks, but for the data that mattered—the actual, real, biological species data—the results were unambiguous.

Ewert amassed a total of nine massive genetic databases. In every single one, without exception, the dependency graph model surpassed common descent.

Darwin could never have even dreamt of a test on such a massive scale.

Darwin also could never have dreamt of the sheer magnitude of the failure of his theory. Because you see, Ewert’s results do not reveal two competitive models with one model edging out the other.

We are not talking about a few decimal points difference. For one of the data sets (HomoloGene), the dependency graph model was superior to common descent by a factor of 10,064. The comparison of the two models yielded a preference for the dependency graph model of greater than ten thousand.

Ten thousand is a big number.

But it gets worse, much worse.

Ewert used Bayesian model selection which compares the probability of the data set given the hypothetical models. In other words, given the model (dependency graph or common descent), what is the probability of this particular data set? Bayesian model selection compares the two models by dividing these two conditional probabilities. The so-called Bayes factor is the quotient yielded by this division.

The problem is that the common descent model is so incredibly inferior to the dependency graph model that the Bayes factor cannot be typed out. In other words, the probability of the data set given the dependency graph model, is so much greater than the probability of the data set given the common descent model, that we cannot type the quotient of their division.

Instead, Ewert reports the logarithm of the number. Remember logarithms? Remember how 2 really means 100, 3 means 1,000, and so forth?

Unbelievably, the 10,064 value is the logarithm (base value of 2) of the quotient! In other words, the probability of the data on the dependency graph model is so much greater than that given the common descent model, we need logarithms even to type it out. If you tried to type out the plain number, you would have to type a 1 followed by more than 3,000 zeros!

That’s the ratio of how probable the data are on these two models!

By using a base value of 2 in the logarithm we express the Bayes factor in bits. So the conditional probability for the dependency graph model has a 10,064 advantage of that of common descent.

10,064 bits is far, far from the range in which one might actually consider the lesser model. See, for example, the Bayes factor Wikipedia page, which explains that a Bayes factor of 3.3 bits provides “substantial” evidence for a model, 5.0 bits provides “strong” evidence, and 6.6 bits provides “decisive” evidence.

This is ridiculous. 6.6 bits is considered to provide “decisive” evidence, and when the dependency graph model case is compared to comment descent case, we get 10,064 bits.

But it gets worse.

The problem with all of this is that the Bayes factor of 10,064 bits for the HomoloGene data set is the very best case for common descent. For the other eight data sets, the Bayes factors range from 40,967 to 515,450.

In other words, while 6.6 bits would be considered to provide “decisive” evidence for the dependency graph model, the actual, real, biological data provide Bayes factors of 10,064 on up to 515,450.

We have known for a long time that common descent has failed hard. In Ewert’s new paper, we now have detailed, quantitative results demonstrating this. And Ewert provides a new model, with a far superior fit to the data.

Centrobin Found to be Important in Sperm Development

Numerous, Successive, Slight Modifications

Proteins are a problem for theories of spontaneous origins for many reasons. They consist of dozens, or often hundreds, or even thousands of amino acids in a linear sequence, and while many different sequences will do the job, that number is tiny compared to the total number of sequences that are possible. It is a proverbial needle-in-the-haystack problem, far beyond the reach of blind searches. To make matters worse, many proteins are overlapping, with portions of their genes occupying the same region of DNA. The same set of mutations would have to result in not one, but two proteins, making the search problem that much more tricky. Furthermore, many proteins perform multiple functions. Random mutations somehow would have to find those very special proteins that can perform double duty in the cell. And finally, many proteins perform crucial roles within a complex environment. Without these proteins the cell sustains a significant fitness degradation. One protein that fits this description is centrobin, and now a new study shows it to be even more important than previously understood.

Centrobin is a massive protein of almost a thousand amino acids. Its importance in the division of animal cells has been known for more than ten years. An important player in animal cell division is the centrosome organelle which organizes the many microtubules—long tubes which are part of the cell’s cytoskeleton. Centrobin is one of the many proteins that helps the centrosome do its job. Centrobin depletion causes “strong disorganization of the microtubule network,” and impaired cell division.

Now, a new study shows just how important centrobin is in the development of the sperm tail. Without centrobin, the tail, or flagellum, development is “severely compromised.” And once the sperm is formed, centrobin is important for its structural integrity. As the paper concludes:

Our results underpin the multifunctional nature of [centrobin] that plays different roles in different cell types in Drosophila, and they identify [centrobin] as an essential component for C-tubule assembly and flagellum development in Drosophila spermatogenesis.

Clearly centrobin is an important protein. Without it such fundamental functions as cell division and organism reproduction are severely impaired.

And yet how did centrobin evolve?

Not only is centrobin a massive protein, but there are no obvious candidate intermediate structures. It is not as though we have that “long series of gradations in complexity” that Darwin called for:

Although the belief that an organ so perfect as the eye could have been formed by natural selection, is enough to stagger any one; yet in the case of any organ, if we know of a long series of gradations in complexity, each good for its possessor, then, under changing conditions of life, there is no logical impossibility in the acquirement of any conceivable degree of perfection through natural selection.

Unfortunately, in the case of centrobin, we do not know of such a series. In fact, centrobin would seem to be a perfectly good example of precisely how Darwin said his theory could be falsified:

If it could be demonstrated that any complex organ existed, which could not possibly have been formed by numerous, successive, slight modifications, my theory would absolutely break down. But I can find out no such case.  

Darwin could “find out no such case,” but he didn’t know about centrobin. Darwin required “a long series of gradations,” formed by “numerous, successive, slight modifications.”

With centrobin we are nowhere close to fulfilling these requirements. In other words, today’s science falsifies evolution. This, according to Darwin’s own words.

Religion drives science, and it matters.

Rewrite the Textbooks (Again), Origin of Mitochondria Blown Up

There You Go Again

Why are evolutionists always wrong? And why are they always so sure of themselves? With the inexorable march of science, the predictions of evolution, which evolutionists were certain of, just keep on turning out false. This week’s failure is the much celebrated notion that the eukaryote’s power plant—the mitochondria—shares a common ancestor with the alphaproteobacteria. A long time ago, as the story goes, that bacterial common ancestor merged with an early eukaryote cell. And these two entities, as luck would have it, just happened to need each other. Evolution had just happened to create that early bacterium, and that early eukaryote, in such a way that they needed, and greatly benefited from, each other. And, as luck would have it again, these two entities worked together. The bacterium would just happen to produce the chemical energy needed by the eukaryote, and the eukaryote would just happen to provide needed supplies. It paved the way for multicellular life with all of its fantastic designs. There was only one problem: the story turned out to be false.

The story that mitochondria evolved from the alphaproteobacteria lineage has been told with great conviction. Consider the Michael Gray 2012 paper which boldly begins with the unambiguous truth claim that “Viewed through the lens of the genome it contains, the mitochondrion is of unquestioned bacterial ancestry, originating from within the bacterial phylum α-Proteobacteria (Alphaproteobacteria).”

There was no question about it. Gray was following classic evolutionary thinking: similarities mandate common origin. That is the common descent model. Evolutionists say that once one looks at biology through the lens of common descent everything falls into place.

Except that it doesn’t.

Over and over evolutionists have to rewrite their theory. Similarities once thought to have arisen from a common ancestor turn out to contradict the common descent model. Evolutionists are left having to say the similarities must have arisen independently.

And big differences, once thought to show up only in distant species, keep on showing up in allied species.

Biology, it turns out, is full of one-offs, special cases, and anomalies. The evolutionary tree model doesn’t work.

Now, a new paper out this week has shown that the mitochondria and alphaproteobacteria don’t line up the way originally thought. That “unquestioned bacterial ancestry” turns out to be, err, wrong.

The paper finds that mitochondria did not evolve from the currently hypothesized alphaproteobacterial ancestor, or from “any other currently recognized alphaproteobacterial lineage.”

The paper does, however, make a rather startling claim. The authors write:

our analyses indicate that mitochondria evolved from a proteobacterial lineage that branched off before the divergence of all sampled alphaproteobacteria.

Mitochondria evolved from a proteobacterial lineage, predating the alphaproteobacteria?

That is a startling claim because, well, simply put there is no evidence for it. The lack of evidence is exceeded only by the evolutionist’s confidence. Note the wording: “indicate.”

The evolutionist’s analyses indicate this new truth.

How can the evolutionists be so sure of themselves in the absence of literally any evidence?

The answer is, because they are evolutionists. They are completely certain that evolution is true. And since evolution must be true, the mitochondria had to have evolved from somewhere. And the same is true for the alphaproteobacteria. They must have evolved from somewhere.

And in both cases, that somewhere must be the earlier proteobacterial lineage. There are no other good evolutionary candidates.

Fortunately this new claim cannot be tested (and therefore cannot be falsified), because the “proteobacterial lineage” is nothing more than an evolutionary construct. Evolutionists can search for possible extant species for hints of a common ancestor with the mitochondria, but failure to find anything can always be ascribed to extinction of the common ancestor.

This is where evolutionary theory often ends up: failures ultimately lead to unfalsifiable truth claims. Because heaven forbid we should question the theory itself.

Religion drives science, and it matters.

Andreas Wagner: Genetic Regulation Drives Evolutionary Change

A Hall of Mirrors

A new paper from Andreas Wagner and co-workers argues that a key and crucial driver of evolution is changes to the interaction between transcription factor proteins and the short DNA sequences to which they bind. In other words, evolution is driven by varying the regulation of protein expression (and a particular type of regulation—the transcription factor-DNA binding) rather than varying the structural proteins themselves. Nowhere does the paper address or even mention the scientific problems with this speculative idea. For example, if evolution primarily proceeds by random changes to transcription factor-DNA binding, creating all manner of biological designs and species, then from where did those transcription factors and DNA sequences come? The answer—that they evolved for some different, independent, function; itself an evolutionary impossibility—necessitates astronomical levels of serendipity. Evolution could not have had foreknowledge. It could not have known that the emerging transcription factors and DNA sequence would, just luckily, be only a mutation away from some new function. This serendipity problem has been escalating for years as evolutionary theory has repeatedly failed, and evolutionists have applied ever more complex hypotheses to try to explain the empirical evidence. Evolutionists have had to impute to evolution increasingly sophisticated, complex, higher-order, mechanisms. And with each one the theory has become ever more serendipitous. So it is not too surprising that evolutionists steer clear of the serendipity problem. Instead, they cite previous literature as a way of legitimizing evolutionary theory. Here I will show examples of how this works in the new Wagner paper.

The paper starts right off with the bold claim that “Changes in the regulation of gene expression need not be deleterious. They can also be adaptive and drive evolutionary change.” That is quite a statement. To support it the paper cites a classic 1975 paper by Mary-Claire King and A. C. Wilson entitled “Evolution at two levels in humans and chimpanzees.” The 1975 paper admits that the popular idea and expectation that evolution occurs by mutations in protein-coding genes had largely failed. The problem was that, at the genetic level, the two species were too similar:

The intriguing result, documented in this article, is that all the biochemical methods agree in showing that the genetic distance between humans and the chimpanzee is probably too small to account for their substantial organismal differences.

Their solution was to resort to a monumental shift in evolutionary theory: evolution would occur via the tweaking of gene regulation.

We suggest that evolutionary changes in anatomy and way of life are more often based on changes in the mechanisms controlling the expression of genes than on sequence changes in proteins. We therefore propose that regulatory mutations account for the major biological differences between humans and chimpanzees.

In other words, evolution would have to occur not by changing proteins, but by changing protein regulation. What was left unsaid was that highly complex, genetic regulation mechanisms would now have to be in place, a priori, in order for evolution to proceed.

Where did those come from?

Evolution would have to create highly complex, genetic regulation mechanisms so that evolution could occur.

Not only would this ushering in of serendipity to evolutionary theory go unnoticed, it would, incredibly, be cited thereafter as a sort of evidence, in its own right, showing that evolution occurs by changes to protein regulation.

But of course the 1975 King-Wilson paper showed no such thing. The paper presupposed the truth of evolution, and from there reasoned that evolution must have primarily occurred via changes to protein regulation. Not because anyone could see how that could occur, but because the old thinking—changes to proteins themselves—wasn’t working.

This was not, and is not, evidence that changes in the regulation of gene expression can be “adaptive and drive evolutionary change,” as the Wagner paper claimed.

But this is how the genre works. The evolution literature makes unfounded claims that contradict the science, and justifies those claims with references to other evolution papers which do the same thing. It is a web of deceit.

Ultimately it all traces back to the belief that evolution is true.

The Wagner paper next cites a 2007 paper that begins its very first sentence with this unfounded claim:

It has long been understood that morphological evolution occurs through alterations of embryonic development.

I didn’t know that. And again, references are provided. This time to a Stephen Jay Gould book and a textbook, neither of which demonstrate that “morphological evolution occurs through alterations of embryonic development.”

These sorts of high claims by evolutionists are ubiquitous in the literature, but they never turn out to be true. Citations are given, and those in turn provide yet more citations. And so on, in a seemingly infinite hall of mirrors, where monumental assertions are casually made and immediately followed by citations that simply do the same thing.

Religion drives science, and it matters.

Brochosome Proteins Encoded By Orphan Genes

A Pattern Problem

A few years ago Paul Nelson debated Joel Velasco on the topic of design and evolution. Nelson masterfully demonstrated design in nature. For his part Velasco also provided an excellent defense of evolution. But the Epicurean claim that the world arose via random chance is not easy to defend, and Velasco’s task would be challenging. Consider, for example, the orphans which Nelson explained are a good example of taxonomically-restricted designs. Such designs make no sense on evolution, and though Velasco responded with many rebuttals, none were very convincing. Since that debate the orphan problem has become worse, as highlighted by a new study of brochosomes.

Background

The term orphan refers to a DNA open reading frame, or ORF, without any known similar sequence in other species or lineages, and hence ORFan or “orphan.” Since orphans are unique to a particular species or lineage, they contradict common ancestry’s much celebrated nested hierarchy model.

The Nelson-Valasco Debate

Velasco addressed the orphan problem with several arguments. First, Velasco reassured the audience that there isn’t much to be concerned with here because “Every other puzzle we’ve ever encountered in the last 150 years has made us even more certain of a fact that we already knew, that we’re all related.”

Second, Velasco argued that the whole orphan problem is contrived, as it is nothing more than a semantic misunderstanding—a confusion of terms. These are nothing more than open reading frames without significant similarity to any known sequence.

Third, Velasco argued that many of the orphans are so categorized merely because the search for similar sequence is done only in “very distantly related” species.

Furthermore, and fourth, Velasco argued that orphans are really nothing more than a gap in our knowledge. For the more we know about a species, the more the orphan problem goes away. And which species do we know the most about? Ourselves of course. And we have no orphans: “Well what about humans, we know a lot about humans. How many orphan genes are in humans? What do you think? Zero.”

In fact, and fifth, Velasco argued that while new orphans are discovered with each new genome that is decoded, the trend is slowing and is suggestive that in the long run relatives for these orphans will be found: “In fact if you trend the absolute number going up, as opposed to the percentage of orphan genes in organisms, that number is going down.”

So to summarize Velasco’s position, the orphan problem will be solved so don’t worry about, but actually orphans are not a problem at all but rather a semantic misunderstanding, but on the other hand the orphan problem is a consequence of incomplete genomic data, but actually on the other hand the problem is a consequence of insufficient knowledge about the species, and in any case even though the number of known orphans keeps on rising, they will eventually go away because the orphans, as a percentage of the overall genomic data (which has been exploding exponentially) is going down.

This string of evolution arguments reminds us of the classic dog-owner’s defense: He’s not my dog, he didn’t bite you, and besides you hit the dog first anyway. Not surprisingly, each of Velasco’s arguments fails, as I explained here.

In fact, there are many orphans, and while function can be difficult to identify, it has been found for many orphans. As science writer Helen Pilcher explained:

In corals, jellyfish and polyps, orphan genes guide the development of explosive stinging cells, sophisticated structures that launch toxin-filled capsules to stun prey. In the freshwater polyp Hydra, orphans guide the development of feeding tentacles around the organism’s mouth. And the polar cod’s orphan antifreeze gene enables it to survive life in the icy Arctic.

Up to a third of genomes have been found have been found to be unique, as this review explains:

Comparative genome analyses indicate that every taxonomic group so far studied contains 10–20% of genes that lack recognizable homologs in other species. Do such ‘orphan’ or ‘taxonomically-restricted’ genes comprise spurious, non-functional ORFs, or does their presence reflect important evolutionary processes? Recent studies in basal metazoans such as Nematostella, Acropora and Hydra have shed light on the function of these genes, and now indicate that they are involved in important species-specific adaptive processes. 

And this is yet another failed prediction of evolution, as this paper explains:

The frequency of de novo creation of proteins has been debated. Early it was assumed that de novo creation should be extremely rare and that the vast majority of all protein coding genes were created in early history of life. However, the early genomics era lead to the insight that protein coding genes do appear to be lineage-specific. Today, with thousands of completely sequenced genomes, this impression remains.

Why then was Velasco so confident and almost nonchalant in his argumentation? Why was he so assured that, one way or another, the orphan problem was not a problem? And why did he believe there are zero orphans in humans, and so it merely is a matter of studying biology, and the orphans will go away?

Lander Orphan Study

It could be due to a significant 2007 study from Eric Lander’s group which rejected most of the large number (several thousands) of orphans that had been tentatively identified in the human genome. The study confidently concluded that “the vast majority” of the orphans were “spurious”:

The analysis here addresses an important challenge in genomics— determining whether an ORF truly encodes a protein. We show that the vast majority of ORFs without cross-species counterparts [i.e., orphans] are simply random occurrences. The exceptions appear to represent a sufficiently small fraction that the best course is would be [sic] consider such ORFs as noncoding in the absence of direct experimental evidence.

The authors went on to propose that “it is time to undertake a thorough revision of the
human gene catalogs by applying this principle to filter the entries.”

That peer-reviewed paper, in a leading journal, was well received (e.g., Larry Moran called it an “excellent study”) and it certainly appeared to be authoritative. So it is not surprising that Velasco would be confident about orphans. For all appearances, they really were no problem for evolution.

There was just one problem. This was all wrong.

There was no scientific evidence that those human sequences, identified as orphans, were “spurious.” The methods used in the Lander study were full of evolutionary assumptions. The results entirely hinged on evolution. Although the paper did not explicitly state this, without the assumption of evolution no such conclusions could have been made.

This is what philosophers refer to as theory-ladenness. Although the paper authoritatively concluded the vast majority of the orphans in the human genome were spurious, this was not an empirical observation or inference, as it might seem to some readers. Their data (and proposed revisions to human gene catalogs), methods, and conclusions were all laden, at their foundation, with the theory of evolution.

So Velasco’s argument was circular. To defend evolution he claimed there were zero orphans in the human genome, but that “fact” was a consequence of assuming evolution is true in the first place. If the assumption of evolution is dropped, then there is no evidence for that conclusion.

Brochosomes

Since the Nelson-Velasco debate the orphan problem has just gotten worse. Consider, for example, brochosomes which are intricate, symmetric, secretory granules forming super-oily coatings on the integuments of leafhoppers. Brochosomes develop in glandular segments of the leafhopper’s Malpighian tubules.

The main component of brochosomes, as shown in a recent paper, is proteins. And these constituent proteins, as well as brochosome-associated proteins, are mostly encoded by orphan genes.

As the paper explains, most of these proteins “appear to be restricted to the superfamily Membracoidea, adding to the growing list of cases where taxonomically restricted genes, also called orphans, encode important taxon-specific traits.”

And how did all these orphan genes arise so rapidly? The paper hypothesizes that “It is possible that secreta exported from the organism may evolve especially rapidly because they are not strongly constrained by interactions with other traits.”

That evolutionists can so easily reach for just-so stories, such as this, is yet another example of how false predictions have no consequence for evolutionary theory. Ever since Darwin evolutionists have proclaimed how important it is that the species fall into the common descent pattern. This has especially been celebrated at the molecular level.

But of course the species fall into no such pattern, and when obvious examples present themselves, such as the brochosome proteins, evolutionists do not miss a step.

There is no empirical content to this theory. Predictions hailed as great successes and confirmations of the truth of evolution suddenly mean nothing and have no consequence when the falsification becomes unavoidable.

Religion drives science, and it matters.

h/t: El Hombre

Embryonic Development Reveals Staggering Complexity

Oh My

I recently cited a paper on the evolution of embryonic development and how the evidence contradicts evolutionary theory and common descent. Even the evolutionists, though in understated terms, admitted there were problems. Evolutionary analyses are “reaching their limits,” it is difficult to “conclude anything about evolutionary origins,” genetic similarities “do not necessarily imply common ancestry,” and “conserved regulatory networks can become unrecognizably divergent.” In other words, like all other disciplines within the life sciences, embryonic development is not working. The science contradicts the theory.

But there is much more to the paper, and as a reader noticed, the authors give a rather blunt admission of the magnitude of the problem, not often seen in the literature:

One of the main reasons for Duboule’s pessimism about the return of the EvoDevo comet is the staggering complexity and diversity of cellular and developmental regulatory processes. The configuration space for realistic models of such systems is vast, high dimensional, and potentially infinitely complex.

Staggering complexity? Staggering diversity? The configuration space is vast and high-dimensional?

And it is potentially infinitely complex?

And we are to believe this is the product of random mutations?

Religion drives science, and it matters.

About That RNA World Hypothesis

It Just Doesn’t Make Sense

Given its widespread popularity and acceptance you might not have realized that the so-called RNA-World hypothesis suffers from some dramatic problems. At the top of the list is the rather awkward fact that there is, err, no evidence for it. While skeptics have pointed this out for years, we now see evolutionists coming clean on this inconvenient truth as well. To wit, here is how Peter Wills and Charles Carter open their recent BioSystems paper:

The RNA World is a widely-embraced hypothetical stage of molecular evolution, devoid of protein enzymes, in which all functional catalysts were ribozymes. Only one fact concerning the RNA World can be established by direct observation: if it ever existed, it ended without leaving any unambiguous trace of itself.

Even this is a bit of an understatement. Because without the prior assumption of evolution, which can and has underwritten a wide range of speculation, there is precisely zero reason to believe this wild hypothesis. No organisms have ever been discovered that demonstrate the RNA World hypothesis in action. Nor have scientists ever constructed any such organisms in their laboratories. This is not too surprising because no one has even produced anything remotely close to a detailed design of how such organisms could function.

Wills and Carter also point out negative evidences such as catalysis (RNA enzymes lack the ability to function over a wide range of temperatures) and the “impossible obstacles” to the hypothetical yet necessary transition from the RNA World to something resembling today’s extant cells. As Carter explains:

Such a rise from RNA to cell-based life would have required an out-of-the-blue appearance of an aaRS [aminoacyl-tRNA synthetase]-like protein that worked even better than its adapted RNA counterpart. That extremely unlikely event would have needed to happen not just once but multiple times—once for every amino acid in the existing gene-protein code. It just doesn’t make sense.

Indeed, it just doesn’t make sense. And yet in spite of these obvious problems, the RNA World has been a textbook staple, presented as a plausible and likely example of how early life evolved.

Religion drives science, and it matters.

How Embryonic Development Bears on Evolution

Follow the Theory

In order for evolution to have occurred, the intricate embryonic development stages of species must have evolved. Indeed, the developmental pathways of the species would be crucial in such a process. If we are to believe the evolutionary claim that the species spontaneously arose, then untold embryonic development pathways must have somehow undergone massive change. But while evolutionists expected the study of such evolution of development to yield great insight into the evolutionary process and history, it has underwhelmed. This shortcoming is well known, as exemplified in this 2015 paper:

First, traditional comparative approaches to the evolution of development—whether focused on the morphological or on the molecular/genetic level—are reaching their limits in terms of explanatory power.

Except that this is an overstatement. To say that comparative approaches “are reaching their limits in terms of explanatory power” is to suggest that there was, at one time, some significant level of explanatory power provided. That would be a very optimistic interpretation of the data.

The paper continues:

The more we learn about the evolution of pattern-forming gene networks, or the ontogeny of complex morphological traits, the more it becomes clear that it is less than straightforward to conclude anything about evolutionary origins or dynamics based on such comparisons alone.

“Less than straightforward”? Let’s be clear—a more accurate descriptor would be “impossible.” In fact, the evidence does not reveal an evolutionary history, but rather is supported by the theory. Evolutionary theory does not follow the data, as Huxley prescribed, but rather the data follow the theory.

The paper continues:

On the one hand, homoplasy or convergent evolution abounds at all levels of investigation. One of the most lauded major insights of EvoDevo is that a common toolkit of genes and signaling pathways is reused over and over again to create a large diversity of different body plans, shapes, and organs.

Most lauded major insights? That would be the mother of all euphemisms. Evolutionists are always rationalizing devastating contradictions as teachable moments, and here we have yet another example. To cast the nonsensical finding of a “common toolkit” as a “major insight” is laughable.

This becomes clear as the paper continues:

Because of this, similarities in gene expression patterns or morphological structure often do not necessarily imply common ancestry, since they may as well reflect the frequent reuse of the same regulatory or morphogenetic modules.

Profound similarities “do not necessarily imply common ancestry.” We have now entered a Lewis Carroll world, as Sober would put it. The whole point of evolution was that such similarities revealed and mandated common descent. But now, we have the exact opposite, as similarities cannot be due to common descent, but must have arisen independently. And this is an “insight”? A fundamental prediction is demolished and evolutionists do not skip a beat. This is not science.

But it gets worse:

On the other hand, developmental system drift allows conserved networks to change considerably in terms of their component genes and regulatory interactions without changing the phenotypic outcomes such systems produce. This means that even functionally conserved regulatory networks can become unrecognizably divergent at the molecular and genetic level, especially across large evolutionary time spans.

We have now reached the height of absurdity. First, profound developmental similarities were found which could not be ascribed to common descent. Now we find that those developmental pathways which can (theoretically) be ascribed to common descent are profoundly different.

When will this bad dream end? The science contradicts the theory. Over. And over. And over. And over.

It never ends. Religion drives science, and it matters.

[h/t: El Hombre]

Evolutionists: Our Findings Suggest That Similarities in Bilateria Evolved Independently

Not Even Wrong

This week one of the top scientific journals in the world published what would seem to be a ground breaking paper. The paper claims to have found evidence for the independent evolution of nervous system similarities across the Bilateria. As the abstract explains:

Our findings … suggest that the similarities in dorsoventral patterning and trunk neuroanatomies evolved independently in Bilateria.

By the end of the manuscript the authors are even more confident:

Therefore, the expression of dorsoventral transcription factors evolved independently from the trunk neuroanatomy at least in certain bilaterian lineages

This is a monumental claim, but there is only one problem: It is blatantly false. The paper’s findings did not “suggest” the evolution, independent or otherwise, of the transcription factor expression patterns. They certainly did not demonstrate, show or find such an incredible conclusion.

It would be difficult to overstate how misleading this paper is. It provided literally zero evidence for any such evolution. Nothing. Nada.

There simply is no such scientific evidence in the paper. The claim that they found that the expression of dorsoventral transcription factors evolved independently in certain bilaterian lineages is not even wrong.

Let’s be clear about this. I am not saying their claim is weak. I am not saying their claim is faulty. I am not saying they failed to make their case conclusively. The problem is they don’t have any case at all.

We cannot criticize the science because, well, there is no science. For a paper entitled “Convergent evolution of bilaterian nerve cords,” one would have expected at least some evidence and explanation for the evolution of bilaterian nerve cords.

Unfortunately papers such as this inform journalists and science writers. They report that scientists have now discovered yet another aspect of evolution. It is yet another example of how science proves evolution.

In fact, if one is looking for a meaningful takeaway, what the study did find is that the expectations of evolution—that nervous system similarities would align with the evolutionary tree—turned out to be, like so many other of evolution’s predictions—false. But that doesn’t fit the narrative.

Religion drives science, and it matters.

Protein Mutations Are Highly Coupled

A Rugged Fitness Landscape

A new study from Michael Harms’ laboratory at the University of Oregon finds that potential amino acid substitutions in protein sequences are highly coupled. That is, if one residue mutates to a new amino acid, the swap impacts the other possible substitutions—they now have a different impact on the protein tertiary structure. As the paper explains:

Proteins exist as ensembles of similar conformations. The effect of a mutation depends on the relative probabilities of conformations in the ensemble, which in turn, depend on the exact amino acid sequence of the protein. Accumulating substitutions alter the relative probabilities of conformations, thereby changing the effects of future mutations. This manifests itself as subtle but pervasive high-order epistasis. Uncertainty in the effect of each mutation accumulates and undermines prediction. Because conformational ensembles are an inevitable feature of proteins, this is likely universal.

This coupling leads to a “profound unpredictability in evolution,” and the authors conclude that “detailed evolutionary predictions are not possible given the chemistry of macromolecules.”

This finding seems to confirm what many evolutionists have said for decades—that evolution is a contingent, not law-like, process:

These [macro]evolutionary happenings are unique, unrepeatable, and irreversible.” – Theodosius Dobzhansky, 1957.

Laws and experiments are inappropriate techniques” for explaining evolutionary events and processes. – Ernst Mayr

What science needs are “plausible scenarios for a fully material universe, even if those scenarios cannot be currently tested.” – Victor Stenger, 2004

any replay of the tape would lead evolution down a pathway radically different from the road actually taken. – Stephen Jay Gould

All of this is in direct contradiction to the science, which reveals undeniable patterns in biology that have been repeated over and over. From the pervasive instances of convergence, recurrence, and all kinds of other “ence’s”, to the non adaptive patterns discussed by Michael Denton, the biological is anything but haphazard or random. Clearly, the same solution, for whatever reason, is used repeatedly across a wide range of species, in various patterns.

This is a clear falsification of an evolutionary expectation expressed across many years, and widely held by a consensus of experts.

But there is another problem with these protein findings. In addition to confirming the complexity and coupling of protein folding, the findings also seem to corroborate what theoretical and experimental studies have shown for years, that the fitness landscape of macromolecules in general, and proteins in particular, is rugged.

The problem of evolving a protein is difficult for several reasons. First, protein function drops off rapidly with only a few mutations. Very quickly a protein loses its function as you move away from the native sequence.

Second, random or starting sequences are stuck in a flat and rugged fitness landscape. There is little sign of a the kind of smooth and gradually increasing fitness landscape that would aid evolution’s enormous task of figuring out how proteins could evolve.

These problems are just getting worse, and this new finding a good example of that trend.

Religion drives science, and it matters.

Blindness in Cave Fish is Due to Epigenetics

Evolutionists Say “We See”

A recent paper out of Brant Weinstein’s and William Jeffery’s laboratories on eye development, or the lack thereof, in blind cave fish has important implications for evolutionary theory (paper discussed here). The study finds that the loss of eyes in fish living in dark Mexican caves is not due to genetic mutations, as evolutionists have vigorously argued for many years, but due to genetic regulation. Specifically, methylation of key development genes represses their expression and with it eye development in this venerable icon of evolution. But the finding is causing yet more problems for evolutionary theory.

Darwin appealed to the blind cave fish in his one long argument for evolution. It is a curious argument in many ways, and the first sign of problems was in Darwin’s presentation where he flipped between two different explanations. At one point he explained the loss of vision in the cave fish as an example of evolutionary change not due to his key mechanism, natural selection. Instead, the Sage of Kent resorted to using the Lamarckian mechanism or law of “use and disuse.” Privately Darwin despised and harshly criticized Lamarck, but when needed he occasionally employed his French forerunner’s ideas.

Elsewhere Darwin hit upon a natural selection-based mechanism for the blind cave fish, explaining that elimination of the costly and unneeded vision system would surely raise the fitness of the hapless creatures.

This latter explanation would become a staple amongst latter day evolutionary apologists, convinced that it mandates the fact of evolution. Anyone who has discussed or debated evolutionary theory with today’s Epicureans has likely encountered this curious argument that because blind cave fish lost their eyes, therefore the world must have arisen by itself.

Huh?

To understand the evolutionary logic, or lack thereof, one must understand the history of ideas, and in particular the idea of fixity, or immutability, of species. According to evolutionists, species are either absolutely fixed in their designs, or otherwise there are no limits to their evolutionary changes and the biological world, and everything else for that matter, spontaneously originated.

Any evidence, for any kind of change, no matter how minor, is immediately yet another proof text for evolution, in all that the word implies.

Of course, from a scientific perspective, the evidence provides precisely zero evidence for evolution. Evolution requires the spontaneous (i.e., by natural processes without external input) creation of an unending parade of profound designs. The cave fish evidence shows the removal, not creation, of such a design.

The celebration of such evidence and argument by Darwin and his disciples reveals more about evolutionists than evolution. That they would find this argument persuasive reveals their underlying metaphysics and the heavy lifting it performs. It is all about religion.

We are reminded of all this with the news of Weinstein’s new study. But we also see something new: The insertion, yet again, of Lamarck into the story. The irony is that the epigenetics, now revealed as the cause of repressed eye development in the cave fish, hearkens back to Lamarck.

Darwin despised Lamarck and later evolutionists made him the third rail in biology. Likewise they have pushed back hard against the scientific findings of epigenetics and their implications.

The environment must not drive biological change.

False.

Well such biological change must not be transgenerational.

False.

Well such inheritance must not be long lasting, or otherwise robust.

False again.

This last failure is revealed yet again in the new blind cave fish findings.

False predictions count. A theory that is repeatedly wrong, over and over, in all of its fundamental expectations, will eventually be seen for what it is.

The rise of epigenetics is yet another such major failure. Evolutionists pushed back against it because it makes no sense on the theory, and that means it cannot now be easily accommodated.

One problem is that epigenetics is complex. The levels of coordination and intricacy of mechanism are far beyond evolution’s meager resources.

It’s not going to happen.

Another problem is the implied serendipity. For instance, one epigenetic mechanism involves the molecular tags places on the tails of the DNA packing proteins called histones. While barcoding often seems to be an apt metaphor for epigenetics, the tagging of histone tails can influence the histone three dimensional structures. It is not merely an information-bearing barcode. Like the tiny rudder causing the huge ship to change course, the tiny molecular tag can cause the much larger packing proteins to undergo conformational change, resulting in important changes in gene accessibility and expression.

This is all possible because of the special, peculiar, structure and properties of the histone protein and its interaction with DNA. With evolution we must believe this just happened to evolve for no reason, and thus fortuitously enabled the rise of epigenetics.

Another problem with epigenetics is that it is worthless, in evolutionary terms that is. The various mechanisms that sense environmental shifts and challenges, attach or remove one of the many different molecular tags to one of the many different DNA or histone locations, propagate these messages across generations, and so forth, do not produce the much needed fitness gain upon which natural selection operates.

The incredible epigenetics mechanisms are helpful only at some yet to be announced future epoch when the associated environmental challenge presents itself. In the meantime, selection is powerless and according to evolution the incredible system of epigenetics, that somehow just happened to arise from a long, long series or random mutations, would wither away with evolution none the wiser.

These are the general problems with epigenetics. In the case of the blind cave fish, however, there is possible explanation. It is a longshot, but since this case specifically involves the loss of a stage of the embryonic development, evolutionists can say that genetic mutations caused changes in the methylating proteins, causing them to be overactive.

This explanation relies on the preexistence of the various epigenetic mechanisms, so does not help to resolve the question of how they could have evolved. What the explanation does provide is a way for evolutionists to dodge the bullet presented by the specter of the cave fish intelligently responding to an environmental shift.

Such teleology in the natural world is not allowed.

So the evolutionary prediction is that these proteins will be found to have particular random changes causing an increase in their methylation function, in particular at key locations in key genes (i.e., the genes associated eye development).

That’s a long shot, and an incredible violation of Occam’s Razor.

My predictions are that (i) this evolutionary prediction will fail just as the hundreds that came before, and (ii) as with those earlier failures, this failure will do nothing to open the evolutionist’s eyes.

Religion drives science, and it matters.

New Book: New Proteins Evolve Very Easily

No Free Lunch

We have seen that a new evolution book co-authored by evolutionist Dennis Venema and Scot McKnight is influenced by the mythical Warfare Thesis (here and here) and makes erroneous arguments that the fossils, echolocation, and pseudogenes support evolution (here,  here and here). We now move on to another topic: protein evolution. Proteins are composed of a linear string of amino acids, often hundreds in length, and perform all sorts of important tasks in the cell. They could not have evolved by any stretch of the imagination, and so pose a rather difficult problem for evolutionists. Our new book on evolution attempts to resolve this problem with a claim that has long since been understood to be false. In fact, the claim, properly understood, provides yet more scientific evidence against evolution.

The problem of protein evolution

For evolution to work biology must be chocked full of structures that can arise via long, gradual evolutionary pathways. Mutations must be able to slowly accumulate, gradually improving the structure. In other words, the “fitness landscape” must be smooth and gradual, not rugged or precipitous.

That evolutionary expectation has been found to be false many times, and proteins are no exception. It is now clear that for a given protein, only a few changes to its amino acid sequence can be sustained before the protein function is all but eliminated. Here is how one paper explained it:

The accepted paradigm that proteins can tolerate nearly any amino acid substitution has been replaced by the view that the deleterious effects of mutations, and especially their tendency to undermine the thermodynamic and kinetic stability of protein, is a major constraint on protein evolvability—the ability of proteins to acquire changes in sequence and function.

In other words, protein function precipitously drops off with only a tiny fraction of its amino acids altered. It is not a gradual fitness landscape. Another paper described the protein fitness landscape as rugged.

Therefore it is not surprising that various studies on evolving proteins have failed to show a viable mechanism. One study concluded that 10^63 attempts would be required to evolve a relatively short protein. And a similar result (10^65 attempts required) was obtained by comparing protein sequences. Another study found that 10^64 to 10^77 attempts are required, and another study concluded that 10^70 attempts would be required.

So something like 10^70 attempts are required yet evolutionists estimate that only 10^43 attempts are possible. In other words, there is a shortfall of 27 orders of magnitude.

But it gets worse. The estimate that 10^43 attempts are possible is utterly unrealistic. For it assumes billions of years are available, and that for that entire time the Earth is covered with bacteria, constantly churning out mutations and new protein experiments. Aside from the fact that these assumptions are entirely unrealistic, the estimate also suffers from the rather inconvenient fact that those bacteria are, err, full of proteins. In other word, for evolution to evolve proteins, they must already exist in the first place.

This is absurd. And yet, even with these overly optimistic assumptions, evolution falls short by 27 orders of magnitude.

The numbers don’t add up. Proteins reveal scientific problems for evolution. What is interesting is how evolutionists react to these problems.

The “solution” to protein evolution

A common solution cited by evolutionists for the problem of protein evolution is the case of nylonases—enzymes that rapidly arose in bacteria, in the last century, and are able to breakdown byproducts of the nylon manufacturing process. The idea here is that these byproducts of the nylon manufacturing process were present in the bacteria’s environment for the first time. The bacteria had never been exposed to such chemicals, and yet in an evolutionary blink of an eye, were able to produce proteins to metabolize the new chemicals. Does this not demonstrate that the chance origin of a protein-coding genes is not a problem? Proteins could have evolved with no problem, after all, we just witnessed it occur with the origin of nylonases. As the new book explains, protein evolution “appears to be trivial for evolution to achieve.” [86]

Unfortunately this icon of evolution is an enormous misrepresentation of the science.

The science

The evolutionary claim that the nylonases demonstrate how easy protein evolution is non scientific for several reasons. Indicators of this include that fact that the nylonases evolved so rapidly—in an entirely unrealistic time frame under evolution, and that they arose in bacteria with thousands of preexisting proteins. Again, this evolutionary claim of how proteins evolve is circular, it requires the preexistence of proteins.

None of this is feasible given the problems of protein evolution discussed above. The scientific inference would be that the bacteria developed the nylonases because those chemicals they metabolize were present in the environment. In other words, directed adaptation.

Indeed, this is precisely what researchers in the field have concluded. They hypothesize that the new metabolism capability is a stress response, an adaptation to a challenging environment. In other words, the environment influenced the adaptation. This is not a case of evolutionary change. The nylonase enzymes did not arise from a random search over sequence space until the right enzymes were luckily found and could be selected for. That would have required eons of time, and is far beyond evolution’s capability, as we have seen. Instead, cellular structures rapidly formed new enzymes, due to the environmental change.

Indeed, such adaptation to nylon manufacture byproducts has been repeated in laboratory experiments. In a matter of months bacteria acquire the ability to digest the unforeseen chemical. Researchers speculate that mechanisms responding to environmental stress are involved in inducing adaptive mutations.

This does not demonstrate protein evolution. In fact it refutes evolution. Evolution does not have the resources to have created directed adaptation mechanisms. And even if it did, such mechanisms would not have been selected for because they provide no immediate fitness improvement.

This is not evidence that protein-coding genes can evolve by chance. A new gene, arising within a modern cell responding to an environmental challenge, is not analogous to chance origin. Unfortunately evolutionists have a long history of inappropriately claiming otherwise (for example, see here and here).

We have seen that this new evolution book makes erroneous arguments that the fossils, echolocation, and pseudogenes support evolution. We now see another erroneous argument for protein evolution.

All these arguments and evidences are typical. They are icons of evolution, and it is astonishing how durable they are in the evolution literature given their complete failure.

If evolution was indicated by the science I would be the first to sign up. But in fact it is an age-old religious idea that makes no sense on the science. And likewise this new book is an utter disaster. The confection immediately crumbles under even a little probing.

Religion drives science, and it matters.

New Book: Olfactory Receptor Genes Prove Common Descent

The “Shared Error” Argument

We have seen that a new evolution book co-authored by evolutionist Dennis Venema and Scot McKnight is influenced by the mythical Warfare Thesis (here and here) and makes erroneous arguments that the fossils and echolocation support evolution (here and here). We now move on to another topic: broken genes, or pseudogenes. This is a popular argument amongst evolutionists and Venema uses as his example the olfactory receptor genes. The idea here is that, in different species (such as the human and chimpanzee), the same damaging mutation can be found in the same pseudogenes. When we find the same strange spelling mistake in the homework of different students we conclude that plagiarism occurred. It is more likely that the mistake had one source, rather than occurred twice, independently. Likewise, the same mutation in different species points to a single source in a common ancestor—common descent. Furthermore, we don’t see mutations that violate the expected pattern. Clearly common descent is the obvious, most parsimonious explanation. As Venema concludes, common descent is “overwhelmingly supported.” [36] This is a powerful argument for evolution that has influenced many people. There’s only one problem: It fails historically, philosophically, and scientifically.

First, the olfactory system is profoundly complex. Odors entering the nose interact with finely-tuned receptor proteins (created from the olfactory receptor genes), setting off an incredible cascade of events in the cell, resulting in an electrical signal sent to the brain. Studies have found that each cell expresses only a single olfactory receptor gene, and so is sensitive to a particular odor. At the brain, the signals are grouped and organized by odor. In other words, for all the cells in the nose expressing the same olfactory receptor gene (and thus sensitive to the same odor), their signals spatially converge as they feed into the brain area.

And of course, as with all the senses, These incoming signals are providing mere electrical information. There is no odor, or light, or sound entering the brain via these nerve cells. Instead, a bunch of electrical signals are entering the brain via these nerve cells. The brain, by itself, has no way of knowing what these electrical signals mean. It must somehow be given the source and meaning of these incoming signals. It then processes and interprets these signals and the end result is that we are conscious of images reported by our eyes, sounds reported by our ears, smells reported by our nose, and so forth. All of this defies evolution, and should give us pause.

Second, the evolutionist’s contention that common descent is needed to explain those shared mutations in different species contradicts the most basic biology. Simply put, similarities across species which cannot be explained by common descent, are rampant in biology. The olfactory system is no exception. Its several fundamental components, if evolution is true, must have evolved several times independently. The level of independent origin which evolutionists must admit to (variously referred to as convergent evolution, parallel evolution, recurrent evolution, cascades of convergence, and so forth depending on the pattern) is staggering and dwarfs the levels of similarities in the olfactory receptor genes. To cast those relatively few similarities as mandates for common descent, while ignoring the volumes of similarities that violate common descent constitutes the mother of all confirmation biases.

Third, the strength of this evolution argument is lack of function, but that renders it fallacious. As lawyers know, if you can’t convict the defendant on the facts, you decry how horrifying the crime is. In this case, the entire argument hinges on the utter uselessness of the broken genes. As Venema explains, they are “damaged,” “defective,” “mess[ed] up,” “wrong,” and “ruin[ed].” Clearly, according to Venema, these genes are useless—that’s why they are called pseudogenes. This is crucial because, for evolutionists, this means they would only arise by chance (what designer would implement useless designs?).

All of this means that evolutionists have a very simple formulation: Either those crippling mutations arose once in a common ancestor, or they just happened to arise by chance, coincidentally, multiple times. Clearly the former is much more likely, and this points to common descent. It is, as Venema concludes, “overwhelmingly supported.” [36]

But this powerful argument comes at a cost. There is no free lunch.

The conclusion that common descent is “overwhelmingly supported” utterly depends on our knowing the pseudogenes are useless. Disutility underwrites the assumption of chance as the only alternative to common descent. And chance as the only alternative is crucial. It is why the argument is so powerful, because the chance hypothesis is so unlikely.

Restricting the problem to a contest between evolution and chance makes evolution the obvious winner, but amidst the celebration we forget the weak link. We forget that the entire edifice resides on our certainty of disutility. This, it turns out, is a very weak link.

The history of evolutionary thought, going back to the Epicureans, is full of predictions of disutility gone wrong. It is, quite literally, a theory of gaps. When gaps in our scientific knowledge leave us with ignorance about function, evolutionists routinely assume there is no function. After all, if the world arose by chance, it should be a claptrap, full of aimless, useless designs, if they could even be called that.

But as those gaps close with the inexorable march of scientific progress, it seems we inevitably learn of function. Evolutionists are consistently claiming disutility at brand new findings, only to be proved wrong, again and again. Look no further than the seemingly endless parade of “We thought it was junk, but now …” stories.

Ultimately, the long history of disutility claims are informed by the theory rather than the evidence. This is a classic example of what philosophers refer to as theory-laden observations.

None of this means there are no truly useless structures in biology. There may well be plenty of them. But it has a terrible history.

Furthermore, regardless of the history, disutility is very difficult to know. As with the proverbial “proving a negative,” proving that a pseudogene, or anything else in biology for that matter, actually is useless, is a very difficult undertaking.

From introns to transposons, initial claims of uselessness have given way to a steady stream of findings of function. And, yes, the olfactory receptor “pseudo” genes are no exception. They are now being called pseudo-pseudogenes because all those evolutionary claims of uselessness are rapidly fading. As one recent paper concluded, “such ‘pseudo-pseudogenes’ could represent a widespread phenomenon.”

This is yet another example in a long history of failed disutility predictions. Clearly, the assumption that we know that olfactory receptor pseudogenes are useless is unfounded. Even the name (pseudogenes) will serve future generations of scientists as a constant reminder of this evolutionary foible. Venema’s powerful argument was demolished before the book was even published.

The story does not end here for even if something like pseudogenes could somehow be proven useless, this would not justify the evolutionary formulation of random chance origin as the only other alternative.

Evolution fails to explain how even a single gene could evolve, let alone the entire olfactory system. In fact the presence of supposedly useless structures, such as pseudogenes, is hardly a plus for evolution. As Elliott Sober has pointed out, there is nothing about this story that provides a positivistic argument for evolution.

The argument, and all its strength, hinges entirely on the refutation of the alternative. This is a proof by the process of elimination. Hence it becomes utterly crucial that the alternatives are carefully and exhaustively considered. In particular, all possible alternatives must be known, understood, evaluated, and disproved.

Do you see a pattern here?

This powerful evolutionary argument not only crucially depends on knowing that the pseudogenes are useless, it also crucially depends on knowing that a simple random chance model is the only alternative to evolution, for their origin.

Not only is this philosophically problematic (how do we know that the random chance model is the only alternative?), historically it has a terrible track record. As Kyle Stanford has shown, the history of science is full of theories that were advocated with this type of contrastive reasoning (by disproving a perceived alternative), only later to fail because the assumed alternative was wrong.

To summarize, this highly influential, popular, argument from similar structures that appear to be useless, lies in ruins. It is a disaster. It fails historically, philosophically, and scientifically. It should never have been used in the first place, for its scientific failure was entirely predictable from both the history and philosophy of science.

New Evolution Book: Echolocation Solved

Just Add Water

We have seen that a new evolution book co-authored by evolutionist Dennis Venema is influenced by the mythical Warfare Thesis (here and here) and makes erroneous arguments that the fossil evidence supports evolution (here). Regarding the Warfare Thesis the book propagates the false history that the basic issue of the seventeenth century Galileo Affair was “the veracity of the new science, and its perceived threat to biblical authority.” As we saw, this is the false, evolutionary rendition of history. The Warfare Thesis is a myth, and the Galileo Affair is perhaps the favorite example for evolutionists. Regarding the fossil evidence (which reveals species appearing abruptly in the strata), the book makes two erroneous arguments: that evolution is needed for science to work at all (the “intellectual necessity” philosophical argument) and the use of random design as the alternative to evolution (a theological argument). Now we move on to another topic: echolocation. This was of particular interest to me since I have used echolocation as an example of how evolution fails, and fails badly. When I saw that Venema appealed to echolocation to argue for evolution I was interested to see what he had to say. I am always looking for good arguments for evolution, but I did not find one here. Below I summarize the five different reasons why echolocation destroys evolution. Finally, I turn to Venema’s argument, if it can be called that. What we will see is that his argument utterly fails. Venema fails to address any of the problems with echolocation, and he fails to present any kind of a positive case that might be used to overcome the many problems. In short, it is a complete disaster.

Complexity: The original sonar technology

Most people are familiar with the concept of radar and sonar. Simply put, a reflected signal is used to track a target. But what most people are less familiar with are the many details and complications any radar or sonar system must reckon with. For example, the transmitted pulse must be very strong because it will weaken as the square of the distance it travels, and only a tiny fraction of it will be reflected. Ultimately, the return signal is very weak, so while the receiver is exposed to the very powerful transmitted signal, it must then detect a return signal many orders of magnitude weaker. Think of shouting as loud as you can, and then listening for the echo off of a mosquito.

This is just the beginning of the many sonar design issues. The pulse rate, duration, intensity, pitch are all design parameters that influence how small a target can be detected, how far away it can be detected, how accurately it can be tracked and resolved, and so forth. An advanced sonar design can vary these parameters to optimize the tracking.

Sonar design must also consider how to compensate for target motion and the resulting Doppler effect, erroneous reflections from clutter in the environment, and how to guide toward a moving target. There is also the possibility of imaging to determine what type of target it is.

Not surprisingly, there are many different sonar design strategies. Depending on the clutter environment, typical types of targets, and so forth, various design strategies might work better.

All of this is what we find in nature’s echolocation designs. Whales and bats have incredibly efficient and accurate tracking capabilities. We have developed sonar, but nature had it all along—the original sonar technology. In fact nature’s designs are better than our military equipment. Which is one reason why they are studied so closely.

Complexity at the molecular level

We have seen how complicated echolocation can be. Not surprisingly the molecular machines that help to make it happen are also highly complex. Prestin, a protein important in mammalian hearing, is a transmembrane protein in the outer hair cells of the cochlea. It serves as a frequency-selective amplifier in a sound system that works something like this.

As sound enters the ear, it deflects the outer hair causing tiny amounts of stretching or compression in the outer hair cells. There are channel proteins that sit in the membrane of these cells which are sensitive to such mechanical strain. These proteins provide a tunnel (or channel) across the membrane so that ions can easily cross, and the mechanical strain can cause the channels to open.

These channels are precisely designed to allow only certain types of ions to cross. For example, some channels allow the positively charged potassium ion to cross but not the positively charged sodium ion, and vice-versa.

When a channel opens, ions usually tend to cross through the membrane (either into the cell or out of the cell) because the ion concentration is not uniform, and because there is a voltage, across the membrane. Such differences in concentrations across the membrane, and the voltage, are actively maintained by the cell. They serve as a sort of battery whose energy can be tapped at any time by opening membrane channels.

When the incoming sound causes certain channels to open, the ions that cross cause a change in the membrane voltage. In the outer hair cells, this voltage change encourages negatively charged chlorine ions to exit the cell. They interact with the prestin protein, in the membrane, to cause a mechanical deformation resulting in the elongation of the cell.

In other words, the incoming sound, that caused the hair to move, ends up causing yet more hair movement, and this serves precisely to amplify the incoming sound. This amplification is greater at low sound levels, as it should be.

One of the interesting features of this system is the speed at which it operates. Obviously in order to amplify sound you need to respond as fast as the changes in sound occur. Protein motors often use chemical energy (such as the splitting of the ATP molecule) but that would be too slow for the ear's sound system. Instead, prestin uses the membrane's voltage. This electrical energy can be used much faster and prestin operates at microsecond rates. Here is how one paper summarized the system:

The exquisitely high sensitivity and frequency selectivity of the mammalian hearing organ originates from a mechanical amplification mechanism that resides in the organ of Corti, the sense organ of hearing in mammals. The gain provided by this amplification can reach as high as a thousandfold; it is highest at low sound levels and progressively diminishes with increasing sound energy.

Evolution has no explanation for the origin of this system beyond unfounded speculation, and this is only the beginning of the many molecular machines behind the echolocation systems found in nature.

Echolocation designs incongruent with evolutionary tree

It does not appear that random mutations are the cause of systems such as echolocation in bats and whales. Although this is an enormous problem for evolutionary theory, it is not the only one. As discussed above, there are many different types of echolocation designs. Evolution would predict that species that are thought to be close neighbors on the evolutionary tree would share similar echolocation designs. In other words, the echolocation designs should be congruent with the evolutionary tree. But they are not.

Whereas Darwin argued that the evolutionary tree explained nature’s designs rather than habitat, nature’s echolocation designs follow the exact opposite rule. Here is how one paper described it:

the animal’s habitat is often more important in shaping its call design than is its evolutionary history.

This is an enormous falsification of a key prediction of evolutionary theory.

Convergence at the morphological level

One consequence of this falsification is that evolutionists must construct highly complicated narratives for the origin of echolocation. For example, if evolution is true, then we must believe that the incredible echolocation ability found in some bats arose multiple times, by evolving independently. That’s not easy for evolutionists to explain. How could such uncanny design details repeat themselves via blind biological variation (no, natural selection doesn’t help)?

But this convergence problem goes far beyond the bats. Whales and bats share some uncanny similarities in how they track their prey. But if evolution is true, we would have to believe that their common ancestor had none of these capabilities. So in completely different parts of the world, in completely different environments, random mutations in these different species must have independently constructed the same ultra complex designs. As one report explained:

Though they evolved separately over millions of years in different worlds of darkness, bats and toothed whales use surprisingly similar acoustic behavior to locate, track, and capture prey using echolocation, the biological equivalent of sonar. Now a team of Danish researchers has shown that the acoustic behavior of these two types of animals while hunting is eerily similar.

If evolution is true then bats and whales would have been evolving independently for millions of years. And yet they both constructed a sonar capability which involves transmitting loud signals while receiving incredibly weak signals, adjusting the signal parameters in real time, processing the received signals, and so forth. They even share the same range of ultrasonic frequencies:

Bats and toothed whales (which include dolphins and porpoises) had many opportunities to evolve echolocation techniques that differ from each other, since their nearest common ancestor was incapable of echolocation. Nevertheless – as scientists have known for years – bats and toothed whales rely on the same range of ultrasonic frequencies, between 15 to 200 kilohertz, to hunt their prey.

And that similarity is in spite of the different environments:

This overlap in frequencies is surprising because sound travels about five times faster in water than in air, giving toothed whales an order of magnitude more time than bats to make a choice about whether to intercept a potential meal.

But that is not all. The bat and whale also use similar strategies for adjusting their signals while homing in on prey:

Bats increase the number of calls per second (what researchers call a “buzz rate”) while in pursuit of prey. Whales were thought to maintain a steady rate of calls or clicks no matter how far they were from a target. But the new research shows that wild whales also increase their rate of calls or clicks during a kill – and that whales’ buzz rates are nearly identical to that of bats, at about 500 calls or clicks per second.

It is another example of a complex design evolution can only speculate about, and once again the evolutionary tree fails to predict its pattern.

Convergence at the molecular level

Not only is incredible echolocation convergence evident at the morphological level, it is also seen at the molecular level. For instance, the prestin proteins in certain bat and whale species are more similar than evolution would expect. The massive prestin protein has too many amino acids that match up between these species. If one were to construct an evolutionary tree on the basis of prestin comparisons alone, then the bat and whale would be grouped together, and that cannot be correct.

This fact alone need not be a problem for evolutionists. They simply say that prestin is under the influence of strong selection. In other words, there are strong functional constraints on prestin that require more similarity, even between distant species, than we typically find in proteins.

In particular, researchers identified nine amino acids in prestin that seem to be responsible for the overly-consistent whale-bat matchup. Those nine amino acids must be under very strong selection. If one of them mutated then the biosonar system would not work well. The bat or whale would not survive, and that is why we don’t observe such changes. That is how natural selection works.

But if all nine amino acids are required, how did evolution stumble onto the design in the first place? It would be highly unlikely for the right nine amino acids to arise via blind mutations, at the same time.

But the convergence of molecular machines behind echolocation goes far beyond prestin. As one paper explains, “convergence is not a rare process restricted to several loci but is instead widespread”.

As one evolutionist admitted, “These results imply that convergent molecular evolution is much more widespread than previously recognized”. And another admitted that the results are astonishing:

We had expected to find identical changes in maybe a dozen or so genes but to see nearly 200 is incredible. We know natural selection is a potent driver of gene sequence evolution, but identifying so many examples where it produces nearly identical results in the genetic sequences of totally unrelated animals is astonishing.

Astonishing.

Venema’s argument for why echolocation is not a problem

This brings us to Venema’s argument for why echolocation is not a problem. Given the enormous problems briefly reviewed above, how exactly does Venema find echolocation to be evolution-friendly? We have looked at the problem of complexity of echolocation, including at the molecular level, the problem that echolocation designs are incongruent with the evolutionary tree and, as an example, the problem of convergence at both the morphological and molecular levels. Surely no objective scientist would find evidence for evolution in nature’s echolocation designs.

Would they?

Believe it or not, here is what Venema writes:

If you’ve ever stumbled through a pitch-black room and pulled yourself up short just before colliding with a wall or other object, you have employed your (very rudimentary) sense of echolocation. What you detected (though you might not have even consciously perceived it) was that sound waves were reflecting off the object in your way. All mammals can do this, but most (like us) do it very poorly. We need to be very close to the object in question before it is even possible for us to notice reflected sound, and more likely than not we won’t, and we’ll stub our toe or worse.

As it turns out, cetacean echolocation is a specifically tuned sense of hearing that is based on the same genes used for hearing in other mammals. One key gene used for hearing in all mammals is called the “prestin” gene, a protein involved with the specialized structures in the mammalian ear that vibrate in response to sound waves. In whales, the prestin gene is tuned to the ultrasonic frequencies that are better suited to echolocation. This tuning required only a few amino acid changes within the protein—an amount of change easily within the reach of the sort of molecular tinkering we saw for the insulin gene in various mammals. This tinkering within the prestin gene to tune it for echolocation was so easy to achieve, it would seem, that nearly identical changes occurred independently in the lineage leading to modern bats, who also use a prestin tuned to ultrasonic frequencies for echolocation. So even echolocation is not “new”—it too is remodeled from a standard mammalian sense of hearing.

This is a complete disaster. Venema’s equating of echolocation with his imagined ability to avoid a wall in a dark room, his transforming convergence to a virtue, his casting of echolocation as “easy to achieve” and the result of mere “tinkering,” and nothing new but rather simply a remodel of “standard mammalian sense of hearing,” is all standard evolutionary pretzel logic.

This is the evolutionary “just add water” view of biology where you add a couple of mutations and, poof, you have echolocation. But as we saw above, echolocation is not at all comparable to “standard mammalian” hearing. It doesn’t fit the evolutionary tree, and the convergence is astonishing and utterly unexpected and unexplained.

Venema’s attempt to explain away echolocation as a standard result of evolution is not even wrong.

When I saw that this new book had a section on echolocation I was keen to read it over. I have followed the echolocation research for years. I write about it, and often include it in presentations. I discuss the various ways the echolocation evidence contradicts evolution. So why would there be a section on this subject in this book promoting evolution? Have I missed something? Is there some fundamental aspect of echolocation I have missed? Is there a new paper I have missed, overturning the large body of research?

But as I read the section, I quickly realized it was nothing more than the usual evolutionary just-so story. A wholesale ignoring of well-established science, an embracing of imagined thought experiments that make no sense, and an utterly unscientific conclusion.

It isn’t even wrong.