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.

IC: We Can Say It, But You Can’t

Pre Adaptation

In contrast [to trait loss], the gain of genetically complex traits appears harder, in that it requires the deployment of multiple gene products in a coordinated spatial and temporal manner. Obviously, this is unlikely to happen in a single step, because it requires potentially numerous changes at multiple loci.

If you guessed this was written by an Intelligent Design advocate, such as Michael Behe describing irreducibly complex structures, you were wrong. It was evolutionist Sean Carroll and co-workers in a 2007 PNAS paper.

When a design person says it, it is heresy. When an evolutionist says it, it is the stuff of good solid scientific research.

The difference is the design person assumes a realist view (the genetically complex trait evinces design) whereas the evolutionist assumes an anti-realist view (in spite of all indications, the genetically complex trait must have arisen by blind causes).

To support their position, evolutionists often appeal to a pre adaptation argument. This argument claims that the various sub components (gene products, etc.), needed for the genetically complex trait, were each needed for some other function. Therefore, they evolved individually and independently, only later to serendipitously fit together perfectly and, in so doing, form a new structure with a new function that just happened to be needed. As Richard Dawkins once put it:

The bombardier beetle’s ancestors simply pressed into different service chemicals that already happened to be lying around. That’s often how evolution works.

The problem, of course, is that this is not realistic. To think that each and every one of the seemingly unending, thousands and thousands, of genetically complex traits just happened to luckily arise from parts that just happened to be lying around, is to make one’s theory dependent on too much serendipity.

Religion drives science, and it matters.

Kneecaps: “Ultimately, there might not be a simple pattern”

We can only say that so it is

Why do ostriches have four, rather than two, kneecaps? A new study has found several possible biomechanical advantages. Perhaps they allow the ostrich to straighten its leg more quickly, helping the animal to run quickly. Perhaps the lower kneecap protects the joined tendons crossing the front of the knee. One reason that does not help to explain the ostriches four kneecaps is evolution. That is because this unique design is not predicted, and makes no sense, on the theory. As one article admits: “Bizarrely, many of the ostrich’s closest relatives don’t have kneecaps at all.” Similarities across the species were a strong argument for evolution, but in fact biology is full of unique designs, particular to one or a few species. Such one-off, “lineage specific,” designs are “bizarre” for evolutionists. So while there are design reasons for the ostriches four kneecaps, on the ordinary view of the evolution of each being, we can only say that so it is.

Subcellular Map of the Human Proteome Reveals “Highly Complex Architecture”

A High Degree of Regulation and Control

New research is using antibodies to map out the spatio-temporal locations of 12,003 different proteins in human cells. The results are another example of how, as Bruce Alberts put it in 1998: “We have always underestimated cells.” Alberts explained how cells were once naively viewed as something of a random affair, where molecules “were thought to diffuse freely, randomly colliding.” The new research reveals the “the highly complex architecture of the human cell” and adds more detail to the fact that the workings of the cell are far from random:

A total of 12,003 proteins targeted by 13,993 antibodies were classified into one or several of 30 cellular compartments and substructures, altogether defining the proteomes of 13 major organelles.

Although evolutionists “thought the cell was so simple,” this research is showing that the “cellular proteome is compartmentalized and spatiotemporally regulated to a high degree.” In fact “[m]ore than half of these 12,003 proteins localize in more than one compartment at the same time.” This is consistent with the fact that most proteins are capable of performing multiple functions, and is another indicator of high complexity:

Moreover, proteins that localize to more than one compartment may have context-specific functions, increasing the functionality of the proteome. The fact that proteins “moonlight” in different parts of the cell is now well accepted. … The more complex a system is, the greater the number of parts that must be sustained in their proper place, and the lesser the tolerance for errors; therefore, a high degree of regulation and control is required.

Indeed, the degree of regulation and control required for this system is not only enormous, but contrary to evolutionary expectations.

Dennis Venema, Galileo, and Protein-Protein Binding

Don’t Count on the Duchess

We have looked at Dennis Venema’s articles on evidences for common descent here, here, here, here, here, and here. In a recent discussion with Venema, he made the erroneous claim that the mammalian immune system, with its search for, and production of, antibodies, is a good example of why evolving protein-protein binding sequences is not a problem. In fact the mammalian immune system is yet another enormous problem for the theory of evolution. Furthermore, the mammalian immune system is not a good example because it is designed for this job of creating protein-protein binding sequences. It searches a well-defined design space extremely rapidly, and measures the success of its search experiments accurately and quickly. The fact that our immune system successfully designs antibodies in short order does nothing to address the problem of how random mutations occurring throughout the genome is supposed to have found myriad binding sequences, crucial for life. Venema also referred to another example which he has written about. Unfortunately this example also fails to demonstrate Venema’s claim of “evolution producing a new protein-protein binding event.”

The problem with evolving protein-protein binding is that too much gene sequence complexity is required to achieve the needed binding affinity. You could say it is an “all-or-none” type of problem.

One or two mutations will not generally do the job—you usually need more mutations before the two proteins stick together very well. And stick together they must, on a massive scale, in order to perform their necessary tasks. Even the simplest, unicellular, organisms contain massive protein machines, consisting of dozens of different proteins binding together to perform crucial life functions.

The study Venema referred to did a beautiful job in confirming this “all-or-none” character of protein-protein binding sequences. The study showed that in order for a viral protein to perform a relatively simple switch from one protein to a very similar protein required four types of mutations.

Anything less and no dice.

The twist in this study was that subsets of the four mutation types were apparently useful for a different function (strengthening the binding affinity to the original protein). So while in general the evolution of protein-protein binding sequences is astronomically difficult because too many simultaneous mutations are required, in this case the four mutation types could be accumulated, with useful benefit realized at some of the intermediate steps.

This is not a general result. It is not a revolutionary new finding that reverses our understanding of protein-protein binding sequences.

It confirms our knowledge, and adds a fascinating outlier case where the “all-or-none” character is circumvented by intermediate functions which, fortuitously “push” the design in the right direction. As the study explains:

The “all-or-none” epistasis among the four canonical phage mutations implies that it would have been unlikely for the new function to evolve on the scale of our experiments, except for the lucky fact that some of the mutations were beneficial to the phage in performing their current function, thereby pushing evolution toward the new function.

The study provides no indication that the untold thousands upon thousands of protein-protein binding problems in molecular biology would enjoy this type of setup. And if they did, oh what a most suspicious sign of design that would be.

Venema is mistaken in his failed attempt to recruit this study as a solution to the evolution of protein-protein binding sequences.

Strangely enough not only had Michael Behe provided his explanation of this study, but Venema was aware of it at the time of his writing. Venema explained that in his next article he would address Behe’s explanation, but in fact Venema simply rehashed Behe’s original explanation for why protein-protein binding is a problem for evolution.

Venema did not address Behe’s explanation but simply concluded that Behe’s original explanation must be false because, after all, this new study demonstrates the evolution of just such protein-protein binding sequences.

This is an unfortunate misrepresentation of a study that most readers will not understand. Venema completely misappropriated the study, and force-fit it into an evolutionary proof.

Additional problems

In addition to this basic problem of serendipity, this confirmation of the “all-or-none” character of protein-protein binding sequences was possible only with a very contrived, designed, laboratory experiment.

Simply put, a virus population was provided with a willing, and well fed host to live off of. In the meantime, many more host targets awaited the virus population. So a few mutations helped the virus’ infect the initial hosts, and mere single additional mutation then allowed the virus’ to infect the second group of hosts.

It was an entirely artificial, laboratory, environment, that wasn’t even intended to replicate a realistic evolutionary environment. Venema nowhere explained this.

Second, the study also discovered even more serendipity. Not only were there “luckily” intermediate fitness benefits, but the finding of the four mutations types also required certain mutations in the host genome.

Without them, no dice.

[Ed: Final section on the absence of synonymous substitutions removed, given the short timeline of the experiment.]

The DNA Code and Evolution

My Dear Watson

The DNA code is used in cells to translate a sequence of nucleotides into a sequence of amino acids, which then make up a protein. In the past fifty years we have learned four important things about the code:

1. The DNA code is universal. There are minor variations scattered about, but the same canonical code is found across the species.

2. The DNA code is special. The DNA is not just some random, off the shelf, code. It has unique properties, for example that make the translation process more robust to mutations. The code has been called “one in a million,” but it probably is even more special than that. For instance, one study found that the code optimizes “a combination of several different functions simultaneously.”

3. Some of the special properties of the DNA code only rarely confer benefit. Many of the code’s special properties deal with rare mutation events. If such properties could arise via random mutation in an individual organism, their benefit would not be common.

4. The DNA code’s fitness landscape has dependencies on the DNA coding sequences and so favors stasis. Changes in the DNA code may well wreak havoc as the DNA coding sequences are suddenly not interpreted correctly. So the fitness landscape, at any given location in the code design space, is not only rugged but often is a local minimum, thus freezing evolution at that code.

Observation #1 above, according to evolutionary theory, means that the code is the ultimate homology and must have been present in the last universal common ancestor (LUCA). There was essentially zero evolution of the code allowed over the course of billions of years.

This code stasis can be understood, from an evolutionary perspective, using Observation #4. Given the many dependencies on the DNA coding sequences, the code can be understood to be at a local minimum and so impossible to evolve.

Hence Francis Crick’s characterization, and subsequent promotion by later evolutionists, of the code as a “frozen accident.” Somehow the code arose, but was then strongly maintained and unevolvable.

But then there is Observation #2.

The code has been found not to be mundane, but special. This falsified the “frozen accident” characterization, as the code is clearly not an accident. It also caused a monumental problem. While evolutionists could understand Observation #1, the universality of the code, as a consequence of the code being at a fitness local minimum, Observation #2 tells us that the code would not have just luckily been constructed at its present design.

If evolution somehow created a code to begin with, it would be at some random starting point. Evolution would have no a priori knowledge of the fitness landscape. There is a large number of possible codes, so it would be incredibly lucky for evolution’s starting point to be anywhere near the special, canonical code we observe today. There would be an enormous evolutionary distance to travel between an initial random starting point, and the code we observe.

And yet there is not even so much as a trace of such a monumental evolutionary process. This would be an incredible convergence. In biology, when we see convergence, we usually also see variety. The mammalian and cephalopod eyes are considered to be convergent, but they also have fundamental differences. And in other species, there are all kinds of different vision systems. The idea that the universal DNA code is the result of convergence would be very suspect. Why are there no other canonical codes found? Why are there not more variants of the code? To have that much evolutionary distance covered, and converge with that level of precision would very strange.

And of course, in addition to this strange absence of any evidence of such a monumental evolutionary process, there is the problem described above with evolving the code to begin with. The code’s fitness landscape is rugged and loaded with many local minima. Making much progress at all in evolving the code would be difficult.

But then there is Observation #3.

Not only do we not see traces of the required monumental process of evolving the code across a great distance, and not only would this process be almost immediately halted by the many local minima in the fitness landscape, but what fitness improvements could actually be realized would not likely be selected for because said improvements rarely actually confer there benefit.

While these problems obviously are daunting, we have so far taken yet another tremendous problem for granted: the creation of the initial code, as a starting point.

We have discussed above the many problems with evolving today’s canonical code from some starting point, all the while allowing for such a starting point simply to magically appear. But that, alone, is a big problem for evolution. The evolution of any code, even a simple code, from no code, is a tremendous problem.

Finally, a possible explanation for these several and significant problems to the evolution of the DNA code is the hypothesis that the code did not actually evolve so much as construct. Just as the right sequence of amino acids will inevitably fold into a functional protein, so too perhaps the DNA code simply is the consequence of biochemical interactions and reactions. In this sense the code would not evolve from random mutations, but rather would be inevitable. In that case, there would be no lengthy evolutionary pathway to traverse.

Now I don’t want to give the impression that this hypothesis is mature or fleshed out. It is extremely speculative.

But there is another, more significant, problem with this hypothesis: It is not evolution.

If true this hypothesis would confirm design. In other words, a chemically determined pathway, which as such is written into the very fabric of matter and nature’s laws, would not only be profound but teleological. The DNA code would be built into biochemistry.

And given Observation #2, it is a very special, unique, detailed, code that would be built into biochemistry. It would not merely be a mundane code that happened to be enabled or determined by biochemistry, but essentially an optimized code.

Long live Aristotle.

The problem is there simply is no free lunch. Evolutionists can try to avoid the science, but there it is.

Jim Stump: “I almost felt sorry for design advocates”

F6 Thinking

In his recent review of Benjamin Jantzen’s Introduction to Design Arguments (Cambridge University Press, 2014), evolutionist Jim Stump finds much to agree with because, as Stump argues, design arguments are both bad science and bad religion. For example, Michael Behe argues that evolution is challenged by the irreducible complexity of biological structures, but “almost all” biologists think Behe’s examples don’t hold water. The problem is Behe is implicitly appealing to a caricature of how evolution works that views complexity arising all at once. “In reality,” the ex Bethel professor explains, “natural selection operates on combinations of traits, not merely on isolated structures. Half-developed wings won’t help an insect fly, but they might help it do other things that contribute to its survival, like skim across the surface of water. Contrary to the ID claim about irreducible complexity, you don’t have to get the whole thing at once.”

Furthermore, even if Behe is right, he can merely conclude that design is the best explanation available. The history of science is full of best explanations that were later rejected because a previously unconceived explanation arose. Therefore Behe’s claim is considerably weakened. Stump finds Jantzen’s analyses to be cogent and by the end “almost felt sorry for design advocates as the soft underbelly of their arguments was exposed.”

Unfortunately what the philosopher demonstrates here is not a helpful and insightful commentary on design arguments but rather the usual sequence of evolutionary misrepresentations.

It begins with Stump's appeal to authority. This is a common evolutionary argument, but the fact that a majority of scientists accept an idea means very little. Certainly expert opinion is an important factor and needs to be considered, but the reasons for that consensus also need to be understood. The history of science is full of examples of new ideas that accurately described and explained natural phenomena, yet were summarily rejected by experts. Scientists are people with a range of nonscientific, as well as scientific influences. Social, career, and funding influences are easy to underestimate. There can be tremendous pressures on a scientist that have little to do with the evidence at hand. This certainly is true in evolutionary circles, where the pressure to conform is intense.

Next, Behe does not appeal to a caricature of how evolution works as Stump describes. In his development of the problem of irreducible complexity, Behe specifically addresses the adaptation of pre existing structures. Indeed, Stump’s representation of ID as claiming that with evolution you must “get the whole thing at once” is itself a caricature.

Furthermore Stump’s view that “natural selection operates on combinations of traits” is nothing more than the usual Aristotelianism dressed up in Darwinian language. Natural selection doesn’t “operate” on anything. And Stump’s credulous explanation of how “Half-developed wings won’t help an insect fly, but they might help it do other things that contribute to its survival, like skim across the surface of water” is simply a just-so story. There is no scientific evidence that this ever actually occurred in history, and it adds enormous serendipity to evolutionary theory. Does that make it impossible? Of course not. But that’s not the point.

The final critique of Behe is that he can only present design as the best explanation and is therefore vulnerable to the problem of unconceived explanations. Is not Behe’s claim considerably weakened?

This coming from an evolutionist is hypocritical for contrastive thinking is foundational to evolutionary thought. If Behe’s claim is considerably weakened then evolution is demolished.

Stump concludes with the usual Leibnizian / Kantian appeal to naturalism. Reminiscent of the final scene in Inherit the Wind which has the victorious Spencer Tracy clutching a Bible, we are told that the divine hand is evident in the created order, not in the failures of nature:

We see God’s hand throughout the created order not because science can’t explain nature, but because it can. The Designer’s mark is not in systems that don’t work quite right and need tinkering; those are signs of imperfection.

If naturalism fails, then nature fails. And if nature fails, then the Creator has failed. It’s the seventeenth century all over again.

NIH Director: Each Neuron is Different

“Men Loved Darkness Rather Than Light”

In his blog post this week on the neuroscience research of Columbia’s Sean Escola, NIH Director Francis Collins makes the obvious, yet too often overlooked point that each of the hundred billion or so neurons in the human brain is different. In our profound ignorance it is easy to view the brain like a pile of pudding, achieving its fantastic abilities through a lucky mixture of the right chemicals. But of course, nothing could be farther from the truth and Collins’ observations helps to disabuse us of such folly. If you have ever wired up a machine you will understand. It is not just a pile of wires that somehow happen to get it right. Each wire has its own, unique function, attaching to two specific connectors. Things are astronomically more complicated in the brain, as its “wires” are not merely a conduit of electrical charge but an incredibly complex cell called a neuron. And each neuron does not merely attach to two distant connectors, but rather to hundreds or thousands of connectors. And each connection is nothing like a simple soldering attachment. In the brain they are called synapses and with thousands of molecular-scale switches researchers compare them to microprocessors.

But on top of all that, each neuron is different. A hundred billion different, unique neurons, each having a different, unique function. Each forming a different, unique set of synapses. We have not even begun to understand all of this neural circuitry, let alone how to design or build anything like it. And yet we insist it all must have arisen spontaneously, as a result of random mutations. That is not science, that is absurdity.

h/t: Paul Asay

Study: The Human Brain Has an Almost Ideal Network of Connections

The Brain Was Evolutionarily Designed?

Perhaps the most unlikely part, of all the many unlikely parts, of evolutionary theory is the evolution of the brain, with all that that entails, including 200 billion nerve cells, one quadrillion synapses, and the thousand or more molecular-scale switches in each synapse. Not surprisingly researchers sometimes can hardly find the words to express what they are studying. The brain is “truly awesome” beyond anything they’d imagined, almost to the point of being beyond belief. (You can read more here, here, here, here, here, here, here, here, here, here, here, here, and here).

And so far we’re only talking about the brain’s physical wonders. On top of all that there is consciousness, will and all those feelings and emotions we have. There is, not surprisingly, no evolutionary explanation for how the brain evolved. And a new study on how information is transferred within the brain now adds yet another intriguing aspect to the problem.

The researchers used a network analysis approach, and considered the tradeoff between the number of connections made and the number of information routing pathways connecting disparate locations. In this simple model, the transfer of information is optimized by minimizing the number of connections while maximizing the direct routings.

Of course the human brain undoubtedly has many more functions and requirements to fulfill, but interestingly their data showed a striking fit. According to their findings the structure of the human brain has an almost ideal network of connections. As the lead researcher explained, “That means the brain was evolutionarily designed to be very, very close to what our algorithm shows.” As usual, the infinitive form reveals the underlying teleological thinking. Aristotle is dead, long live Aristotle.

But that is the least of evolution’s problems. What is striking, and a dead giveaway, is the high confidence of evolutionists. There is no question that evolutionary theory has its challenges. This study of the brain’s information transfer is yet another example of this. The researchers of this study, in spite of statements about evolution, have no scientific theory for how the brain could have evolved. Nothing.

This paper provides yet another example that it is not exactly obvious that the world arose spontaneously (and that is putting it gently). In fact, science tells us the exact opposite. And yet evolutionists insist that evolution is a fact—no question about it. It would be, evolutionists like to say, perverse to say anything less. Those over-the-top claims by evolutionists tell all. This isn’t about science.

About That Squid and its RNA editing …

Molecules Gone Wild

We recently reported that the common squid, Doryteuthis pealeiirecodes, uses massive, tissue-specific, RNA editing to modify many of its proteins. One evolutionary explanation for this apparent intelligent design would be that the editing machinery is merely an uncontrolled, random process. This would be in keeping with evolution’s view of life as a train off the tracks. Indeed, many past findings were initially described as vestigial or junk, until the design could no longer be denied. One current example is the finding that most of the human genome is transcribed. Apparently it is functional, and so isn’t mostly junk. But one evolutionary explanation that continues to have currency is that the transcription machinery is uncontrolled and has gone wild.

Well this “Molecules Gone Wild” explanation won’t work this time, for several reasons. First, the RNA editing is not only tissue specific, but also is protein specific. Certain proteins, such as those working on the squid nervous system, undergo much more editing than the other proteins. This doesn’t look like a random, uncontrolled, editor.

Furthermore, the RNA edits cause changes to the resulting protein amino acid sequence at a rate far higher than would be expected from random edits. And, just as striking, that non random rate is highly correlated with the editing level of the RNA site. In a given RNA transcript, some sites are found to be edited more often than others. The level of editing can range from a percent up close to 100%. And as that editing level increases, the rate at which the edits deviate from the randomly expected rate of changing the resulting protein amino acid sequence increases as well. The correlation is striking, as the graph below shows.

The horizontal red line shows the rate of amino acid sequence change that would result from random RNA edits. As you can see, for those sites that are more consistently edited, the rate increasingly rises above that red line. Something very non random is going on there, and evolution has no explanation for it aside from, “Wow, an incredibly complex design happened to arise by chance, and then it was maintained by natural selection.” That’s not science.

Amino Acid Frequency Correlates With the Number of Codons

An Optimal Code

One of the powerful evidences for evolution is the DNA code, at least according to evolutionists. The DNA code is essentially the same across all of life and, evolutionists explain, there is no reason for such universality. The code is ubiquitous across all life, but it is not special or unique. It is a mundane code, like any other, which just happened to evolve early in evolutionary history. But once in place it could not evolve, so it has been preserved ever since. In other words, the DNA code is the result of contingency, not law. As usual the evolutionary reasoning makes no sense. There is no credible, scientific, explanation for how a code could arise spontaneously in some primitive cell. And if it could not evolve once it was in place, then how could it evolve in the first place? Beyond all this, it certainly is not just another code. For instance, consider Morse code shown below:

The Morse code encodes letters and numbers using short and long signals called “dots” and “dashes.” It was used with telegraph systems in the nineteenth century. The letter codes are shorter for those letters that are used more frequently, such as A, E, I, N and T. This serves to minimize the length of the transmitted message and maximize the information conveyed by the telegraph.

Similarly the DNA code is an optimized code. Unlike the Morse code which is a variable word length code, the DNA code uses a constant word length. Each word consists of three chemical “letters” and the code has four different letters in all. This means there are 4^3 or 64 different words that are possible in this code. Each word codes for an amino acid, but only 20 different amino acids are coded for.

So an amino acid can have more than one code word assigned to it. One way that the DNA code is optimized is by assigning more code words to those amino acids that appear more frequently. This serves to maximize the additional information that can be overlaid on the genetic message.

For instance, if you need to code for an alanine amino acid, then you have four different code words available to you. This choice might encode for some other type of information, such as an overlapping gene. Many DNA segments code for more than one gene, for instance, by reading backwards. Not very mundane. Below is a chart of the DNA code (Lewin, Genes VII). On the right is a graph showing the number of code words for each amino acid plotted against the typical amino acid frequency. You can see that the higher frequency amino acids have more code words assigned to them.

Like the Morse code, the DNA code is optimized to maximize the information conveyed. When evolutionists say the DNA code is powerful evidence for evolution they are manipulating science to support their preconceived truth.

Paper: Water Molecule Harnesses its Electronic Structure to Encode Features

Aristotelianism on Steroids

It’s no secret that the biological world contains all manner of complicated and finely-tuned machines and mechanisms. Even evolutionists admit that life has the appearance of design. But it doesn’t stop there. Biology, for instance, rests on a foundation of chemistry, and there too we find all kinds of fascinations. At the atomic level, matter and its interactions have specific and particular properties that result in a vast set of crucial puzzle pieces. There are the positive and negative ions, such as sodium and chlorine, which result in molecules with ionic bonds, such as salt. There are atoms that can accept or donate electrons, such as carbon, which result in life’s macromolecules, such as carbohydrates and fats. Even quantum mechanics, which may seem like a distant underworld, can be important in biological processes. The fundamental nature of matter and its interactions appear to be ingenious building blocks on which chemistry and biology rely. A good example of this is water, which continues to yield interesting secrets about how nature works.

Every biology student learns that water has a wide range of particular properties that are crucial for life. It expands, rather than contracts, when it freezes leading to ponds merely freezing at the top rather than all the way through, in the winter; it is the universal solvent; and it absorbs heat without increasing much in temperature. Here is how a new research paper summarizes water:

Water is one of the most common substances yet it exhibits anomalous properties important for sustaining life. It has been an enduring challenge to understand how a molecule of such apparent simplicity can encode for complex and unusual behavior across a wide range of pressures and temperatures. … Water challenges our fundamental understanding of emergent materials properties from a molecular perspective. It exhibits a uniquely rich phenomenology including dramatic variations in behavior over the wide temperature range of the liquid into water’s crystalline phases and amorphous states.

The paper finds that water’s many properties can be explained with an N-body model with electrostatic forces. And as is so common, the authors use teleological language to describe the phenomena. Watch for the infinitive form:

We show that many-body responses arising from water’s electronic structure are essential mechanisms harnessed by the molecule to encode for the distinguishing features of its condensed states.

So the water molecule harnesses its electronic structure to encode its distinguishing features. Such Aristotelian language and thought are ubiquitous in the natural sciences. This suggests that it is not easy or natural for practitioners to study the natural world strictly from a materialistic perspective. The world didn’t “just happen.”

It’s Just Getting Worse: Our Retina Structure is “optimized for our vision purposes”

Theory of the Gaps

Research out of Israel continues to hammer away at the once powerful proof text for evolution, that our retina is one big kludge given that the photocells were obviously installed backwards. Not only that, but to add insult to injury, the resulting neuron wire bundle had to go somewhere, and the result was a blind spot in our retina. Such a kludge could only be ascribed to the blind process of evolution. The problem with such arguments, aside being nonscientific, is that they are vulnerable to the inexorable march of scientific progress. The act has played out repeatedly: When we first observe a design we don’t understand it and conclude it must be mostly nonsense and another confirmation of evolution. Then, years later, science discovers a nifty function for the design.

So it is with our retina and its “backward” photocells. They were celebrated as an example of nature’s “errors and bungles” and yet another vindication of the Epicurean call for a designer-less world.

But that was then and this is now. It turns out those backward photocells, along with the retina’s Müller cells, work to focus the green-red part of the light spectrum onto the cone photoreceptors and pass the shorter-wavelength blue-purple light through to the rod photoreceptors. As Professor Erez Ribak put it, those backward photocells and the overall retina “optical structure is optimized for our vision purposes.”

This is another example of the danger of constructing theories on the gaps in our knowledge.

This Piece by Lawrence Krauss is a Damning Indictment

Follow the Evidence

When Eric Metaxas wrote a Christmas Day piece in the Wall Street Journal about how science is lending support to modern-day arguments for design in the universe, he drew fire from all quarters. Apparently Metaxas arguments were full of fallacies. Of particular interest was a criticism by leading physicist, Lawrence Krauss. Surely a top scientist would leave no doubt about Metaxas’ flubs.

Krauss’ first point was that Metaxas is a “religious writer with an agenda,” and so his arguments cannot be trusted. But religious people in general, and Christians in particular, are all over the map when it comes to design. Indeed, many of Metaxas’ critics are Christian theologians.

Clearly Metaxas, qua “religious writer” or Christian, is not bound to an agenda regarding design. Why then does Krauss sense an agenda at work? Perhaps because, in fact, it is Krauss who is the one with the agenda. You see Krauss is an atheist, and when it comes to design, atheists are most definitely not all over the map. If you are an atheist, then you can’t accommodate arguments that the science strongly points to a designer. In fact, elsewhere, when not claiming others have an agenda, Krauss has advocated the abolishment of religion:

Religion will go away in a generation, or at least largely go away - and that's what I think we have an obligation to do.

And Krauss says Metaxas is the one with the agenda?

Unfortunately Krauss’ criticism goes downhill from there. Krauss explains that “The piece was rife with inappropriate scientific misrepresentations.”

Misrepresentations?

Metaxas makes two basic arguments. First, evolution’s just-add-water view of life had led astronomers to expect that the universe is teeming with life. ETs should be common and if we point our radios to the stars we should eventually pick up some interesting signals. But no such signals have been found. It is a clear example of yet another falsified evolutionary expectation. In fact Metaxas cites some astronomers who have argued the probabilities for ET life is far lower than previously expected.

Second, Metaxas points out that the universe is fine-tuned for life. And while this is a more subtle point, no one questions that this is an interesting and powerful set of evidences that must be reckoned with. That is, except Krauss.

Metaxas is pointing to fundamental findings. While there is much more to say about them, there is no misrepresentation as Krauss charges.

In fact, again, it is Krauss who is the one who is guilty of his charges. Krauss makes the common evolutionary appeal to future findings. “We currently DO NOT know,” the publicly-funded professor begins, “the factors that allow the evolution of life in the Universe.” [emphasis in original]

I’ve seen this response many times. Evolutionists argue the science proves their theory, and when they are presented with the actual evidence they then make the argument from ignorance. So what if the evidence is against them, future science might switch things around.

Absolutely. That certainly is true. Who knows what science may discover in the future.

But that is irrelevant.

No one is talking about unknown findings at some unknown time in the future made by some unknown scientist. Metaxas is talking about the here and now. He made no sweeping metaphysical claims, as evolutionists do. He was merely discussing today’s science.

It gets worse.

Krauss next resorts to a silly straw man version of Metaxas’ simple point:

The mistake made by the author is akin to saying that if one looks at all the factors in my life that led directly to my sitting at my computer to write this, one would obtain a probability so small as to conclude that it is impossible that anyone else could ever sit down to compose a letter to the WSJ.

At this point Krauss wasn’t even wrong. Did he write this while standing in line to board his next flight?

Krauss continues with more misrepresentations of both science and Metaxas’ points. It is true that science isn’t going to prove anything for Krauss and the evolutionists. One can always interpret the evidence to support the chance-creationism hypothesis. Just look at how Krauss ends his rebuttal:

The appearance of design of life on Earth is also overwhelming, but we now understand, thanks to Charles Darwin that the appearance of design is not the same as design, it is in fact a remnant of the remarkable efficiency of natural selection.

The remarkable efficiency of natural selection?

Krauss is apparently unaware of the most basic biological research in the past half century. Yes, Charles Darwin hoped for such remarkable efficiency. And yes he presented many thought experiments for why he believed it to be true. But that was nineteenth century naturalism. There was no scientific evidence for it then, and we now understand much more about the many problems with that view.

Krauss’ criticism of Metaxas reveals the pathetic state of evolutionary thinking. Religion drives science, and it matters.

Potassium Channels Even More Clever Than Thought

What Was Thought to be the Problem is Actually the Solution

At the cellular level our bodies depend on a delicate balance of ions that is constantly adjusted. Potassium ions, for example, are atoms with one missing electron which are constantly streaming into or out of our cells. These positively charged ions enter and exit the cell via huge protein machines called channels which are imbedded in the cell wall and, like a donut, have a hole in the middle through which the ions flow. What is astonishing is how well these channels work. Not only do they open and close as needed, but they have two seemingly impossible design features. On the one hand they are extremely selective, allowing only a particular type of ion to flow through it. But on the other hand, they allow the chosen ions to flow through incredibly fast. It would seem that high selectivity would come at the cost of a slow transmission rate. But no, potassium channels for instance filter out practically everything but potassium ions, and yet their flow rate is practically at the maximum speed that is physically attainable. Now a recent study has added more information about how potassium channels perform their amazing feats.

One of the conundrums with ion channels is how ions of like charge, which therefore repulse each other, could be stuffed through the small hole in the ion channel protein machine. One possible answer is that the ions are separated from each other. For more than a decade now it has been thought that the potassium ions flowing through potassium channels are separated by water molecules. This would avoid the problem that the positively charged potassium ions repel each other, not making for a very smooth or concentrated flow.

The new study, however, persuasively argues that, in fact, the potassium ions travel together, not separated by water molecules. This higher concentration of potassium ions is achieved with a subtle, complex design of the charge contour within the channel. In fact, as the paper explains, the “repulsion between adjacent ions is found to be the key to high-efficiency K+ conduction.”

That's incredible, and this poses a problem for the theory of evolution because it means that random mutations, rather than forming a gene that produces some simple, easily formed molecular donut, instead must have discovered an astronomically unlikely design. Final causes and teleology which are so much despised by evolutionists are clearly the better explanation for the potassium channel. No that doesn’t mean science comes to an end, no that is not a religious explanation, and no that isn’t the final word in the matter. That is just what the science is telling us, loud and clear.

Making New Genes Just Got More Exotic—Yes They Evolve, But How?

Gene Duplication Meets Epigenetics

Making new genes is not easy. For several decades now it has been thought that the only process for gene construction is to start by duplicating an existing gene and then making adjustments to it (it is not the only process, but that is another story). Naturally evolutionists interpreted this duplication process as another example of an evolutionary mechanism. What they don’t consider, however, is how subtle this process is.

Too often the origins debate focuses on the simplistic opposites of stasis and change. Evolutionists would have it that creationism and Intelligent Design require complete stasis in the biological world—no changing of the species. Any change that is discovered, such as the construction of new genes, is interpreted as yet another proof text of evolution.

But why? Why should we think that adaptation to the environment immediately implies random mutations, natural selection, common descent, a strictly blind naturalistic origin of the world via chance, and so forth?

It simply doesn’t follow. In fact, research has consistently shown that the species have a fantastically complicated built-in adaptation capability. Organisms respond rapidly to environmental challenges with directed changes, not slowly with blind, random changes.

As research continues, this story just continues to grow. For instance, regarding the construction of new genes via duplication of an existing gene, a recent study added yet another layer of incredible subtlety and nuance. It turns out that after a duplication event, the organism often labels one of the genes, as though marking it for change. The technical term for this is “epigenetics,” and it amounts to small chemicals being attached to either the gene or to the proteins about which the gene is wrapped. This chemical “barcode” technology is fantastic, as it involves several different chemicals, each conveying a different message, and the message varies depending on just where the chemical is attached. The chemicals can even be attached to other small chemicals that were previously attached.

So the question is not, “Do organisms change and adapt?” It would be silly to make that the test for the truth of blind, random evolution.

There is no question that organisms adapt. And so there is no question that species undergo evolution in the sense that they respond and change. But evolution can be a loaded term. For most of us, evolutionists included, “evolution” automatically means the undirected origins of, well, pretty much everything.

That simply is not what the science shows. The science shows that the species adapt via fantastically exotic, creative and nuanced processes. Perhaps all of this arose from an undirected origins, but that would call for an enormous serendipity. In other words, it would mean that the incredible processes by which evolution occurs were, themselves, created by evolution. That would be highly serendipitous.

We need to stick to what the evidence tells us, and be careful not to make unwarranted claims beyond the evidence.

Is the Origin of New Genes “Basically a Solved Problem”?

De Novo Genes a Done Deal

It is no surprise that proteins—the essential machines of life—are not likely to have evolved. At least, that is, if you believe in science. Even according to evolutionists and the most optimistic assumptions possible, the evolution of proteins is so unlikely it is beyond practical consideration. While this conclusion is intuitive and hardly surprising, there are several reasons for it. One of the reasons is that the scenarios evolutionists typically envision involve the pre existence of proteins. For instance, proteins are needed to create proteins, at least in today’s biological world. Indeed, proteins are also required for life as we know it. So the first proteins would have had to evolved in a very different kind of biological world. Another reason why protein evolution is difficult is that the fitness landscape in protein sequence space is mostly flat and rugged. A few random mutations will quickly degrade protein function and most of the hyper-dimensional sequence space has little or no function and is far from a useful protein. It is extremely difficult for a random sequence to migrate via mutations close enough to a useful protein for natural selection to take over. In fact this challenge makes the protein evolution difficult regardless of whether proteins already exist. But in spite of this problem, evolutionists believe that protein evolution is not a significant problem. Recently an evolutionist commented that it is “basically a solved problem.”

It wasn’t too many years ago that evolutionists ruled out such protein evolution. Because the problem is so difficult, they believed proteins somehow evolved very early in evolutionary history, and have merely undergone various modifications ever since. As one recent paper explains:

In the pre-genomics era it was widely assumed that much of present-day genetic diversity could be traced by common ancestry to a molecular big bang, where all genes evolved at once.

Likewise another recent paper states:

The emergence of new genes has long been thought to be almost exclusively driven by duplication or recombination of existing gene fragments. The possibility of de novo evolution from intergenic non-coding sequences seemed remote.

But that has all changed now. Not because evolutionists have figured out how new proteins can evolve de novo, but because, if evolution is true, new proteins must have evolved de novo. For in this post-genomic era, we now know that the genomes of species are chocked full of unique, one-time, protein-coding genes. They are not found in allied species, which under evolutionary theory means those genes must have evolved relatively recently.

But how?

If you read the headlines, you would have the impression that the problem is well in hand. For instance, super-star science writer Carl Zimmer wrote in the New York Times earlier this year that “researchers have documented the step-by-step process by which a new gene can come into existence.”

Case closed right?

Well not quite. In fact, not even close. What Zimmer tells his readers is a “step-by-step process” is what scientists affectionately refer to as a cartoon. In fact, here it is:

Was it not a bit serendipitous that DNA segments could so easily become transcribed?

And once transcribed and translated, the resulting protein would most likely be worthless junk. It would be somewhere in the middle of that rugged protein sequence hyperspace, light years away from a design that would improve fitness.

Yet the protein would continue to be synthesized by the patient organism, waiting forever as mutations randomly sample the rugged hyperspace. If that was the case then such experiments would rapidly accumulate, and the organism would be producing a plethora of junk proteins.

For this evolutionists envision that the rapid rise of these experimental protein-coding genes is offset by their destruction:

This fast rate of gene emergence raises the question why the genomes do not fill up with such genes over time. In spite of huge variation in total genome size, genomes do not show a proportionally large variation in terms of protein-coding repertoires. Hence, the emergence rate of new genes must in some way be balanced with a corresponding loss rate.

So evolutionists must say that mutations halt the progress, for example, by creating stop codons somewhere in the middle of the gene.

But if that was the case, then the incredible problem of searching through the sequence space just became that much more impossible. Not only must we search through an astronomically huge, flat, rugged fitness landscape that makes finding a needle in a haystack seem trivial, but now the searches are routinely interrupted and must start all over again from scratch. It is an evolutionary treadmill where mutations are working furiously and getting nowhere as they are continually creating and destroying genes.

This evolutionary narrative is certainly not “basically a solved problem.” In fact, what evolutionists have are high claims of the spontaneous evolution of incredibly complex structures, not because of the evidence, but in spite of the evidence.

So what gives evolutionist’s their confidence? It is not that they understand how such genes could have evolved, but that the genes are observed over and over. And since evolution must be true, then those solo genes must evolved:

Several studies have by now also shown that de novo emerged transcripts and proteins can assume a function within the organism. All of this provided solid evidence that de novo gene birth was indeed possible.

And what exactly do these studies show? Did they really show that “de novo emerged transcripts and proteins can assume a function within the organism”?

Not exactly.

One study found a gene in a yeast species, but the corresponding genome location in allied species came up blank. Again, it is the belief that evolution must be true that does the heavy lifting. A gene is found in a species, it is not found in allied species, those species must share a common ancestor, that common ancestor must have existed relatively recently because the species are similar, therefore the gene must have evolved recently.

It all hinges on evolution being true.

The same logic applies in the other studies, such as the one which found a gene in the mouse genome that is missing in other mammals.

Two more studies found more of these de novo genes in the fly genome, and upon testing discovered that such genes are often surprisingly essential. That doesn’t help. Now, the genes must not only have somehow evolved rapidly, they must have rapidly become essential. It was another surprise for evolutionists. Other studies have found genes in only some individuals, within a population.

Does any of this mean that the de novo genes evolved from random mutations as the evolutionists claim? Of course not.

This de novo gene story parallels the twentieth century evolutionary insistence that species adapt by random biological variation, not geared to help with the current environmental challenges. Those random variations are then subject to natural selection, and the resulting adaptation is the first step toward the large-scale change evolution requires to create the species.

Only recently have evolutionists begun to reckon with the failure of that narrative. I don’t know how genes arose, but once again evolutionists have made unscientific and unsubstantiated claims, and set themselves up for another failure. Only a few years ago they agreed that such evolution of new genes would be impossible. Now they have been forced to adopt it because the evidence unambiguously reveals solo genes, and evolutionists dogmatically insist that everything must have spontaneously evolved. So it is yet another false prediction followed by yet another epicycle, making the theory far more complicated and unlikely.

Müller Cells are Wavelength-Dependent Wave-Guides

Enhancing the Cone Photoreceptor Sensitivity

The best arguments for evolution have always been from dysteleology. This world, as evolutionists explain, just does not appear to have been designed. Consider our retina for example. Isn’t it all backwards, with the photocells—which detect the incoming light—pointed toward the rear and behind several layers of cell types and neural processes. Does this make any sense? Surely such a claptrap would offend any “tidy-minded engineer,” as Richard Dawkins put it. But such arguments have never worked and the history of evolutionary thought is full of their failures. Aside from the fact they are metaphysical and not open to scientific testing, they inevitably are simply false. The “bad retina design” argument, as discussed here, here, here, here and here for example, has repeatedly been rebuked. As we learn more we find the retina has all kinds of subtle and clever designs. And now new research out of Israel continues to confirm this trend. Unbelievably, the scientists have demonstrated that the retina’s Müller cells are wavelength-dependent wave-guides that focus the longer-wavelength green-red light onto the cone photoreceptors and pass the shorter-wavelength blue-purple light through to the rod photoreceptors.

It just so happens that is a great idea because while the cone photoreceptors are fast acting and provide color vision, they are less sensitive and need all the help they can get. The rod photoreceptors, on the other hand, are mainly sensitive to the shorter-wavelength blue-purple light, so they don’t miss too much the filtering out of the green-red light. As one science writer concluded:

Having the photoreceptors at the back of the retina is not a design constraint, it is a design feature. The idea that the vertebrate eye, like a traditional front-illuminated camera, might have been improved somehow if it had only been able to orient its wiring behind the photoreceptor layer, like a cephalopod, is folly.

It just isn’t very smart to criticize a design when you’ve never built one yourself and, much less, don’t even know how it works. It’s even worse to then use that ill-conceived criticism as justification for the claim that the design arose spontaneously. From a scientific perspective that claim was always weak. Now it is simply ridiculous. The retina’s incredible design reveals the details of what always was intuitively obvious. As Paul explained, God has made foolish the wisdom of this world.

Now Long Non-Coding RNA Makes Proteins: ENCODE 1, Dan Graur 0

It’s Not Nice to Fool Mother Nature

It has become increasingly obvious that lncRNAs are functional but now Kristian Baker’s group has implicated them in encoding proteins, just like mRNA. As one report explains:

Previously, lncRNAs were thought to lack the information and capacity to encode for proteins, distinguishing them from the messenger RNAs that are expressed from known genes and act primarily as templates for the synthesis of proteins. Yet this team demonstrated that a subset of these lncRNAs is engaged by the translation machinery and can function to produce protein products

This means that Dan Graur’s doubling down last year (either our genome is mostly junk or evolution is false) is rapidly going the way of every other evolutionary prediction (and that would be down).

It’s not nice to bet against Mother Nature.