Analysis of Genomes Converges on the Case for a Creator

00018
By Fazale Rana – November 13, 2019

Are you a Marvel or a DC fan?

Do you like the Marvel superheroes better than those who occupy the DC universe? Or is it the other way around for you?

Even though you might prefer DC over Marvel (or Marvel over DC), over the years these two comic book rivals have often created superheroes with nearly identical powers. In fact, a number of Marvel and DC superheroes are so strikingly similar that their likeness to one another is obviously intentional.1

Here are just a few of the superheroes Marvel and DC have ripped off each other:

  • Superman (DC, created in 1938) and Hyperion (Marvel, created in 1969)
  • Batman (DC, created in 1939) and Moon Knight (Marvel, created in 1975)
  • Green Lantern (DC, created in 1940) and Nova (Marvel, created in 1976)
  • Catwoman (DC, created in 1940) and Black Cat (Marvel, created in 1979)
  • Atom (DC, created in 1961) and Ant-Man (Marvel, created in 1962)
  • Aquaman (DC, created in 1941) and Namor (Marvel, created in 1939)
  • Green Arrow (DC, created in 1941) and Hawkeye (Marvel, created in 1964)
  • Swamp Thing (DC, created in 1971) and Man Thing (Marvel, created in 1971)
  • Deathstroke (DC, created in 1980) and Deadpool (Marvel, created in 1991)

This same type of striking similarity is also found in biology. Life scientists have discovered countless examples of biological designs that are virtually exact replicas of one another. Yet, these identical (or nearly identical) designs occur in organisms that belong to distinct, unrelated groups (such as the camera eyes of vertebrates and octopi). Therefore, they must have an independent origin.

 

blog__inline--analysis-of-genomes-converges-1

Figure 1: The Camera Eyes of Vertebrates (left) and Cephalopods (right); 1: Retina; 2: Nerve Fibers; 3: Optic Nerve; 4: Blind Spot. Image credit: Wikipedia

From an evolutionary perspective, it appears as if the evolutionary process independently and repeatedly arrived at the same outcome, time and time again. As evolutionary biologists Simon Conway Morris and George McGhee point out in their respective books, Life’s Solution and Convergent Evolution, identical evolutionary outcomes are a widespread feature of the biological realm.2 Scientists observe these repeated outcomes (known as convergence) at the ecological, organismal, biochemical, and genetic levels.

From my perspective, the widespread occurrence of convergent evolution is a feature of biology that evolutionary theory can’t genuinely explain. In fact, I see pervasive convergence as a failed scientific prediction—for the evolutionary paradigm. Recent work by a research team from Stanford University demonstrates my point.3

These researchers discovered that identical genetic changes occurred when: (1) bats and whales “evolved” echolocation, (2) killer whales and manatees “evolved” specialized skin in support of their aquatic lifestyles, and (3) pikas and alpacas “evolved” increased lung capacity required to live in high-altitude environments.

Why do I think this discovery is so problematic for the evolutionary paradigm? To understand my concern, we first need to consider the nature of the evolutionary process.

Biological Evolution Is Historically Contingent

Essentially, chance governs biological and biochemical evolution at its most fundamental level. Evolutionary pathways consist of a historical sequence of chance genetic changes operated on by natural selection, which, too, consists of chance components. The consequences are profound. If evolutionary events could be repeated, the outcome would be dramatically different every time. The inability of evolutionary processes to retrace the same path makes it highly unlikely that the same biological and biochemical designs should appear repeatedly throughout nature.

The concept of historical contingency embodies this idea and is the theme of Stephen Jay Gould’s book Wonderful Life.4 To help illustrate the concept, Gould uses the metaphor of “replaying life’s tape.” If one were to push the rewind button, erase life’s history, and then let the tape run again, the results would be completely different each time.

Are Evolutionary Processes Historically Contingent?

Gould based the concept of historical contingency on his understanding of the evolutionary process. In the decades since Gould’s original description of historical contingency, several studies have affirmed his view.

For example, in a landmark study in 2002, two Canadian investigators simulated macroevolutionary processes using autonomously replicating computer programs, with the programs operating like digital organisms.5 These programs were placed into different “ecosystems” and, because they replicated autonomously, could evolve. By monitoring the long-term evolution of the digital organisms, the two researchers determined that evolutionary outcomes are historically contingent and unpredictable. Every time they placed the same digital organism in the same environment, it evolved along a unique trajectory.

In other words, given the historically contingent nature of the evolutionary mechanisms, we would expect convergence to be rare in the biological realm. Yet, biologists continue to uncover example after example of convergent features—some of which are quite astounding.

The Origin of Echolocation

One of the most remarkable examples of convergence is the independent origin of echolocation (sound waves emitted from an organism to an object and then back to the organism) in bats (chiropterans) and cetaceans (toothed whales). Research indicates that echolocation arose independently in two different groups of bats and also in the toothed whales.

 

blog__inline--analysis-of-genomes-converges-2

Figure 2: Echolocation in Bats. Image credit: Shutterstock

One reason why this example of convergence is so remarkable has to do with the way some evolutionary biologists account for the widespread occurrences of convergence in biological systems. Undaunted by the myriad examples of convergence, these scientists assert that independent evolutionary outcomes result when unrelated organisms encounter nearly identical selection forces (e.g., environmental, competitive, and predatory pressures). According to this idea, natural selection channels unrelated organisms down similar pathways toward the same endpoint.

But this explanation is unsatisfactory because bats and whales live in different types of habitats (terrestrial and aquatic). Consequently, the genetic changes responsible for the independent emergence of echolocation in the chiropterans and cetaceans should be distinct. Presumably, the evolutionary pathways that converged on a complex biological system such as echolocation would have taken different routes that would be reflected in the genomes. In other words, even though the physical traits appear to be identical (or nearly identical), the genetic makeup of the organisms should reflect an independent evolutionary history.

But this expectation isn’t borne out by the data.

Genetic Convergence Parallels Trait Convergence

In recent years, evolutionary biologists have developed interest in understanding the genetic basis for convergence. Specifically, these scientists want to understand the genetic changes that lead to convergent anatomical and physiological features (how genotype leads to phenotype).

Toward this end, a Stanford research team developed an algorithm that allowed them to search through entire genome sequences of animals to identify similar genetic features that contribute to particular biological traits.6 In turn, they applied this method to three test cases related to the convergence of:

  • echolocation in bats and whales
  • scaly skin in killer whales
  • lung structure and capacity in pikas and alpacas

The investigators discovered that for echolocating animals, the same 25 convergent genetic changes took place in their genomes and were distributed among the same 18 genes. As it turns out, these genes play a role in the development of the cochlear ganglion, thought to be involved in echolocation. They also discovered that for aquatic mammals, there were 27 identical convergent genetic changes that occurred in same 15 genes that play a role in skin development. And finally, for high-altitude animals, they learned that the same 25 convergent genetic changes occurred in the same 16 genes that play a role in lung development.

In response to this finding, study author Gill Bejerano remarked, “These genes often control multiple functions in different tissues throughout the body, so it seems it would be very difficult to introduce even minor changes. But here we’ve found that not only do these very different species share specific genetic changes, but also that these changes occur in coding genes.”7

In other words, these results are not expected from an evolutionary standpoint. It is nothing short of amazing that genetic convergence would parallel phenotypic convergence.

On the other hand, these results make perfect sense from a creation model vantage point.

Convergence and the Case for Creation

Instead of viewing convergent features as having emerged through repeated evolutionary outcomes, we could understand them as reflecting the work of a Divine Mind. In this scheme, the repeated origins of biological features equate to the repeated creations by an Intelligent Agent who employs a common set of solutions to address a common set of problems facing unrelated organisms.

Like the superhero rip-offs in the Marvel and DC comics, the convergent features in biology appear to be intentional, reflecting a teleology that appears to be endemic in living systems.

Resources

Convergence of Echolocation

The Historical Contingency of the Evolutionary Process

Endnotes
  1. Jamie Gerber, “15 DC and Marvel Superheroes Who Are Strikingly Similar,” ScreenRant (November 12, 2016), screenrant.com/marvel-dc-superheroes-copies-rip-offs/.
  2. Simon Conway Morris, Life’s Solution: Inevitable Humans in a Lonely Universe (New York: Cambridge University Press, 2003); George McGhee, Convergent Evolution: Limited Forms Most Beautiful (Cambridge, MA: MIT Press, 2011).
  3. Amir Marcovitz et al., “A Functional Enrichment Test for Molecular Convergent Evolution Finds a Clear Protein-Coding Signal in Echolocating Bats and Whales,” Proceedings of the National Academy of Sciences, USA 116, no. 42 (October 15, 2019), 21094–21103, doi:10.1073/pnas.1818532116.
  4. Stephen Jay Gould, Wonderful Life: The Burgess Shale and the Nature of History (New York: W. W. Norton & Company, 1990).
  5. Gabriel Yedid and Graham Bell, “Macroevolution Simulated with Autonomously Replicating Computer Programs,” Nature 420 (December 19, 2002): 810–12, doi:10.1038/nature01151.
  6. Marcovitz et al., “A Functional Enrichment Test.”
  7. Stanford Medicine, “Scientists Uncover Genetic Similarities among Species That Use Sound to Navigate,” ScienceDaily, October 4, 2019, sciencedaily.com/releases/2019/10/191004105643.htm.

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2019/11/13/analysis-of-genomes-converges-on-the-case-for-a-creator

Origin and Design of the Genetic Code: A One-Two Punch for Creation

00015

By Fazale Rana – October 23, 2019

So, in the spirit of the endless debates that take place on sports talk radio, I ask: What duo is the greatest one-two punch in NBA history? Is it:

  • Kareem and Magic?
  • Kobe and Shaq?
  • Michael and Scottie?

Another confession: I am a science-faith junkie. I never tire when it comes to engaging in discussions about the interplay between science and the Christian faith. From my perspective, the most interesting facet of this conversation centers around the scientific evidence for God’s existence.

So, toward this end, I ask: What is the most compelling biochemical evidence for God’s existence? Is it:

  • The complexity of biochemical systems?
  • The eerie similarity between biomolecular motors and machines designed by human engineers?
  • The information found in DNA?

Without hesitation I would say it is actually another feature: the origin and design of the genetic code.

The genetic code is a biochemical code that consists of a set of rules defining the information stored in DNA. These rules specify the sequence of amino acids used by the cell’s machinery to synthesize proteins. The genetic code makes it possible for the biochemical apparatus in the cell to convert the information formatted as nucleotide sequences in DNA into information formatted as amino acid sequences in proteins.

 

blog__inline--origin-and-design-of-the-genetic-code

Figure: A Depiction of the Genetic Code. Image credit: Shutterstock

In previous articles (see the Resources section), I discussed the code’s most salient feature that I think points to a Creator’s handiwork: it’s multidimensional optimization. That optimization is so extensive that evolutionary biologists struggle to account for it’s origin, as illustrated by the work of biologist Steven Massey1.

Both the optimization of the genetic code and the failure of evolutionary processes to account for its design form a potent one-two punch, evincing the work of a Creator. Optimization is a marker of design, and if it can’t be accounted for through evolutionary processes, the design must be authentic—the product of a Mind.

Can Evolutionary Processes Generate the Genetic Code?

For biochemists working to understand the origin of the genetic code, its extreme optimization means that it is not the “frozen accident” that Francis Crick proposed in a classic paper titled “On the Origin of the Genetic Code.”2

Many investigators now think that natural selection shaped the genetic code, producing its optimal properties. However, I question if natural selection could evolve a genetic code with the degree of optimality displayed in nature. In the Cell’s Design (published in 2008), I cite the work of the late biophysicist Hubert Yockey in support of my claim.3 Yockey determined that natural selection would have to explore 1.40 x 1070 different genetic codes to discover the universal genetic code found in nature. Yockey estimated 6.3 x 1015 seconds (200 million years) is the maximum time available for the code to originate. Natural selection would have to evaluate roughly 1055 codes per second to find the universal genetic code. And even if the search time was extended for the entire duration of the universe’s existence, it still would require searching through 1052 codes per second to find nature’s genetic code. Put simply, natural selection lacks the time to find the universal genetic code.

Researchers from Germany raised the same difficulty for evolution recently. Because of the genetic code’s multidimensional optimality, they concluded that “the optimality of the SGC [standard genetic code] is a robust feature and cannot be explained by any simple evolutionary hypothesis proposed so far. . . . the probability of finding the standard genetic code by chance is very low. Selection is not an omnipotent force, so this raises the question of whether a selection process could have found the SGC in the case of extreme code optimalities.”4

Two More Evolutionary Mechanisms Considered

Life scientist Massey reached a similar conclusion through a detailed analysis of two possible evolutionary mechanisms, both based on natural selection.9

If the genetic code evolved, then alternate genetic codes would have to have been generated and evaluated until the optimal genetic code found in nature was discovered. This process would require that coding assignments change. Biochemists have identified two mechanisms that could contribute to coding reassignments: (1) codon capture and (2) an ambiguous intermediate mechanism. Massey tested both mechanisms.

Massey discovered that neither mechanism can evolve the optimal genetic code. When he ran computer simulations of the evolutionary process using codon capture as a mechanism, they all ended in failure, unable to find a highly optimized genetic code. When Massey ran simulations with the ambiguous intermediate mechanism, he could evolve an optimized genetic code. But he didn’t view this result as success. He learned that it takes between 20 to 30 codon reassignments to produce a genetic code with the same degree of optimization as the genetic code found in nature.

The problem with this evolutionary mechanism is that the number of coding reassignments observed in nature is scarce based on the few deviants of the genetic code thought to have evolved since the origin of the last common ancestor. On top of this problem, the structure of the optimized codes that evolved via the ambiguous intermediate mechanism is different from the structure of the genetic code found in nature. In short, the result obtained via the ambiguous intermediate mechanism is unrealistic.

As Massey points out, “The evolution of the SGC remains to be deciphered, and constitutes one of the greatest challenges in the field of molecular evolution.”10

Making Sense of Explanatory Models

In the face of these discouraging results for the evolutionary paradigm, Massey concludes that perhaps another evolutionary force apart from natural selection shaped the genetic code. One idea Massey thinks has merit is the Coevolution Theory proposed by J. T. Wong. Wong argued that the genetic code evolved in conjunction with the evolution of biosynthetic pathways that produce amino acids. Yet, Wong’s theory doesn’t account for the extreme optimization of the genetic code in nature. And, in fact, the relationships between coding assignments and amino acid biosynthesis appear to result from a statistical artifact, and nothing more.11 In other words, Wong’s ideas don’t work.

That brings us back to the question of how to account for the genetic code’s optimization and design.

As I see it, in the same way that two NBA superstars work together to help produce a championship-caliber team, the genetic code’s optimization and the failure of every evolutionary model to account for it form a potent one-two punch that makes a case for a Creator.

And that is worth talking about.

Resources

Endnotes
  1. Steven E. Massey, “Searching of Code Space for an Error-Minimized Genetic Code via Codon Capture Leads to Failure, or Requires at Least 20 Improving Codon Reassignments via the Ambiguous Intermediate Mechanism,” Journal of Molecular Evolution 70, no. 1 (January 2010): 106–15, doi:10.1007/s00239-009-9313-7.
  2. F. H. C. Crick, “The Origin of the Genetic Code,” Journal of Molecular Biology 38, no. 3 (December 28, 1968): 367–79, doi:10.1016/0022-2836(68)90392-6.
  3. Hubert P. Yockey, Information Theory and Molecular Biology (Cambridge, UK: Cambridge University Press, 1992), 180–83.
  4. Stefan Wichmann and Zachary Ardern, “Optimality of the Standard Genetic Code Is Robust with Respect to Comparison Code Sets,” Biosystems 185 (November 2019): 104023, doi:10.1016/j.biosystems.2019.104023.
  5. Massey, “Searching of Code Space.”
  6. Massey, “Searching of Code Space.”
  7. Ramin Amirnovin, “An Analysis of the Metabolic Theory of the Origin of the Genetic Code,” Journal of Molecular Evolution 44, no. 5 (May 1997): 473–76, doi:10.1007//PL00006170.

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

New Insights into Genetic Code Optimization Signal Creator’s Handiwork

00014

By Fazale Rana – October 16, 2019

I knew my career as a baseball player would be short-lived when, as a thirteen-year-old, I made the transition from Little League to the Babe Ruth League, which uses official Major League Baseball rules. Suddenly there were a whole lot more rules for me to follow than I ever had to think about in Little League.

Unlike in Little League, at the Babe Ruth level the hitter and base runners have to know what the other is going to do. Usually, the third-base coach is responsible for this communication. Before each pitch is thrown, the third-base coach uses a series of hand signs to relay instructions to the hitter and base runners.

blog__inline--new-insights-into-genetic-code

Credit: Shutterstock

My inability to pick up the signs from the third-base coach was a harbinger for my doomed baseball career. I did okay when I was on base, but I struggled to pick up his signs when I was at bat.

The issue wasn’t that there were too many signs for me to memorize. I struggled recognizing the indicator sign.

To prevent the opposing team from stealing the signs, it is common for the third-base coach to use an indicator sign. Each time he relays instructions, the coach randomly runs through a series of signs. At some point in the sequence, the coach gives the indicator sign. When he does that, it means that the next signal is the actual sign.

All of this activity was simply too much for me to process. When I was at the plate, I couldn’t consistently keep up with the third-base coach. It got so bad that a couple of times the third-base coach had to call time-out and have me walk up the third-base line, so he could whisper to me what I was to do when I was at the plate. It was a bit humiliating.

Codes Come from Intelligent Agents

The signs relayed by a third-base coach to the hitter and base runners are a type of code—a set of rules used to convert and convey information across formats.

Experience teaches us that it takes intelligent agents, such as baseball coaches, to devise codes, even those that are rather basic in their design. The more sophisticated a code, the greater the level of ingenuity required to develop it.

Perhaps the most sophisticated codes of all are those that can detect errors during data transmission.

I sure could have used a code like that when I played baseball. It would have helped me if the hand signals used by the third-base coach were designed in such a way that I could always understand what he wanted, even if I failed to properly pick up the indicator signal.

The Genetic Code

As it turns out, just such a code exists in nature. It is one of the most sophisticated codes known to us—far more sophisticated than the best codes designed by the brightest computer engineers in the world. In fact, this code resides at the heart of biochemical systems. It is the genetic code.

This biochemical code consists of a set of rules that define the information stored in DNA. These rules specify the sequence of amino acids that the cell’s machinery uses to build proteins. In this process, information formatted as nucleotide sequences in DNA is converted into information formatted as amino acid sequences in proteins.

Moreover, the genetic code is universal, meaning that all life on Earth uses it.1

Biochemists marvel at the design of the genetic code, in part because its structure displays exquisite optimization. This optimization includes the capacity to dramatically curtail errors that result from mutations.

Recently, a team from Germany identified another facet of the genetic code that is highly optimized, further highlighting its remarkable qualities.2

The Optimal Genetic Code

As I describe in The Cell’s Design, scientists from Princeton University and the University of Bath (UK) quantified the error-minimization capacity of the genetic code during the 1990s. Their work indicated that the universal genetic code is optimized to withstand the potentially harmful effects of substitution mutations better than virtually any other conceivable genetic code.3

In 2018, another team of researchers from Germany demonstrated that the universal genetic code is also optimized to withstand the harmful effects of frameshift mutations—again, better than other conceivable codes.4

In 2007, researchers from Israel showed that the genetic code is also optimized to harbor overlapping codes.5 This is important because, in addition to the genetic code, regions of DNA harbor other overlapping codes that direct the binding of histone proteins, transcription factors, and the machinery that splices genes after they have been transcribed.

The Robust Optimality of the Genetic Code

With these previous studies serving as a backdrop, the German research team wanted to probe more deeply into the genetic code’s optimality. These researchers focused on potential optimality of three properties of the genetic code: (1) resistance to harmful effects of substitution mutations, (2) resistance to harmful effects of frameshift mutations, and (3) capacity to support overlapping genes.

As with earlier studies, the team assessed the optimality of the naturally occurring genetic code by comparing its performance with sets of random codes that are conceivable alternatives. For all three property comparisons, they discovered that the natural (or standard) genetic code (SGC) displays a high degree of optimality. The researchers write, “We find that the SGC’s optimality is very robust, as no code set with no optimised properties is found. We therefore conclude that the optimality of the SGC is a robust feature across all evolutionary hypotheses.”6

On top of this insight, the research team adds one other dimension to multidimensional optimality of the genetic code: its capacity to support overlapping genes.

Interestingly, the researchers also note that the results of their work raise significant challenges to evolutionary explanations for the genetic code, pointing to the code’s multidimensional optimality that is extreme in all dimensions. They write:

We conclude that the optimality of the SGC is a robust feature and cannot be explained by any simple evolutionary hypothesis proposed so far. . . . the probability of finding the standard genetic code by chance is very low. Selection is not an omnipotent force, so this raises the question of whether a selection process could have found the SGC in the case of extreme code optimalities.7

While natural selection isn’t omnipotent, a transcendent Creator would be, and could account for the genetic code’s extreme optimality.

The Genetic Code and the Case for a Creator

In The Cell’s Design, I point out that our common experience teaches us that codes come from minds. It’s true on the baseball diamond and true in the computer lab. By analogy, the mere existence of the genetic code suggests that biochemical systems come from a Mind—a conclusion that gains additional support when we consider the code’s sophistication and exquisite optimization.

The genetic code’s ability to withstand errors that arise from substitution and frameshift mutations, along with its optimal capacity to harbor multiple overlapping codes and overlapping genes, seems to defy naturalistic explanation.

As a neophyte playing baseball, I could barely manage the simple code the third-base coach used. How mind-boggling it is for me when I think of the vastly superior ingenuity and sophistication of the universal genetic code.

And, just like the hitter and base runner work together to produce runs in baseball, the elegant design of the genetic code and the inability of evolutionary processes to account for its extreme multidimensional optimization combine to make the case that a Creator played a role in the origin and design of biochemical systems.

With respect to the case for a Creator, the insight from the German research team hits it out of the park.

Resources:

Endnotes
  1. Some organisms have a genetic code that deviates from the universal code in one or two of the coding assignments. Presumably, these deviant codes originate when the universal genetic code evolves, altering coding assignments.
  2. Stefan Wichmann and Zachery Ardern, “Optimality of the Standard Genetic Code Is Robust with Respect to Comparison Code Sets,” Biosystems 185 (November 2019): 104023, doi:10.1016/j.biosystems.2019.104023.
  3. David Haig and Laurence D. Hurst, “A Quantitative Measure of Error Minimization in the Genetic Code,” Journal of Molecular Evolution 33, no. 5 (November 1991): 412–17, doi:1007/BF02103132; Gretchen Vogel, “Tracking the History of the Genetic Code,” Science 281, no. 5375 (July 17, 1998): 329–31, doi:1126/science.281.5375.329; Stephen J. Freeland and Laurence D. Hurst, “The Genetic Code Is One in a Million,” Journal of Molecular Evolution 47, no. 3 (September 1998): 238–48, doi:10.1007/PL00006381; Stephen J. Freeland et al., “Early Fixation of an Optimal Genetic Code,” Molecular Biology and Evolution 17, no. 4 (April 2000): 511–18, 10.1093/oxfordjournals.molbev.a026331.
  4. Regine Geyer and Amir Madany Mamlouk, “On the Efficiency of the Genetic Code after Frameshift Mutations,” PeerJ 6 (May 21, 2018): e4825, doi:10.7717/peerj.4825.
  5. Shalev Itzkovitz and Uri Alon, “The Genetic Code Is Nearly Optimal for Allowing Additional Information within Protein-Coding Sequences,” Genome Research 17, no. 4 (April 2007): 405–12, doi:10.1101/gr.5987307.
  6. Wichmann and Ardern, “Optimality.”
  7. Wichmann and Ardern, “Optimality.”

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

Is the Optimal Set of Protein Amino Acids Purposed by a Mind?

00013

By Fazale Rana – October 9, 2019

To get our assays to work properly, we had to carefully design and optimize each test before executing it with exacting precision in the laboratory. Optimizing these assays was no easy feat. It could take weeks of painstaking effort to get the protocols just right.

My experiences working in the lab taught me some important lessons that I carry with me today as a Christian apologist. One of these lessons has to do with optimization. Optimized systems don’t just happen, whether they are laboratory procedures, manufacturing operations, or well-designed objects or devices. Instead, optimization results from the insights and efforts of intelligent agents, and therefore serves as a sure indicator of intelligent design.

As it turns out, nearly every biochemical system appears to be highly optimized. For me, this fact indicates that life stems from a Mind. And as life scientists continue to characterize biochemical systems, they keep discovering more and more examples of biochemical optimization, as recent work by a large team of collaborators working at the Earth-Life Science Institute (ELSI) in Tokyo, Japan, illustrates.1

These researchers uncovered more evidence that the twenty amino acids encoded by the genetic code possess the optimal set of physicochemical properties. If not for these properties, it would not be possible for the cell to build proteins that could support the wide range of activities required to sustain living systems. This insight gives us important perspective into the structure-function relationships of proteins. It also has theological significance, adding to the biochemical case for a Creator.

Before describing the ELSI team’s work and its theological implications, a little background might be helpful for some readers. For those who are familiar with basic biochemistry, just skip ahead to Why These Twenty Amino Acids?

Background: Protein Structure

Proteins are large, complex molecules that play a key role in virtually all of the cell’s operations. Biochemists have long known that the three-dimensional structure of a protein dictates its function. Because proteins are such large, complex molecules, biochemists categorize protein structure into four different levels: primary, secondary, tertiary, and quaternary structures.

blog__inline--is-the-optimal-set-of-protein-amino-acids-1

Figure 1: The Four Levels of Protein Structure. Image credit: Shutterstock

  • A protein’s primary structure is the linear sequence of amino acids that make up each of its polypeptide chains.
  • The secondary structure refers to short-range three-dimensional arrangements of the polypeptide chain’s backbone arising from the interactions between chemical groups that make up its backbone. Three of the most common secondary structures are the random coil, alpha (α) helix, and beta (β) pleated sheet.
  • Tertiary structure describes the overall shape of the entire polypeptide chain and the location of each of its atoms in three-dimensional space. The structure and spatial orientation of the chemical groups that extend from the protein backbone are also part of the tertiary structure.
  • Quaternary structure arises when several individual polypeptide chains interact to form a functional protein complex.

Background: Amino Acids

The building blocks of proteins are amino acids. These compounds are characterized by having both an amino group and a carboxylic acid bound to a central carbon atom. Also bound to this carbon are a hydrogen atom and a substituent that biochemists call an R group.

blog__inline--is-the-optimal-set-of-protein-amino-acids-2

Figure 2: The Structure of a Typical Amino Acid. Image credit: Shutterstock

The R group determines the amino acid’s identity. For example, if the R group is hydrogen, the amino acid is called glycine. If the R group is a methyl group, the amino acid is called alanine.

Close to 150 amino acids are found in proteins. But only 19 amino acids (plus 1 imino acid, called proline) are specified by the genetic code. Biochemists refer to these 20 as the canonical set.

blog__inline--is-the-optimal-set-of-protein-amino-acids-3

Figure 3: The Protein-Forming Amino Acids. Image credit: Shutterstock

A protein’s primary structure forms when amino acids react with each other to form a linear chain, with the amino group of one amino acid combining with the carboxylic acid of another to form an amide linkage. (Sometimes biochemists call the linkage a peptide bond.)

blog__inline--is-the-optimal-set-of-protein-amino-acids-4

Figure 4: The Chemical Linkage between Amino Acids. Image credit: Shutterstock

The repeating amide linkages along the amino acid chain form the protein’s backbone. The amino acids’ R groups extend from the backbone, creating a distinct physicochemical profile along the protein chain for each unique amino acid sequence. To first approximation, this unique physicochemical profile dictates the protein’s higher-order structures and, hence, the protein’s function.

Why These Twenty Amino Acids?

Research has revealed that the set of amino acids used to build proteins is universal. In other words, the proteins found in every organism on Earth are made up of the same canonical set.

Biochemists have long wondered: Why these 20 amino acids?

In the early 1980s biochemists discovered that an exquisite molecular rationale undergirds the amino acid set used to make proteins.2 Every aspect of amino acid structure has to be precisely the way it is for life to be possible. On top of that, biochemists concluded that the set of 20 amino acids possesses the “just-right” physical and chemical properties that evenly and uniformly vary across a broad range of size, charge, and hydrophobicity (water resistance). In fact, it appears as if the amino acids selected for proteins seem to form a uniquely optimal set of 20 amino acids compared to random sets of amino acids.3

With these previous studies as a backdrop, the ELSI investigators wanted to develop a better understanding of the optimal nature of the universal set of amino acids used to build proteins. They also wanted to gain insight into the origin of the canonical set.

To do this they used a library of 1,913 amino acids (including the 20 amino acids that make up the canonical set) to construct random sets of amino acids. The researchers varied the set sizes from 3 to 20 amino acids and evaluated the performance of the random sets in terms of their capacity to support: (1) the folding of protein chains into three-dimensional structures; (2) protein catalytic activity; and (3) protein solubility.

They discovered that if a random set of amino acids included even a single amino acid from the canonical set, it dramatically out-performed random sets of the same size without any of the canonical amino acids. Based on these results, the researchers concluded that each of the 20 amino acids used to build proteins stands out, possessing highly unusual properties that make them ideally suited for their biochemical role, confirming the results of previous studies.

An Evolutionary Origin for the Canonical Set?

The ELSI researchers believe that—from an evolutionary standpoint—these results also shed light as to how the canonical set of amino acids emerged. Because of the unique adaptive properties of the canonical amino acids, the researchers speculate that “each time a CAA [canonical amino acid] was discovered and embedded during evolution, it provided an adaptive value unusual among many alternatives, and each selective step may have helped bootstrap the developing set to include still more CAAs.”4

In other words, the researchers offer the conjecture that whenever the evolutionary process stumbled upon one of the amino acids in the canonical set and incorporated it into nascent biochemical systems, the addition offered such a significant evolutionary advantage that it became instantiated into the biochemistry of the emerging cellular systems. Presumably, as this selection process occurred repeatedly over time, members of the canonical set would be added, one by one, to the evolving amino acid set, eventually culminating in the full canonical set.

Scientists find further support for this scenario in the following observation: some of the canonical amino acids seemingly play a more important role in optimizing smaller sets of amino acids, some play a more important role in optimizing intermediate size sets of amino acids, and others play a more prominent role in optimizing larger sets. They argue that this difference may reflect the sequence by which amino acids were added to the evolving set of amino acids as life emerged.

On the surface, this evolutionary explanation is not unreasonable. But more careful consideration of the idea raises concerns. For example, just because a canonical amino acid becomes incorporated into a set of amino acids and improves its adaptive value doesn’t mean that the resulting set of amino acids could produce the range of proteins with the solubility, foldability, and catalytic range needed to support life processes. Intuitively, it seems to me as a biochemist, that there must be a threshold for the number of canonical amino acids in any set of amino acids for it to have the range of physicochemical properties needed to build all the proteins needed to support minimal life.

I also question this evolutionary scenario because some of the amino acids that optimize smaller sets would not have been the ones present initially on the early Earth because they cannot be made by prebiotic reactions. Instead, many of the amino acids that optimize smaller sets can only be generated through biosynthetic routes that must have emerged much later in any evolutionary scenario for the origin of life.5 This limitation also means that the only way for some of the canonical amino acids to become incorporated into the canonical set is that multi-step biosynthetic routes for those amino acids evolved first. But if the full canonical set isn’t available, then it is questionable if the proteins needed to catalyze the biosynthesis of these amino acid would exist, resulting in a chicken-and-egg dilemma.

In light of these concerns, is there a better explanation for the highly optimized canonical set of amino acids?

A Creator’s Role?

Optimality of the universal set of protein amino acids finds explanation if life stems from a Creator’s handiwork. As noted, optimization is an indicator of intelligent design, achieved through foresight and preplanning. Optimization requires inordinate attention to detail and careful craftsmanship. By analogy, the optimized biochemistry epitomized by the amino acid set that makes up proteins rationally points to the work of a Creator.

Is There a Biochemical Anthropic Principle?

This discovery also leads to another philosophical implication: It lends support to the existence of a biochemical anthropic principle.

The ELSI researchers speculate that no matter the starting point in the evolutionary process, the pathways will all converge at the canonical set of amino acids because of the acids’ unusual adaptive properties. In other words, the amino acids that make up the universal set of protein-coding amino acids are not the outworking of an historically contingent evolutionary process, but instead seem to be fundamentally prescribed by the laws of nature. To put it differently, it appears as if the canonical set of amino acids has been preordained in some way.6 One of the study’s authors, Rudrarup Bose, suggests that “Life may not be just a set of accidental events. Rather, there may be some universal laws governing the evolution of life.”7

Though I prefer to see the origin of life as a creation event, it is important to recognize that even if one were to adopt an evolutionary perspective on life’s origin, it looks as if a Mind is responsible for jimmy-rigging the process to a predetermined endpoint. It looks as if a Mind purposed for life to be present in the universe and structured the laws of nature so that, in this case, the uniquely optimal canonical set of amino acids would inevitably emerge.

Along these lines, it is remarkable to think that the canonical set of amino acids has the precise properties needed for life to exist. This “coincidence” is eerie, to say the least. As a biochemist, I interpret this coincidence as evidence that our universe has been designed for a purpose. It is provocative to think that regardless of one’s perspective on the origin of life, the evidence converges toward a single conclusion: namely that life manifests from an intelligent agent—God.

Resources

The Optimality of Biochemical Systems

The Biochemical Anthropic Principle

Endnotes
  1. Melissa Ilardo et al., “Adaptive Properties of the Genetically Encoded Amino Acid Alphabet Are Inherited from Its Subset,” Scientific Reports 9, no. 12468 (August 28, 2019), doi:10.1038/s41598-019-47574-x.
  2. Arthur L. Weber and Stanley L. Miller, “Reasons for the Occurrence of the Twenty Coded Protein Amino Acids,” Journal of Molecular Evolution 17, no. 5 (September 1981): 273–84, doi:10.1007/BF01795749; H. James Cleaves II, “The Origin of the Biologically Coded Amino Acids,” Journal of Theoretical Biology 263, no. 4 (April 2010): 490–98, doi:10.1016/j.jtbi.2009.12.014.
  3. Gayle K. Philip and Stephen J. Freeland, “Did Evolution Select a Nonrandom ‘Alphabet’ of Amino Acids?” Astrobiology 11, no. 3 (April 2011), 235–40, doi:10.1089/ast.2010.0567; Matthias Granhold et al., “Modern Diversification of the Amino Acid Repertoire Driven by Oxygen,” Proceedings of the National Academy of Sciences, USA 115, no. 1 (January 2, 2018): 41–46, doi:10.1073/pnas.1717100115.
  4. Ilardo et al., “Adaptive Properties.”
  5. J. Tze-Fei Wong and Patricia M. Bronskill, “Inadequacy of Prebiotic Synthesis as Origin of Proteinous Amino Acids,” Journal of Molecular Evolution 13, no. 2 (June 1979): 115–25, doi:10.1007/BF01732867.
  6. Tokyo Institute of Technology, “Scientists Find Biology’s Optimal ‘Molecular Alphabet’ May Be Preordained,” ScienceDaily, September 10, 2019, http://www.sciencedaily.com/releases/2019/09/190910080017.htm.
  7. Tokyo Institute, “Scientists Find.”

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

ATP Transport Challenges the Evolutionary Origin of Mitochondria

00007

By Fazale Rana – August 21, 2019

In high school, I spent most Sunday mornings with my family gathered around the TV watching weekly reruns of the old Abbott and Costello movies.

blog__inline--atp-transport-challenges-1

Image: Bud Abbott and Lou Costello. Image credit: Wikipedia

One of my favorite routines has the two comedians trying to help a woman get her parallel-parked car out of a tight parking spot. As Costello takes his place behind the wheel, Abbott tells him to “Go ahead and back up.” And of course, confusion and hilarity follow as Costello repeatedly tries to clarify if he is to “go ahead” or “back up,” finally yelling, “Will you please make up your mind!”

As it turns out, biologists who are trying to account for the origin of mitochondria (through an evolutionary route) are just as confused about directions as Costello. Specifically, they are trying to determine which direction ATP transport occurred in the evolutionary precursors to mitochondria (referred to as pre-mitochondria).

In an attempt to address this question, a research team from the University of Virginia (UVA) has added to the frustration, raising new challenges for evolutionary explanations for the origin of mitochondria. Their work threatens to drive the scientific community off the evolutionary route into the ditch when it comes to explaining the origin of eukaryotic cells.1

To fully appreciate the problems this work creates for the endosymbiont hypothesis, a little background is in order. (For those familiar with the evidence for the endosymbiont hypothesis, you may want to skip ahead to The Role of Mitochondria.)

The Endosymbiont Hypothesis

Most biologists believe that the endosymbiont hypothesis serves as the best explanation for the origin of complex cells.

According to this idea, complex cells originated when symbiotic relationships formed among single-celled microbes after free-living bacterial and/or archaeal cells were engulfed by a “host” microbe.

The “poster children” of the endosymbiont hypothesis are mitochondria. Presumably, the mitochondria started its evolutionary journey as an endosymbiont. Evolutionary biologists believe that once engulfed by the host cell, this microbe took up permanent residency, growing and dividing inside the host. Over time, the endosymbiont and the host became mutually interdependent, with the endosymbiont providing a metabolic benefit for the host cell (such as providing a source of ATP). In turn, the host cell provided nutrients to the endosymbiont. Presumably, the endosymbiont gradually evolved into an organelle through a process referred to as genome reduction. This reduction resulted when genes from the endosymbiont’s genome were transferred into the genome of the host organism, generating the mitonuclear genome.

blog__inline--atp-transport-challenges-2

Image: Endosymbiont Hypothesis. Image credit: Wikipedia

Evidence for the Endosymbiont Hypothesis

Much of the evidence for the endosymbiotic origin of mitochondria centers around the similarity between mitochondria and bacteria. These organelles are about the same size and shape as typical bacteria and have a double membrane structure like gram-negative cells. These organelles also divide in a way that is reminiscent of bacterial cells.

Biochemical evidence also exists for the endosymbiont hypothesis. Evolutionary biologists view the presence of the diminutive mitochondrial genome as a vestige of this organelle’s evolutionary history. They see the biochemical similarities between mitochondrial and bacterial genomes as further evidence for the evolutionary origin of these organelles.

The presence of the unique lipid, cardiolipin, in the mitochondrial inner membrane also serves as evidence for the endosymbiont hypothesis. This important lipid component of bacterial inner membranes is absent in the membranes of eukaryotic cells—except for the inner membranes of mitochondria. In fact, biochemists consider cardiolipin a signature lipid for mitochondria and a vestige of the organelle’s evolutionary history.

The Role of Mitochondria

Mitochondria serve cells in a number of ways, including:

  • Calcium storage
  • Calcium signaling
  • Signaling with reactive oxygen species
  • Regulation of cellular metabolism
  • Heat production
  • Apoptosis

Arguably one of the most important functions of mitochondria relates to their role in energy conversion. This organelle generates ATP molecules by processing the breakdown products of glycolysis through the tricarboxylic acid cycle and the electron transport chain.

Biochemists refer to ATP as a high-energy compound—it serves as an energy currency for the cell, and most cellular processes are powered by ATP. One way that ATP provides energy is through its conversion to ADP and an inorganic phosphate molecule. This breakdown reaction liberates energy that can be coupled to cellular activities that require energy.

 

blog__inline--atp-transport-challenges-3

Image: The ATP/ADP Reaction Cycle. Image credit: Shutterstock

ATP Production and Transport

The enzyme complex ATP synthase, located in the mitochondrial inner membrane, generates ATP from ADP and inorganic phosphate, using a proton gradient generated by the flow of electrons through the electron transport chain. As ATP synthase generates ATP, it deposits this molecule in the innermost region of the mitochondria (called the matrix or the lumen).

In order for ATP to become available to power cellular processes, it has to be transported out of the lumen and across the mitochondrial inner membrane into the cytoplasm. Unfortunately, the inner mitochondrial membrane is impermeable to ATP (and ADP). In order to overcome this barrier, a protein embedded in the inner membrane called ATP/ADP translocase performs the transport operation. Conveniently, for every molecule of ATP transported out of the lumen, a molecule of ADP is transported from the cytoplasm into the lumen. In turn, this ADP is converted into ATP by ATP synthase.

Because of the importance of this process, copies of ATP/ADP translocase comprises 10% of the proteins in the inner membrane.

If this enzyme doesn’t function properly, it will result in mitochondrial myopathies.

The Problem ATP Transport Causes for The Endosymbiont Hypothesis

Two intertwined questions confronting the endosymbiont hypothesis relate to the evolutionary driving force behind symbiogenesis and the nature of pre-mitochondria.

Traditionally, evolutionary biologists have posited that the host cell was an anaerobe, while the endosymbiont was an aerobic microbe, producing ATP from lactic acid generated by the host cell. (Lactic acid is the breakdown product of glucose in the absence of oxygen).

But, as cell biologist Franklin Harold points out, this scenario has an inherent flaw. Namely, if the endosymbiont is producing ATP necessary for its survival from host cell nutrients, why would it relinquish some—or even all—of the ATP it produces to the host cell?

According to Harold, “The trouble is that unless the invaders share their bounty with the host, they will quickly outgrow him; they would be pathogens, not symbionts.”2

And, the only way they could share their bounty with the host cell is to transport ATP from the engulfed cell’s interior to the host cell’s cytoplasm. While mitochondria accomplish this task with the ATP/ADP translocase, there is no good reason to think that the engulfed cell would do this. Given the role ATP plays as the energy currency in the cell and the energy that is expended to make this molecule, there is no advantage for the engulfed cell to pump ATP from its interior to the exterior environment.

Harold sums up the problem this way: “Such a carrier would not have been present in the free-living symbiont but must have been acquired in the course of its enslavement; it cannot be called upon to explain the initial benefits of the association.”3

In other words, currently, there is no evolutionary explanation for why the ATP/ADP translocase in the mitochondrial inner membrane—a protein central to the role of mitochondria in eukaryotic cells—pumps ATP from the lumen to the cytoplasm.

Two Alternative Models

This problem has led evolutionary biologists to propose two alternative models to account for the evolutionary driving force behind symbiogenesis: 1) the hydrogen hypothesis; and 2) the oxygen scavenger hypothesis.

The hydrogen hypothesis argues that the host cell was a methanogenic member of archaea that consumed hydrogen gas and the symbiont was a hydrogen-generating alpha proteobacteria.

The oxygen-scavenging model suggests that the engulfed cell was aerobic, and because it used oxygen, it reduced the amount of oxygen in the cytoplasm of the host cell, thought to be an anaerobe.

Today, most evolutionary biologists prefer the hydrogen hypothesis—in part because the oxygen scavenger model, too, has a fatal flaw. As Harold points out, “This [oxygen scavenger model], too, is dubious, because respiration generates free radicals that are known to be a major source of damage to cellular membranes and genes.”4

Moving Forward, Or Moving Backward?

To help make headway, two researchers from UVA attempted to reconstruct the evolutionary precursor to mitochondria, dubbed pre-mitochondria.

Operating within the evolutionary framework, these two investigators reconstructed the putative genome of pre-mitochondria using genes in the mitochondrial genome and genes from the nuclear genomes of organisms they believe were transferred to the nucleus during the process of symbiogenesis. (Genes that clustered with alphaproteobacterial genes were deemed to be of mitochondrial origin.)

Based on their reconstruction, they conclude that the original engulfed cell actually used its ATP/ADP translocase to import ATP from the host cell cytoplasm into its interior, exchanging the ATP for an ADP. This is the type of ATP/ADP translocase found in obligate intracellular parasites alive today.

According to the authors, this means that:

“Pre-mitochondrion [was] an ‘energy scavenger’ and suggests an energy parasitism between the endosymbiont and its host at the origin of the mitochondria. . . . This is in sharp contrast with the current role of mitochondria as the cell’s energy producer and contradicts the traditional endosymbiotic theory that the symbiosis was driven by the symbiont supplying the host ATP.”5

The authors speculate that at some point during symbiogenesis the ATP/ADP translocase “went ahead and backed up,” reversing direction. But, this explanation is little more than a just-so story with no evidential support. Confounding their conjecture is their discovery that the ATP/ADP translocase found in mitochondria is evolutionarily unrelated to the ATP/ADP translocases found in obligate intracellular parasites.

The fact that the engulfed cell was an obligate intracellular parasite not only brings a halt to the traditional version of the endosymbiont hypothesis, it flattens the tires of both the oxygen scavenger model and hydrogen hypothesis. According to Wang and Wu (the UVA investigators):

“Our results suggest that mitochondria most likely originated from an obligate intracellular parasite and not from a free-living bacterium. This has important implications for our understanding of the origin of mitochondria. It implies that at the beginning of the endosymbiosis, the bacterial symbiont provided no benefits whatsoever to the host. Therefore we argue that the benefits proposed by various hypotheses (e.g, oxygen scavenger and hydrogen hypotheses) are irrelevant in explaining the establishment of the initial symbiosis.”6

If the results of the analysis by the UVA researchers stand, it leaves evolutionary biologists with no clear direction when it comes to determining the evolutionary driving force behind the early stages of symbiogenesis or the evolutionary route to mitochondria.

It seems that the more evolutionary biologists probe the question of mitochondrial origins, the more confusion and uncertainty results. In fact, there is not a coherent compelling evolutionary explanation for the origin of eukaryotic cells—one of the key events in life’s history. The study by the UVA investigators (along with other studies) casts aspersions on the most prominent evolutionary explanations for the origin of eukaryotes, justifying skepticism about the grand claim of the evolutionary paradigm: namely, that the origin, design, and history of life can be explained exclusively through evolutionary processes.

In light of this uncertainty, can the origin of mitochondria, and hence eukaryotic cells, be better explained by a creation model? I think so, but for many scientists this is a road less traveled.

Resources

Challenges to the Endosymbiont Hypothesis:

In Support of a Creation Model for the Origin of Eukaryotic Cells:

ATP Production and the Case for a Creator:

Endnotes
  1. Zhang Wang and Martin Wu, “Phylogenomic Reconstruction Indicates Mitochondrial Ancestor Was an Energy Parasite,” PLOS One 9, no. 10 (October 15, 2014): e110685, doi:10.1371/journal.pone.0110685.
  2. Franklin M. Harold, In Search of Cell History: The Evolution of Life’s Building Blocks (Chicago, IL: The University of Chicago Press, 2014), 131.
  3. Harold, In Search of Cell History, 131.
  4. Harold, In Search of Cell History, 132.
  5. Wang and Wu, “Phylogenomic Reconstruction.”
  6. Wang and Wu, “Phylogenomic Reconstruction.”

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

Does Information Come from a Mind?

00006

By Fazale Rana – August 14, 2019

Imagine you’re flying over the desert, and you notice a pile of rocks down below. Most likely, you would think little of it. But suppose the rocks were arranged to spell out a message. I bet you would conclude that someone had arranged those rocks to communicate something to you and others who might happen to fly over the desert.

You reach that conclusion because experience has taught you that messages come from persons/people—or, rather, that information comes from a mind. And, toward that end, information serves as a marker for the work of intelligent agency.

blog__inline--does-information-come-from-a-mind

Image credit: Shutterstock

Recently, a skeptic challenged me on this point, arguing that we can identify numerous examples of natural systems that harbor information, but that the information in these systems arose through natural processes—not a mind.

So, does information truly come from a mind? And can this claim be used to make a case for a Creator’s existence and role in life’s origin and design?

I think it can. And my reasons are outlined below.

Information and the Case for a Creator

In light of the (presumed) relationship between information and minds, I find it provocative that biochemical systems are information systems.

Two of the most important classes of information-harboring molecules are nucleic acids (DNA and RNA) and proteins. In both cases, the information content of these molecules arises from the nucleotide and amino acid sequences, respectively, that make up these two types of biomolecules.

The information harbored in nucleotide sequences of nucleic acids and amino acid sequences of proteins is digital information. Digital information is represented by a succession of discrete units, just like the ones and zeroes that encode the information manipulated by electronic devices. In this respect, sequences of nucleotides and amino acids for discrete informational units that encode the information in DNA and RNA and proteins, respectively.

But the information in nucleic acids and proteins also has analog characteristics. Analog information varies in an uninterrupted continuous manner, like radio waves used for broadcasting purposes. Analog information in nucleic acids and proteins are expressed through the three-dimensional structures adopted by both classes of biomolecules. (For more on the nature of biochemical information, see Resources.)

If our experience teaches us that information comes from minds, then the fact that key classes of biomolecules are comprised of both digital and analog information makes it reasonable to conclude that life itself stems from the work of a Mind.

Is Biochemical Information Really Information?

Skeptics, such as philosopher Massimo Pigliucci, often dismiss this particular design argument, maintaining that biochemical information is not genuine information. Instead, they maintain that when scientists refer to biomolecules as harboring information, they are employing an illustrative analogy—a scientific metaphor—and nothing more. They accuse creationists and intelligent design proponents of misconstruing scientists’ use of analogical language to make the case for a Creator.1

In light of this criticism, it is worth noting that the case for a Creator doesn’t merely rest on the presence of digital and analog information in biomolecules, but gains added support from work in information theory and bioinformatics.

For example, information theorist Bernd-Olaf Küppers points out in his classic work Information and the Origin of Life that the structure of the information housed in nucleic acids and proteins closely resembles the hierarchical organization of human language.2 This is what Küppers writes:

The analogy between human language and the molecular genetic language is quite strict. . . . Thus, central problems of the origin of biological information can adequately be illustrated by examples from human language without the sacrifice of exactitude.3

Added to this insight is the work by a team from NIH who discovered that the information content of proteins bears the same mathematical structure as human language. To this end, they discovered that a universal grammar exists that defines the structure of the biochemical information in proteins. (For more details on the NIH team’s work, see Resources.)

In other words, the discovery that the biochemical information shares the same features as human language deepens the analogy between biochemical information and the type of information we create as human designers. And, in doing so, it strengthens the case for a Creator.

Further Studies that Strengthen the Case for a Creator

So, too, does other work, such as studies in DNA barcoding. Biologists have been able to identify, catalog, and monitor animal and plant species using relatively short, standardized segments of DNA within genomes. They refer to these sequences as DNA barcodes that are analogous to the barcodes merchants use to price products and monitor inventory.

Typically, barcodes harbor information in the form of parallel dark lines on a white background, creating areas of high and low reflectance that can be read by a scanner and interpreted as binary numbers. Barcoding with DNA is possible because this biomolecule, at its essence, is an information-based system. To put it another way, this work demonstrates that the information in DNA is not metaphorical, but is in fact informational. (For more details on DNA barcoding, see “DNA Barcodes Used to Inventory Plant Biodiversity” in Resources.)

Work in nanotechnology also strengthens the analogy between biochemical information and the information we create as human designers. For example, a number of researchers are exploring DNA as a data storage medium. Again, this work demonstrates that biochemical information is information. (For details on DNA as a data storage medium, see Resources.)

Finally, researchers have learned that the protein machines that operate on DNA during processes such as transcription, replication, and repair literally operate like a computer system. In fact, the similarity is so strong that this insight has spawned a new area of nanotechnology called DNA computing. In other words, the cell’s machinery manipulates information in the same way human designers manipulate digital information. For more details, take a look at the article “Biochemical Turing Machines ‘Reboot’ the Watchmaker Argument” in Resources.)

The bottom line is this: The more we learn about the architecture and manipulation of biochemical information, the stronger the analogy becomes.

Does Information Come from a Mind?

Other skeptics challenge this argument in a different way. They assert that information can originate without a mind. For example, a skeptic recently challenged me this way:

“A volcano can generate information in the rocks it produces. From [the] information we observe, we can work out what it means. Namely, in this example, that the rock came from the volcano. There was no Mind in information generation, but rather minds at work, generating meaning.

Likewise, a growing tree can generate information through its rings. Humans can also generate information by producing sound waves.

However, I don’t think that volcanoes have minds, nor do trees—at least not the way we have minds.”

–Roland W. via Facebook

I find this to be an interesting point. But, I don’t think this objection undermines the case for a Creator. Ironically, I think it makes the case stronger. Before I explain why, though, I need to bring up an important clarification.

In Roland’s examples, he conflates two different types of information. When I refer to the analogy between human languages and biochemical information, I am specifically referring to semantic information, which consists of combinations of symbols that communicate meaning. In fact, Roland’s point about humans generating information with sound waves is an example of semantic information, with the sounds serving as combinations of ephemeral symbols.

The type of information found in volcanic rocks and tree rings is different from the semantic information found in human languages. It is actually algorithmic information, meaning that it consists of a set of instructions. And technically, the rocks and tree rings don’t contain this information—they result from it.

The reason why we can extract meaning and insight from rocks and tree rings is because of the laws of nature, which correspond to algorithmic information. We can think of these laws as instructions that determine the way the world works. Because we have discovered these laws, and because we have also discovered nature’s algorithms, we can extract insight and meaning from studying rocks and tree rings.

In fact, Küppers points out that biochemical systems also consist of sets of instructions instantiated within the biomolecules themselves. These instructions direct activities of the biomolecular systems and, hence, the cell’s operations. To put it another way, biochemical information is also algorithmic information.

From an algorithmic standpoint, the information content relates to the complexity of the instructions. The more complex the instructions, the greater the information content. To illustrate, consider a DNA sequence that consists of alternating nucleotides, AGAGAGAG . . . and so on. The instructions needed to generate this sequence are:

  1. Add an A
  2. Add a G
  3. Repeat steps 1 and 2, x number of times, where x corresponds to the length of the DNA sequence divided by 2

But what about a DNA sequence that corresponds to a typical gene? In effect, because there is no pattern to that sequence, the set of instructions needed to create that sequence is the sequence itself. In other words, a much greater amount of algorithmic information resides in a gene than in a repetitive DNA sequence.

And, of course, our common experience teaches us that information—whether it’s found in a gene, a rock pile, or a tree ring—comes from a Mind.

Resources

Endnotes
  1. For example, see Massimo Pigliucci and Maarten Boudry, “Why Machine-Information Metaphors Are Bad for Science and Science Education,” Science and Education 20, no. 5–6 (May 2011): 453–71; doi:10.1007/s11191-010-9267-6.
  2. Bernd-Olaf Küppers, Information and the Origin of Life (Boston, MA: MIT Press, 1990), 24–25.
  3. Küppers, Information, 23.

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

New Insights into Endothermy Heat Up the Case for a Creator

00005

By Fazale Rana – August 7, 2019

When I was younger, I was always hot. I needed to be in air conditioning everywhere I went. I could never get the temperature cold enough. But now that I am older, I feel like a frail person who is always chilled, needing to drape myself with a blanket to keep warm.

Nevertheless, like all human beings, I am still warm-blooded. I am an endotherm, as are all mammals and birds.

For many biologists, endothermy represents a bit of an enigma. Maintaining a constant body temperature requires an elevated basal metabolic rate. But the energy needed to preserve a constant body temperature doesn’t come cheap. In fact, warm-blooded animals demand 30 times the energy per unit time compared to cold-blooded (ectothermic) creatures.

Though biologists have tried to account for endothermy, no model has adequately explained why birds and mammals are warm-blooded. The advantages of being warm-blooded over being cold-blooded have not seemed to adequately outweigh costs—until now.

Recently, a biologist from the University of Nevada, Reno, Michael L. Logan, published a model that helps make sense of this enigma.1 His work evokes the optimal design and elegant rationale for endothermy in birds and mammals—and ectothermy in amphibians and reptiles.

An Explanation for Endothermy

For endothermy to exist, it must confer some significant advantage for animals’ constant, elevated body temperatures.

Logan argues that endothermy maintains mammalian and bird body temperatures close to the thermal optimum for immune system functionality. The operations of the immune system are temperature-dependent. If the temperature is too low or too high, the immune system responds poorly to infectious agents. But an elevated and stable body temperature primes mammalian and bird immune systems to rapidly and effectively respond to pathogens. When birds and mammals acquire a pathogen, their bodies mount a fever response. This slight elevation in temperature places their body temperature at the thermal optimum.

In other words, the fever response plays a critical role when animals battle infectious agents. And warm-blooded animals have the advantage of possessing body temperatures close to ideal.

Temperature and Immune System Function

A body of evidence indicates that the immune system’s components display temperature-dependent changes in activity. As it turns out, fever optimizes immune system function by:

  1. Increasing the flow of blood through the bloodstream because of the vasodilation (blood vessel expansion) associated with fever. This increased blood flow accelerates the movement of immune cells throughout the body, giving them more timely access to pathogens.
  2. Increasing binding of immune system proteins to immune cells, assisting their trafficking to lymph tissue.
  3. Increasing cellular activity, such as proliferation and differentiation of immune cells and phagocytosis.

blog__inline--new-insights-into-endothermy

Figure: The Human Immune System. Image credit: Shutterstock

Other studies indicate that some pathogens, such as fungi, lose virulence at higher temperatures, further accounting for elevated body temperatures and the importance of the fever response. Of course, if body temperature becomes too high, it will compromise immune system function, moving it away from the temperature optimum and leading to other complications. So, the fever response must be carefully regulated.

Here’s the key point: the metabolic costs of endothermy are justified because warm-bloodedness allows the immune systems of birds and mammals to be near enough to the temperature optimum that infectious agents can be quickly cleared from their bodies.

Fever Response in Ectotherms

Cold-blooded animals (ectotherms) also mount a fever response to infectious agents for the same reason as endotherms. However, the body temperature of ectotherms is set by their surroundings. This limitation means that ectotherms need to regulate their body temperature and mount the fever response through their behavior by moving into spaces with elevated temperatures. Doing so places them at the mercy of environmental changes. This condition means that cold-blooded creatures experience a significant time lag between the onset of infection and the fever response. It also means that, in some cases, ectotherms can’t elevate their body temperature to the immune system optimum if, for example, it is night or overcast.

Finally, in an attempt to elevate their body temperatures, ectotherms need to be out from under cover, making themselves vulnerable to predators. So, according to Logan’s model, endothermy offers some tangible advantages compared to ectothermy.

But endothermy comes at a cost. As mentioned, the metabolic cost of endothermy is extensive compared to ectothermy. Pathogen virulence marks another disadvantage. Logan points out that pathogens that infect cold-blooded animals are much less virulent than pathogens that infect warm-blooded creatures.

Endothermy and Ectothermy Trade-Offs

So, when it comes to regulation of animal body temperature, a set of trade-offs exists that include:

  • Metabolic costs
  • Immune system responsiveness and effectiveness
  • Pathogen virulence
  • Vulnerability to predators

These trade-offs can be managed by two viable strategies: endothermy and ectothermy. Each has advantages and disadvantages. And each is optimized in its own right.

Regulation of Body Temperature and the Case for a Creator

Logan seeks to account for the evolutionary origins of endothermy by appealing to the advantages it offers organisms battling pathogens. But, examining Logan’s scenario leaves one feeling as if the explanation is little more than an evolutionary just-so story.

When endothermy presented an enigma for biologists, it would have been hard to argue that it reflected the handiwork of a Creator, particularly in light of its large metabolic cost. But now that scientists understand the trade-offs in play and the optimization associated with the endothermic lifestyle, we can also interpret the optimization of endothermy and ectothermy as evidence for design.

From my vantage point, optimization signifies the handiwork of a Creator. As I discuss in The Cell’s Design, saying something is optimized is equivalent to saying it is well-designed. The optimization of an engineered system doesn’t just happen. Rather, such systems require forethought, planning, and careful attention to detail. In the same way, the optimized designs of biological systems like endothermy and ectothermy reasonably point to the work of a Creator.

And I am chill with that.

Resources

Endnotes
  1. Michael L. Logan, “Did Pathogens Facilitate the Rise of Endothermy?” Ideas in Ecology and Evolution 12 (June 4, 2019): 1–8, https://ojs.library.queensu.ca/index.php/IEE/article/view/13342.

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

Is SETI an Intelligent Design Research Program?

Untitled00003

By Fazale Rana – July 24, 2019

I have always felt at home on college and university campuses. Perhaps this is one reason I enjoy speaking at university venues. I also love any chance I get to interact with college students. They have inquisitive minds and they won’t hesitate to challenge ideas.

Skeptical Challenge

A few years ago I was invited to present a case for a Creator, using evidence from biochemistry, at Cal Poly San Luis Obispo. During the Q&A session, a skeptical student challenged my claims, insisting that intelligent design/creationism isn’t science. In leveling this charge, he was advocating scientism—the view that science is the only way to discover truth; in fact, science equates to truth. Thus, if something isn’t scientific, then it can’t be true. On this basis he rejected my claims.

You might be surprised by my response. I agreed with my questioner.

My case for a Creator based on the design of biochemical systems is not science. It is a philosophical and theological argument informed by scientific discovery. In other words, scientific discoveries have metaphysical implications. And, by identifying and articulating those implications, I built a case for God’s existence and role in the origin and design of life.

Having said this, I do think that design detection is legitimately part of the fabric of science. We can use scientific methodologies to detect the work of intelligent agency. That is, we can develop rigorous scientific evidence for intelligent design. I also think we can ascribe attributes to the intelligent designer from scientific evidence at hand.

In defense of this view, I (and others who are part of the Intelligent Design Movement, or IDM) have pointed out that there are branches of science that function as intelligent design programs, such as research in archaeology and the Search for Extraterrestrial Intelligence (SETI). We stand to learn much from these disciplines about the science of design detection. (For a detailed discussion, see the Resources section.)

SETI and Intelligent Design

Recently, I raised this point in a conversation with another skeptic. He challenged me on that point, noting that Seth Shostak, an astronomer from the SETI Institute, wrote a piece for Space.com repudiating the connection between intelligent design (ID) and SETI, arguing that they don’t equate.

 

00004

Figure: Seth Shostak. Image credit: Wikipedia

 

According to Shostak,

“They [intelligent design proponents] point to SETI and say, ‘upon receiving a complex radio signal from space, SETI researchers will claim it as proof that intelligent life resides in the neighborhood of a distant star. Thus, isn’t their search completely analogous to our own line of reasoning—a clear case of complexity implying intelligence and deliberate design?’ And SETI, they would note, enjoys widespread scientific acceptance.”1

Shostak goes on to say, “If we as SETI researchers admit this is so, it sounds as if we’re guilty of promoting a logical double standard. If the ID folks aren’t allowed to claim intelligent design when pointing to DNA, how can we hope to claim intelligent design on the basis of a complex radio signal?”2

In an attempt to distinguish the SETI Institute from the IDM, Shostak asserts that ID proponents make their case for intelligent design based on the complexity of biological and biochemical systems. But this is not what the SETI Institute does. According to Shostak, “The signals actually sought by today’s SETI searches are not complex, as the ID advocates assume. We’re not looking for intricately coded messages, mathematical series, or even the aliens’ version of ‘I Love Lucy.’”

Instead of employing complexity as an indicator of intelligent agency, SETI looks for signals that display the property of artificiality. What they mean by artificiality is that specifically, SETI is looking for a simple signal of narrow-band electromagnetic radiation that forms an endless sinusoidal pattern. According to SETI investigators, this type of signal does not occur naturally. Shostak also points out that the context of the signal is important. If the signal comes from a location in space that couldn’t conceivably harbor life, then SETI researchers would be less likely to conclude that it comes from an intelligent civilization. On the other hand, if the signal comes from a planetary system that appears life-friendly, this signal would be heralded as a successful detection event.

Artificiality and Intelligent Design

I agree with Shostak. Artificiality, not complexity, is the best indicator of intelligent design. And, it is also important to rule out natural process explanations. I can’t speak for all creationists and ID proponents, but the methodology I use to detect design in biological systems is precisely the same one the SETI Institute employs.

In my book The Cell’s Design, I propose the use of an ID pattern to detect design. Toward this end, I point out that objects, devices, and systems designed by human beings—intelligent designers—are characterized by certain properties that are distinct from objects and systems generated by natural processes. To put it in Shostak’s terms, human designs display artificiality. And we can use the ID pattern as a way to define what artificiality should look like.

Here are three ways I adopt this approach:

  1. In The Cell’s Design, I follow after natural theologian William Paley’s work. Paley described designs created by human beings as contrivances in which the concept of artificiality was embedded. I explain examples of such artificiality in biochemical systems.
  2. In Origins of Life (a work I coauthored with astronomer Hugh Ross) and Creating Life in the Lab, I point out that natural processes don’t seem to be able to account for the origin of life and, hence, the origin of biochemical systems.
  3. Finally, in Creating Life in the Lab, I show that attempts to create protocells starting with simple molecules and attempts to recapitulate the different stages in the origin-of-life pathway depend upon intelligent agency. This dependence further reinforces the artificiality displayed by biochemical systems.

Collectively, all three books present a comprehensive case for a Creator’s role in the origin and fundamental design of life, with each component of the overall case for design resting on the artificiality of biochemical systems. So, even though the SETI Institute may want to distance themselves from the IDM, SETI is an intelligent design program. And intelligent design is, indeed, part of the construct of science.

In other words, scientists from a creation model perspective can make a rigorous scientific case for the role of intelligent agency in the origin and design of biochemical systems, and even assign attributes to the designer. At that point, we can then draw metaphysical conclusions about who that designer might be.

Resources

Endnotes
  1. Seth Shostak, “SETI and Intelligent Design,” Space.com (December 1, 2005), https://www.space.com/1826-seti-intelligent-design.html.
  2. Shostak, “SETI and Intelligent Design.”

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

Does Old-Earth Creationism Make God Deceptive?

Untitled00002

By Fazale Rana – July 17, 2019

“Are [vestigial structures] unequivocal evidence of evolution?

No. Are they reasonable evidence of evolution? Yes.

Ditto gene sequences.

Appearance of evolution is no more a valid deflection [for the overwhelming evidence for evolution] than the appearance of age is a valid dodge of the overwhelming confluence of evidence of antiquity.

Both are sinking ships. I got off before going under with you on this one.”

—Hill R. (a former old-earth creationist who now espouses theistic evolution/evolutionary creationism)

Most people who follow my work at Reasons to Believe know I question the grand claim of the evolutionary paradigm; namely, that evolutionary processes provide the exclusive explanation for the origin, design, and history of life. In light of my skepticism, friends and foes alike often ask me how I deal with (what many people perceive to be) the compelling evidence for the evolutionary history of life, such as vestigial structures and shared genetic features in genomes.

As part of my response, I point out that this type of evidence for evolution can be accommodated by a creation model, with the shared features reflecting common design, not common descent—particularly now that we know that there is a biological rationale for many vestigial structures and shared genetic features. This response prompted my friend Hill R. to level his objection. In effect, Hill says I am committing the “appearance of evolution” fallacy, which he believes is analogous to the “appearance of age” fallacy committed by young-earth creationists (YECs).

Hill is not alone in his criticism. Other people who embrace theistic evolution/evolutionary creation (such as my friends at BioLogos) level a similar charge. According to these critics, both appearance of age and appearance of evolution fallacies make God deceptive.

If biological systems are designed, but God made them appear as if they evolved, then the conclusions we draw when we investigate nature are inherently untrustworthy. This is a problem because, according to Scripture, God reveals himself to us through the record of nature. But if we are misled by nature’s features and, consequently, draw the wrong conclusion, then it makes God deceptive. However, God cannot lie or deceive. It is contrary to his nature.

So, how do I respond to this theological objection to RTB’s creation model?

Before I reply, I want to offer a little more background information to make sure that anyone who is unfamiliar with this concern can better appreciate the seriousness of the charge against our creation model. If you don’t need the background explanation, then feel free to skip ahead to A Response to the Appearance of Evolution Challenge.

Evidence for Evolution: Vestigial Structures

Evolutionary biologists often point to vestigial structures—such as the pelvis and hind limbs of whales and dolphins (cetaceans)—as compelling evidence for biological evolution. Evolutionary biologists view vestigial structures this way because they are also homologous (structurally similar) structures. Vestigial structures are rudimentary body parts that are smaller and simpler than the corresponding features possessed by the other members of a biological group. As a case in point, the whale pelvis and hind limbs are homologous to the pelvis and hind limbs of all other mammals.

blog__inline--does-old-earth-creationism-make-god-deceptive-1

Figure 1: Whale Pelvis. Image credit: Shutterstock

Evolutionary biologists believe that vestigial structures were fully functional at one time but degenerated over the course of many generations because the organisms no longer needed them to survive in an ever-changing environment—for example, when the whale ancestor transitioned from land to water. From an evolutionary standpoint, fully functional versions of these structures existed in the ancestral species. The structures’ form and function may be retained (possibly modified) in some of the evolutionary lineages derived from the ancestral species, but if no longer required, the structures become diminished (and even lost) in other lineages.

Evidence for Evolution: Shared Genetic Features

Evolutionary biologists also consider shared genetic features found in organisms that naturally group together as compelling evidence for common descent. One feature of particular interest is the identical (or nearly identical) DNA sequence patterns found in genomes. According to this line of reasoning, the shared patterns arose as a result of a series of substitution mutations that occurred in the common ancestor’s genome. Presumably, as the varying evolutionary lineages diverged from the nexus point, they carried with them the altered sequences created by the primordial mutations.

Synonymous mutations play a significant role in this particular argument for common descent. Because synonymous mutations don’t alter the amino acid sequence of proteins, their effects are considered to be inconsequential. (In a sense, they are analogous to vestigial anatomical features.) So, when the same (or nearly the same) patterns of synonymous mutations are observed in genomes of organisms that cluster together into the same group, most life scientists interpret them as compelling evidence of the organisms’ common evolutionary history.

A Response to the Evidence for Evolution

As a rejoinder to this evidence, I point out that we continue to uncover evidence that vestigial structures display function (see Vestigial Structures are Functional in the Resources section.) Likewise, evidence is beginning to accumulate that synonymous mutations have functional consequences. (see Shared Genetic Features Reflect Design in the Resources section.) Again, if these features have functional utility, then they can reasonably be interpreted as the Creator’s handiwork.

But, even though these biological features bear function, many critics of the RTB model think that the shared features of these biological systems still bear the hallmarks of an evolutionary history. Therefore, they argue that these features look as if they evolved. And if so, we are guilty of the “appearance of evolution” fallacy.

Appearance of Age and the Appearance of Evolution

In 1857, Philip Gosse, a biologist and preacher from England, sought to reconcile the emerging evidence for Earth’s antiquity with Scripture. Gosse was convinced that the earth was old. He was also convinced that Scripture taught that the earth was young. In an attempt to harmonize these disparate stances, he proposed the appearance of age argument in a book titled Omphalos. In this work, Gosse argued that God created Earth in six days, but made it with the appearance of age.

blog__inline--does-old-earth-creationism-make-god-deceptive-2

Figure 2: Philip Henry Gosse, 1855. Image credit: Wikipedia

This idea persists today, finding its way into responses modern-day YECs make to the scientific evidence for Earth’s and life’s antiquity. For many people (including me), the appearance of age argument is fraught with theological problems, the chief one being that it makes God deceptive. If Earth appears to be old, and it measures to be old, yet it is young, then we can’t trust anything we learn when we study nature. This problem is not merely epistemological; it is theological because nature is one way that God has chosen to make himself known to us. But if our investigation of nature is unreliable, then it means that God is untrustworthy.

In other words, on the surface, both the appearance of age and the appearance of evolution arguments made by YECs and old-earth creationists (OECs), respectively, seem to be equally problematic.

But does the RTB position actually commit the appearance of evolution fallacy? Does it suffer from the same theological problems as the argument first presented by Gosse in Omphalos? Are we being hypocritical when we criticize the appearance of age fallacy, only to commit the appearance of evolution fallacy?

A Response to the Appearance of Evolution Challenge

This charge against the RTB creation model neglects to fully represent the reasons I question the evolutionary paradigm.

First, my skepticism is not theologically motivated but scientifically informed. For example, I point out in an article I recently wrote for Sapientia that a survey of the scientific literature makes it clear that evolutionary theory as currently formulated cannot account for the key transitions in life’s history, including:

  • the origin of life
  • the origin of eukaryotic cells
  • the origin of body plans
  • the origin of human exceptionalism

Additionally, some predictions that flow out of the evolutionary paradigm have failed (such as the widespread prevalence of convergence), further justifying my skepticism. (See Scientific Challenges to the Evolutionary Paradigm in the Resources section.)

In other words, when we interpret shared features as a manifestation of common design (including vestigial structures and shared genetic patterns), it is in the context of scientifically demonstrable limitations of the evolutionary framework to fully account for life’s origin, history, and design. To put it differently, because of the shortcomings of evolutionary theory, we don’t see biological systems as having evolved. Rather, we think they’ve been designed.

Appearance of Design Fallacy

Even biologists who are outspoken atheists readily admit that biological and biochemical systems appear to be designed. Why else would Nobel Laureate Francis Crick offer this word of caution to scientists studying biochemical systems: “Biologists must keep in mind that what they see was not designed, but rather evolved.”1 What other reason would evolutionary biologist Richard Dawkins offer for defining biology as “the study of complicated things that give the appearance of having been designed for a purpose”?2

Biologists can’t escape the use of design language when they describe the architecture and operation of biological systems. In and of itself, this practice highlights the fact that biological systems appear to be designed, not evolved.

To sidestep the inexorable theological implications that arise when biologists use design language, biologist Colin Pittendrigh coined the term teleonomy in 1958 to describe systems that appear to be purposeful and goal-directed, but aren’t. In contrast with teleology—which interprets purposefulness and goal-directedness as emanating from a Mind— teleonomy views design as the outworking of evolutionary processes. In other words, teleonomy allows biologists to utilize design language— when they describe biological systems—without even a tinge of guilt.

In fact, the teleonomic interpretation of biological design resides at the heart of the Darwinian revolution. Charles Darwin claimed that natural selection could account for the design of biological systems. In doing so, he supplanted Mind with mechanism. He replaced teleology with teleonomy.

Prior to Darwin, biology found its grounding in teleology. In fact, Sir Richard Owen—one of England’s premier biologists in the early 1800s—produced a sophisticated theoretical framework to account for shared biological features found in organisms that naturally cluster together (homologous structures). For Owen (and many biologists of his time) homologous structures were physical manifestations of an archetypal design that existed in the Creator’s mind.

Thus, shared biological features—whether anatomical, physiological, biochemical, or genetic—can be properly viewed as evidence for common design, not common descent. In fact, when Darwin proposed his theory of evolution, he appropriated Owen’s concept of the archetype but then replaced it with a hypothetical common ancestor.

Interestingly, Owen (and other like-minded biologists) found an explanation for vestigial structures like the pelvis and hind limb bones (found in whales and snakes) in the concept of the archetype. They regarded these structures as necessary to the architectural design of the organism. In short, a model that interprets shared biological characteristics from a design/creation model framework has historical precedence and is based on the obvious design displayed by biological systems.

Given the historical precedence for interpreting the appearance of design in biology as bona fide design and the inescapable use of design language by biologists, it seems to me that RTB’s critics commit the appearance of design fallacy when they (along with other biologists) claim that things in biology look designed, but they actually evolved.

Theories Are Underdetermined by Data

A final point. One of the frustrating aspects of scientific discovery relates to what’s called the underdetermination thesis.3 Namely, two competing theories can explain the same set of data. According to this idea, theories are underdetermined by data. This limitation means that two or more theories—that may be radically different from one another—can equally account for the same data. Or, to put it another way, the methodology of science never leads to one unique theory. Because of this shortcoming, other factors—nonscientific ones—influence the acceptance or rejection of a scientific theory, such as a commitment to mechanistic explanations to explain all of biology.

As a consequence of the underdetermination theory, evolutionary models don’t have the market cornered when it comes to offering an interpretation of biological data. Creation models, such as the RTB model—which relies on the concept of common design—also makes sense of the biological data. And given the inability of current evolutionary theory to explain key transitions in life’s history, maybe a creation model approach is the better alternative.

In other words, when we interpret vestigial structures and shared genetic features from a creation model perspective, we are not committing an appearance of age type of fallacy, nor are we making God deceptive. Instead, we are offering a common sense and scientifically robust interpretation of the elegant designs so prevalent throughout the living realm.

Far from a sinking ship one should abandon, a creation model offers a lifeline to scientific and biblical integrity.

Resources

Vestigial Structures Are Functional

Shared Genetic Features Reflect Design

Scientific Challenges for the Evolutionary Paradigm

Archetype Biology

Endnotes
  1. Francis Crick, What Mad Pursuit: A Personal View of Scientific Discovery (New York: Basic Books, 1988), 138.
  2. Richard Dawkins, The Blind Watchmaker: Why the Evidence for Evolution Reveals a Universe without Design (New York: W. W. Norton, 1996), 4.
  3. Val Dusek, Philosophy of Technology: An Introduction (Malden, MA: Blackwell Publishing, 2006), 12.

Reprinted with permission by the author

Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2018/11/21/vocal-signals-smile-on-the-case-for-human-exceptionalism

Biochemical Grammar Communicates the Case for Creation

Untitled 19
BY FAZALE RANA – MAY 29, 2019

As I get older, I find myself forgetting things—a lot. But, thanks to smartphone technology, I have learned how to manage my forgetfulness by using the “Notes” app on my iPhone.

blog__inline--biochemical-grammar-communicates-1

Figure 1: The Apple Notes app icon. Image credit: Wikipedia

This app makes it easy for me to:

  • Jot down ideas that suddenly come to me
  • List books I want to read and websites I want to visit
  • Make note of musical artists I want to check out
  • Record “to do” and grocery lists
  • Write down details I need to have at my fingertips when I travel
  • List new scientific discoveries with implications for the RTB creation model that I want to blog about, such as the recent discovery of a protein grammar calling attention to the elegant design of biochemical systems

And the list goes on. I will never forget, again!

On top of that, I can use the Notes app to categorize and organize all my notes and house them in a single location. Thus, I don’t have to manage scraps of paper that invariably wind up getting scattered all over the place—and often lost.

And, as a bonus, the Notes app anticipates the next word I am going to use even before I type it. I find myself relying on this feature more and more. It is much easier to select a word than type it out. In fact, the more I use this feature, the better the app becomes at anticipating the next word I want to type.

Recently, a team of bioinformaticists from the University of Alabama, Birmingham (UAB) and the National Institutes of Health (NIH) used the same algorithm the Notes app uses to anticipate word usage to study protein architectures.1 Their analysis reveals new insight into the structural features of proteins and also highlights the analogy between the information housed in these biomolecules and human language. This analogy contributes to the revitalized Watchmaker argument presented in my book The Cell’s Design.

N-Gram Language Modeling

The algorithm used by the Notes app to anticipate the next word the user will likely type is called n-gram language modeling. This algorithm determines the probability of a word being used based on the previous word (or words) typed. (If the probability is based on a single word, it is called a unigram probability. If the calculation is based on the previous two words, it is called a bigram probability, and so on.) This algorithm “trains” the Notes app so that the more I use it, the more reliable the calculated probabilities—and, hence, the better the word recommendations.

N-Gram Language Modeling and the Case for a Creator

To understand why the work of research team from UAB and NIH provides evidence for a Creator’s role in the origin and design of life, a brief review of protein structure is in order.

Protein Structure

Proteins are large complex molecules that play a key role in virtually all of the cell’s operations. Biochemists have long known that the three-dimensional structure of a protein dictates its function.

Because proteins are such large complex molecules, biochemists categorize protein structure into four different levels: primary, secondary, tertiary, and quaternary structures. A protein’s primary structure is the linear sequence of amino acids that make up each of its polypeptide chains.

The secondary structure refers to short-range three-dimensional arrangements of the polypeptide chain’s backbone arising from the interactions between chemical groups that make up its backbone. Three of the most common secondary structures are the random coil, alpha (α) helix, and beta (β) pleated sheet.

Tertiary structure describes the overall shape of the entire polypeptide chain and the location of each of its atoms in three-dimensional space. The structure and spatial orientation of the chemical groups that extend from the protein backbone are also part of the tertiary structure.

Quaternary structure arises when several individual polypeptide chains interact to form a functional protein complex.

 

blog__inline--biochemical-grammar-communicates-2

Figure 2: The four levels of protein structure. Image credit: Shutterstock

Protein Domains

Within the tertiary structure of proteins, biochemists have discovered compact, self-contained regions that fold independently. These three-dimensional regions of the protein’s structure are called domains. Some proteins consist of a single compact domain, but many proteins possess several domains. In effect, domains can be thought to be the fundamental units of a protein’s tertiary structure. Each domain possesses a unique biochemical function. Biochemists refer to the spatial arrangement of domains as a protein’s domain architecture.

Researchers have discovered several thousand distinct protein domains. Many of these domains recur in different proteins, with each protein’s tertiary structure comprised of a mix-and-match combination of protein domains. Biochemists have also learned that a relationship exists between the complexity of an organism and the number of unique domains found in its set of proteins and the number of multi-domain proteins encoded by its genome.

blog__inline--biochemical-grammar-communicates-3

Figure 3: Pyruvate kinase, an example of a protein with three domains. Image credit: Wikipedia

The Key Question in Protein Chemistry

As much progress as biochemists have made characterizing protein structure over the last several decades, they still lack a fundamental understanding of the relationship between primary structure (the amino acid sequence) and tertiary structure and, hence, protein function. In order to develop this insight, they need to determine the “rules” that dictate the way proteins fold. Treating proteins as information systems can help determine some of these rules.

Protein as Information Systems

Proteins are not only large, complex molecules but also information-harboring systems. The amino acid sequence that defines a protein’s primary structure is a type of information—biochemical information—with the individual amino acids analogous to the letters that make up an alphabet.

N-Gram Analysis of Proteins

To gain insight into the relationship between a protein’s primary structure and its tertiary structures, the researchers from UAB and NIH carried out an n-gram analysis on the 23 million protein domains found in the protein sets of 4,800 species found across all three domains of life.

These researchers point out that an individual amino acid in a protein’s primary structure doesn’t contain information just as an individual letter in an alphabet doesn’t harbor any meaning. In human language, the most basic unit that conveys meaning is a word. And, in proteins, the most basic unit that conveys biochemical meaning is a domain.

To decipher the “grammar” used by proteins, the researchers treated adjacent pairs of protein domains in the tertiary structure of each protein in the sample set as a bigram (similar to two words together). Surveying the proteins found in their data set of 4,800 species, they discovered that 95% of all the possible domain combinations don’t exist!

This finding is key. It indicates that there are, indeed, rules that dictate the way domains interact. In other words, just like certain word combinations never occur in human languages because of the rules of grammar, there appears to be a protein “grammar” that constrains the domain combinations in proteins. This insight implies that physicochemical constraints (which define protein grammar) dictate a protein’s tertiary structure, preventing 95% of conceivable domain-domain interactions.

Entropy of Protein Grammar

In thermodynamics, entropy is often used as a measure of the disorder of a system. Information theorists borrow the concept of entropy and use it to measure the information content of a system. For information theorists, the entropy of a system is indirectly proportional to the amount of information contained in a sequence of symbols. As the information content increases, the entropy of the sequence decreases, and vice versa. Using this concept, the UAB and NIH researchers calculated the entropy of the protein domain combinations.

In human language, the entropy increases as the vocabulary increases. This makes sense because, as the number of words increases in a language, the likelihood that random word combinations would harbor meaning decreases. In like manner, the research team discovered that the entropy of the protein grammar increases as the number of domains increases. (This increase in entropy likely reflects the physicochemical constraints—the protein grammar, if you will—on domain interactions.)

Human languages all carry the same amount of information. That is to say, they all display the same entropy content. Information theorists interpret this observation as an indication that a universal grammar undergirds all human languages. It is intriguing that the researchers discovered that the protein “languages” across prokaryotes and eukaryotes all display the same level of entropy and, consequently, the same information content. This relationship holds despite the diversity and differences in complexity of the organism in their data set. By analogy, this finding indicates that a universal grammar exists for proteins. Or to put it another way, the same set of physicochemical constraints dictate the way protein domains interact for all organisms.

At this point, the researchers don’t know what the grammatical rules are for proteins, but knowing that they exist paves the way for future studies. It also generates hope that one day biochemists might understand them and, in turn, use them to predict protein structure from amino acid sequences.

This study also illustrates how fruitful it can be to treat biochemical systems as information systems. The researchers conclude that “The similarities between natural languages and genomes are apparent when domains are treated as functional analogs of words in natural languages.”2

In my view, it is this relationship that points to a Creator’s role in the origin and design of life.

Protein Grammar and the Case for a Creator

As discussed in The Cell’s Design, the recognition that biochemical systems are information-based systems has interesting philosophical ramifications. Common, everyday experience teaches that information derives solely from the activity of human beings. So, by analogy, biochemical information systems, too, should come from a divine Mind. Or at least it is rational to hold that view.

But the case for a Creator strengthens when we recognize that it’s not merely the presence of information in biomolecules that contributes to this version of a revitalized Watchmaker analogy. Added vigor comes from the UAB and NIH researchers’ discovery that the mathematical structure of human languages and biochemical languages is identical.

Skeptics often dismiss the updated Watchmaker argument by arguing that biochemical information is not genuine information. Instead, they maintain that when scientists refer to biomolecules as harboring information, they are employing an illustrative analogy—a scientific metaphor—and nothing more. They accuse creationists and intelligent design proponents of misconstruing their use of analogical language to make the case for design.3

But the UAB and NIH scientists’ work questions the validity of this objection. Biochemical information has all of the properties of human language. It really is information, just like the information we conceive and use to communicate.

Is There a Biochemical Anthropic Principle?

This discovery also yields another interesting philosophical implication. It lends support to the existence of a biochemical anthropic principle. Discovery of a protein grammar means that there are physicochemical constraints on protein structure. It is remarkable to think that protein tertiary structures may be fundamentally dictated by the laws of nature, instead of being the outworking of an historically contingent evolutionary history. To put it differently, the discovery of a protein grammar reveals that the structure of biological systems may reflect some deep, underlying principles that arise from the very nature of the universe itself. And yet these structures are precisely the types of structures life needs to exist.

I interpret this “coincidence” as evidence that our universe has been designed for a purpose. And as a Christian, I find that notion to resonate powerfully with the idea that life manifests from an intelligent Agent—namely, God.

Resources to Dig Deeper

Endnotes
  1. Lijia Yu et al., “Grammar of Protein Domain Architectures,” Proceedings of the National Academy of Sciences, USA 116, no. 9 (February 26, 2019): 3636–45, doi:10.1073/pnas.1814684116.
  2. Yu et al., 3636–45.
  3. For example, see Massimo Pigliucci and Maarten Boudry, “Why Machine-Information Metaphors Are Bad for Science and Science Education,” Science and Education 20, no. 5–6 (May 2011): 453–71; doi:10.1007/s11191-010-9267-6.

Reprinted with permission by the author
Original article at:
https://www.reasons.org/explore/blogs/the-cells-design/read/the-cells-design/2019/05/29/biochemical-grammar-communicates-the-case-for-creation