Today’s New Reason To Believe

Keith McPherson

Keith McPherson received his Master of Science in Electrical Engineering from Georgia Institute of Technology in 1993, and currently works as an electrical engineer in Melbourne, FL, in the fields of communications and signal processing.

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In part 1 of this series we learned how the genetic system is an information-processing system, and outlined several reasons why we could expect to find coding techniques in play to protect the genetic data. Such coding techniques are known and used by engineers to protect the data processed by many modern digital communications systems.

We now turn our attention to a few analogies of such coding techniques.

Analogy: Optimality of the Genetic Code and Gray Mapping

The first analogy will show from a qualitative and quantitative perspective that the genetic code is in fact an optimal (or near optimal) mapping from codons to amino acids. (See here for a table describing the genetic code.)

The genetic code seems optimized to the specific nucleotide error probabilities quite well, as is the case for a good code from an engineering perspective. For example, the first and third nucleotides of a codon (see here) are more likely to be misread during translation, and this error appears to be taken into account in the genetic code mapping. These most common errors, or mutations, translate the desired codon into a codon that codes either for the same amino acid, or for an amino acid that has very similar physicochemical properties, thus minimizing function loss. This is similar to Gray codes used in digital communications.

More specifically, the genetic code seems to be specifically designed to code for the same or very similar amino acids for the most common types of substitution mutations (errors), thereby minimizing protein function loss. In like fashion, Gray codes used in engineering are specifically designed to code for the most similar bit patterns for the most common types of symbol errors, thereby minimizing information loss.

I noticed the similarity to Gray coding after reading a paper by Dr. Fazale Rana in 2002. The Gray code interpretation was highlighted by Manish Gupta in a paper published in 2006. Gupta plotted the 64 codons used in the genetic code in terms of nucleotide distance (see Figure 3 here), and remarked on the correspondence to Gray codes used in engineering. The concept of nucleotide distance and the illustrated plot establishes the validity of the Gray map interpretation.

Recall from part 1 that many genetic code mappings are possible due to the high level of redundancy. Therefore, from a qualitative perspective, and from an engineering perspective, the genetic code is superb, perhaps much better than one may expect from a naturalistic perspective.

Recent work shows just how remarkable the natural code is. (See here and here.) Researchers studying the error-minimizing properties of the genetic code noticed that prior work concluded that the natural code ranked in the top 0.02 percent for efficiency, but that the prior work overlooked bias in mutations.¹ When this bias is taken into account, the natural code makes a radical leap forward from the top 0.02 percent to literally one in a million.

Dr. Fazale Rana comments further on the error-minimizing properties of the genetic code:

The genetic code’s error-minimization properties are actually more dramatic than these results indicate. When researchers calculated the error-minimization capacity of one million randomly generated genetic codes, they discovered that the error-minimization values formed a distribution where the naturally occurring genetic code’s capacity occurred outside the distribution. Researchers estimate the existence of 10¹⁸ possible genetic codes possessing the same type and degree of redundancy as the universal genetic code. All of these codes fall within the error-minimization distribution. This finding means that of 10¹⁸ possible genetic codes, few, if any, have an error-minimization capacity that approaches the code found universally in nature. ²

In summary, qualitative and quantitative evidence suggests that the natural genetic code is highly optimized and, in fact, tuned to the most common type of errors (mutations). In addition, this work highlights an underlying analogy between the genetic system and modern communications systems—the so-called Gray code.

The next article in this series will look at another coding analogy between modern digital communications systems and the genetic information-processing system.

Notes/References:

1. Bias includes the fact that not all codons are equally mistranslated to other codons, and that certain nucleotide positions within the codon are more prone to error. Purine/purine and pyrimidine/pyrimidine errors (transition mutations) are more common than purine/pyrimidine errors (transversion mutations), and the ranking of the positions is 3rd, 1st, and 2nd in terms of being more error prone.

2. Fazale Rana, “FYI: I.D. in DNA; Deciphering Design in the Genetic Code,” Facts for Faith, Quarter 1, 2002, 14-23.

By Katie Galloway
[Katie Galloway is an RTB volunteer apologist. She is completing her PhD at Caltech in Chemical Engineering with an emphasis in biological systems.]

Ancient Armored Fish May Hold Clues to Lighter, Stronger Bioinspired Armor

If you’ve seen The Dark Knight, you know that early on Batman has an epiphany about his armor. Following a battle with thugs and Rottweilers, he notes, “I need to be lighter, quicker.” But even equipped with the best tech that Wayne Enterprises has to offer, Batman will have to sacrifice a degree of protection to be more mobile. Mobility and protection are often inherent trade-offs in designing armor, so when researchers encountered ancient armored fish that appear to be designed for both, they work to discover their secrets.

Polypterus senegalus (falsely labeled “dinosaur eels”) is an ancient armored fish that grows to about a foot long and lives in shallow, muddy, freshwater lakes in Africa. The skeleton of this 96-million-year-old Cretaceous predator is lined with four-layered interlocking scales that protect it from biting attacks. Recently, a group from MIT set out to discover the structural strategies of these scales that provide protection for the fish. (See their results here.)

From the inside out, the four-layered scales increase in hardness and decrease in thickness. The top layer of ganoine (crystallized apatite) is only 10 micrometers (μm) thick yet it’s harder than iron. The second layer, dentine, is half as hard as iron, more elastic, and five times as thick. By layering these two materials on top of a thick base of more flexible materials (40 μm of isopedine and 300 μm of basal bone plate), the scale, as a whole, is able to prevent and minimize damage from attacks. But why not just make scales entirely out of the hardest material?

The MIT researchers developed a model to test the difference between an all-ganoine scale and the multilayered scale.¹ While the all-ganoine scale prevents deeper penetration, it is more vulnerable to catastrophic failure caused by radial cracks. On the other hand, the flexible material in the layered scale does a better job of dissipating energy introduced by a bite, thus preventing radial cracks. At low forces, the outer hard layer resists penetration by deflecting objects. Under greater pressure, the energy from the object pushes beyond the top layer into the softer layers, which distribute the force over a larger area, reducing the overall pressure at the point of contact. (To see this principle in action, press lightly on your fingernail with a pen. The nail easily resists the pen. Push harder and notice that the pressure is distributed across the nail. This is one reason why it’s more common to bruise a nail than to puncture or split one—something to be thankful for when you accidentally smash a finger or a toe!)

By having a scale that is able to resist and absorb bites, the armored fish limits damage and prevents the spread of radial cracks. Additionally, reducing the ganoine content in the scale decreases the weight and stiffness, providing the fish with excellent protection and greater mobility.

Nature continues to reveal amazing designs that inspire us to create new technologies. The knowledge and foresight discovered in natural designs underscores the notion of intelligence behind creation and strengthens the case for a Creator. Bruce Wayne would be wise to learn from this battle-tested fish.

1. Benjamin J. F. Bruet et al., “Materials design principles of ancient fish armour,” Nature Materials 7, no. 9 (2008): 748 - 56.

Keith McPherson

Bio: Keith McPherson received his Master of Science in Electrical Engineering from Georgia Institute of Technology in 1993, and currently works as an electrical engineer in Melbourne, FL, in the fields of communications and signal processing.

Calcium levels of pregnant cows in Iowa may not be the first image that comes to mind when Christian apologists invoke supernatural design, but that appears to be the case based on a recent feedback control study. As an electrical engineer, I can appreciate the level of design and fine-tuning required in making such systems work properly. The next several paragraphs examine and compare (in some technical detail) human-designed control systems with natural ones found in cows.

In engineering, feedback control is a common method used to maintain specified levels of important system outputs and quantities in the face of a variety of disturbances. A very typical design approach used to accomplish this is a 2nd order control system using a Proportional plus Integral (PI) controller. In this scheme, the overall system, the PI controller, and the two associated controller constants¹ are very carefully designed to produce the desired transient system response. Such a response seeks an appropriate trade-off between the settling time, overshoot, and oscillatory behavior, to name a few characteristics of interest. An optimally designed control system will ensure that the system output tracks to the desired level after a disturbance within a reasonable time and with a reasonable transient characteristic.

(See here for a schematic diagram of a typical feedback control system.)

Apparently, God is an engineer, and a very good one. The very same scheme of feedback control as described above and the network topology reflected in the diagram has been found in mammals for calcium homeostasis, which is the regulation of plasma calcium concentration (calcium concentration in the blood). (See hereand here for the researchers’ reports.)

In the biological world, calcium homeostasis is necessary for the survival of mammals. The plasma calcium concentration needs to be maintained very tightly in mammals² in spite of various disturbances related to diet or the calcium demand to meet milk production and fetal growth needs. Researchers studied the transient response of calcium concentration of a total of 38 dairy cows during a 10-day period surrounding the process of calving. They concluded that the cows were able to maintain a life-essential level of calcium in the blood only with the aid of the functional equivalent of a 2nd order PI feedback control system. As their studies show, such a control system can be realistically implemented biologically using two hormones. This biological control system is a strict and rigorous analogy to 2nd order PI control systems widely used in engineering applications.

For one engineering example, I have recently completed the design and characterization of a 2nd order PI control system for a communications application. The two controller constants were judiciously calculated, tested, and the final set chosen to produce the best transient response, thereby optimizing the overall system performance. A scaled response of this control system was compared with the calcium concentration experimental data ³ and was found to match very closely. ⁴ This suggests to me that in addition to the design implied by having the correct network topology, the two effective constants for the biological controller have likewise been judiciously “chosen” to produce the best transient response and, thereby, provide optimal results for the mammals subject to stringent calcium concentration regulatory demands.

This analogy between manmade and natural control systems implies stringent design at various levels, irreducible complexity, and fine-tuning. The researchers seem to recognize the level of complexity involved.

Yet, the most important implication of integral feedback does not lie in producing a simple dynamical model that agrees well with the actual data. Rather, it lies in the severe structural constraints that it imposes in the underlying homeostatic mechanism.

The “severe structural constraints” associated with the homeostatic mechanism, coupled with the seemingly optimal controller constants, display a system that has been designed and fine-tuned.

The researchers also reference the results of other research in conjunction with their own.

These results as well as those reported in this article seem to point to the prevalence of integral control in mechanisms where physiological quantities must be maintained with a narrow range despite internal and external disturbances.

Finally, they suggest that “[f]urther work is needed to catalog and uncover the architecture of these systems where integral control is at work.”

This analogy specifically, and feedback control in biological systems in general, reinvigorates William Paley’s famous Watchmaker argument. The biological feedback control system discussed in this article is the direct counterpart to feedback control systems used in engineering, systems that without question are recognized to require design and exquisite fine-tuning by intelligent agents. Consistent logic suggests that a divine Intelligent Agent is responsible for similar systems found in biology.

Such purposeful design and fine-tuning fits nicely with Christian theism and with the Reasons To Believe creation model.

Notes/References:

1. For a typical 2nd order PI control system, the designer has two constants to specify as part of the system design: the proportional constant (Kp), and the integral constant (Ki).

2. 0.085-0.105 g/l in humans and 0.08-0.1 g/l in dairy cows.

3. See Figure 2.b for the biological transient response.

4. In addition to the characteristic shape of the two responses, the damping ratio was investigated. The so-called damping ratio is a parameter used to design and characterize control systems in engineering. The mentioned engineering control system was designed for a damping ratio of exactly 1.0. Analysis and side by side comparison of the two transient responses suggest that the damping ratio of the calcium homeostasis PI control system is slightly larger than 1.0, but probably no larger than 1.1. Damping ratios on this order are known to yield good system results in many engineering contexts. In engineering applications, the damping ratio typically lies between 0.5 and 2. A system with a damping ratio of 1.0 is referred to as a critically damped system. These systems converge faster than any other without oscillating. See here for more information on damping.