# New Insights into Genetic Code Optimization Signal Creator’s Handiwork

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.

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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 codea 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.

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##### 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.”