Frequently Occurring Fallacies No. 1: The Fallacy of 'One True Sequence'

A number of fallacies are in circulation amongs the enthusiasts for reality denial, and one that I wish to highlight here is known in scientific circles as "The Error of the One True Sequence". This fallacy asserts that one, and ONLY one, DNA sequence can code for a protein that performs a specific task. This is usually erected alonside assorted bogus "probability" calculations that purport to demonstrate that evolutionary processes cannot achieve what they plainly do achieve in the real world, but those other probability fallacies will be the subject of other posts. Here I want to destroy the myth that one, and ONLY one, sequence can ever work in a given situation.

Insulin provides an excellent example for my purposes, because insulin is critical to the health and well being of just about every vertebrate organism on the planet. When a vertebrate organism is unable to produce insulin, the well-known condition of diabetes mellitus, then the ability to regulate blood sugar is seriously disrupted, and in the case of Type 1 diabetes mellitus, in which the beta-cells of the Islets of Langerhans in the pancreas are destroyed by an autoimmune reaction, the result is likely to be fatal in the medium to long term due to diabetic nephropathy resulting in renal failure.

Consequently, the insulin molecule is critical to healthy functioning of vertebrate animals. The gene that codes for insulin is well known, and has been mapped in a multiplicity of organisms, including organisms whose entire genomes have been sequenced, ranging from the pufferfish Tetraodon nigroviridis through to Homo sapiens. There is demonstrable variability in insulin molecules (and the genes coding for them) across the entire panoply of vertebrate taxa. Bovine insulin, for example, is not identical to human insulin. I refer everyone to the following gene sequences, all of which have been obtained from publicly searchable online gene databases:

[1] Human insulin gene on Chromosome 11, which is as follows:

atg gcc ctg tgg atg cgc ctc ctg ccc ctg ctg gcg ctg ctg gcc ctc tgg gga cct gac
cca gcc gca gcc ttt gtg aac caa cac ctg tgc ggc tca cac ctg gtg gaa gct ctc tac
cta gtg tgc ggg gaa cga ggc ttc ttc tac aca ccc aag acc cgc cgg gag gca gag gac
ctg cag gtg ggg cag gtg gag ctg ggc ggg ggc cct ggt gca ggc agc ctg cag ccc ttg
gcc ctg gag ggg tcc ctg cag aag cgt ggc att gtg gaa caa tgc tgt acc agc atc tgc
tcc ctc tac cag ctg gag aac tac tgc aac tag

which codes for the following protein sequence (using the standard single letter mnemonics for individual amino acids, which I have colour coded to match the colour coding in this diagram of the insulin synthesis pathway in humans):

MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKT
RREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKR
GIVEQCCTSICSLYQLENYCN

Now, I refer everyone to this data, which is the coding sequence for insulin in the Lowland Gorilla (differences are highlighted in boldface):

atg gcc ctg tgg atg cgc ctc ctg ccc ctg ctg gcg ctg ctg gcc ctc tgg gga cct gac
cca gcc gcg gcc ttt gtg aac caa cac ctg tgc ggc tcc cac ctg gtg gaa gct ctc tac
cta gtg tgc ggg gaa cga ggc ttc ttc tac aca ccc aag acc cgc cgg gag gca gag gac
ctg cag gtg ggg cag gtg gag ctg ggc ggg ggc cct ggt gca ggc agc ctg cag ccc ttg
gcc ctg gag ggg tcc ctg cag aag cgt ggc atc gtg gaa cag tgc tgt acc agc atc tgc
tcc ctc tac cag ctg gag aac tac tgc aac tag

this codes for the protein sequence:

MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKT
RREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKR
GIVEQCCTSICSLYQLENYCN

which so happens to be the same precursor protein. However, Gorillas are closely related to humans. Let's move a little further away, to the domestic cow, Bos taurus (whose sequence is found here):

atg gcc ctg tgg aca cgc ctg cgg ccc ctg ctg gcc ctg ctg gcg ctc tgg ccc ccc ccc
ccg gcc cgc gcc ttc gtc aac cag cat ctg tgt ggc tcc cac ctg gtg gag gcg ctg tac
ctg gtg tgc gga gag cgc ggc ttc ttc tac acg ccc aag gcc cgc cgg gag gtg gag ggc
ccg cag gtg ggg gcg ctg gag ctg gcc gga ggc ccg ggc gcg ggc ggc ctg gag ggg ccc
ccg cag aag cgt ggc atc gtg gag cag tgc tgt gcc agc gtc tgc tcg ctc tac cag ctg
gag aac tac tgt aac tag

Already this is a smaller sequence - 318 codons instead of 333 - so we KNOW we're going to get a different insulin molecule with this species ... which is as follows:

MALWTRLRPLLALLALWPPPPARAFVNQHLCGSHLVEALYLVCGERGFFYTPK
ARREVEGPQVGALELAGGPGAGGLEGPPQKRGIVE
QCCASVCSLYQLENYCN

clearly a different protein, but one which still functions as an insulin precursor and results in a mature insulin molecule in cows, one which differs in exact sequence from that in humans. Indeed, prior to the advent of transgenic bacteria, into which human insulin genes had been transplanted for the purpose of harnessing those bacteria to produce human insulin for medical use, bovine insulin harvested from the pancreases of slaughtered beef cows was used to treat diabetes mellitus in humans. Now, of course, with the advent of transgenically manufactured true human insulin, from a sterile source, bovine insulin is no longer needed, much to the relief of those who are aware of the risk from BSE.

Moving on again, we have a different coding sequence from the tropical Zebrafish, Danio rerio, (sequence to be found here) which is as follows:

atg gca gtg tgg ctt cag gct ggt gct ctg ttg gtc ctg ttg gtc gtg tcc agt gta agc
act aac cca ggc aca ccg cag cac ctg tgt gga tct cat ctg gtc gat gcc ctt tat ctg
gtc tgt ggc cca aca ggc ttc ttc tac aac ccc aag aga gac gtt gag ccc ctt ctg ggt
ttc ctt cct cct aaa tct gcc cag gaa act gag gtg gct gac ttt gca ttt aaa gat cat
gcc gag ctg ata agg aag aga ggc att gta gag cag tgc tgc cac aaa ccc tgc agc atc
ttt gag ctg cag aac tac tgt aac tga

And this sequence codes for the following protein:

MAVWLQAGALLVLLVVSSVSTNPGTPQHLCGSHLVDALYLVCGPTFTGFFYNP
KRDVEPLLGFLPPKSAQETEVADFAFKDHAELIRK
RGIVEQCCHKPCSIFELQNYCN

so again we have a different insulin precursor protein that is ultimately converted into a different insulin molecule within the Zebra Fish.

I could go on and extract more sequences, but I think the point has already been established, namely that there are a multiplicity of possible insulin molecules in existence, and consequently, the idea that there can only be ONE sequence for a functional protein, even one as critically important to life as insulin, is DEAD FLAT WRONG. Now, if this is true for a protein as crucial to the functioning of vertebrate life as insulin, you can be sure that the same applies to other proteins, including various enzymes, and therefore, whenever the "One True Sequence" fallacy rears its ugly head in various places, the above provides the refutation thereof.

Frequently Occurring Fallacies No. 2: The 'Serial Trials' Fallacy

For the second of the fallacies that tends to appear in reality-denial attempts to dismiss the validity of evolution, I have decided to cover the Serial Trials Fallacy. This usually, but not always, accompanies the One True Sequence fallacy above. However, the nature of this fallacy arises via an entirely different mechanism.

Typically, what happens is that a probability calculation is constructed, usually on the basis of assumptions that are either left unstated altogether (conveniently preventing independent verification of their validity), or if they are stated, they usually fail to survive intense critical scrutiny. However, even if we allow these assumptions to remain unchallenged, the appearance of the Serial Trials fallacy means that destruction of the validity of the spurious probability calculation is easy even without resorting to the effort of destroying those other assumptions.

Basically, the Serial Trials Fallacy consists of assuming that only one participant in an interacting system is performing the necessary task at any one time. While this may be true for a lone experimenter engaged in a coin tossing exercise, this is assuredly NOT true of any system involving chemical reactions, which involves untold billions of atoms or molecules at any given moment. This of course has import for abiogenesis as well, against which bad probability calculations and the Serial Trials Fallacy are routinely deployed. I shall concentrate here on abiogenetic scenarios, but what follows applies equally to nuclear DNA replication and any absurd arguments based upon bad probability calculations and the Serial Trials Fallacy that mutations cannot occur in a given amount of time.

The idea is simply this. If you only have one participant in the system in question, and the probability of the desired outcome is small, then it will take a long time for that outcome to appear among the other outcomes. But, if you have billions of participants in the system in question, all acting simultaneously, then even a low-probability outcome will occur a lot more quickly.

For example, if I perform trials that consist of ten coin tosses in a row per trial, and this takes about 20 seconds, then I'm going to take a long time to arrive at 10 heads in a row, because the probability is indeed 1/(210) = 1/1024. In accordance with a basic law of probability, namely that if the probability of the event is P, the number of serial trials required will be 1/P, I shall need to conduct 1,024 serial trials to obtain 10 heads in a row (averaged over the long term of course) and at 1 trial every 20 seconds, this will take me about six days, if all I do is toss coins without any breaks for sleep, food or other necessary biological functions. If, however, I co-opt 1,024 people to perform these trials in parallel, at least one of them should arrive at 10 heads from the very outset. If I manage by some logistical wonder to co-opt the entire population of China to toss coins in this fashion, then with a billion people tossing the coins, we should see 1,000,000,000/1024, which gives us 976,562 Chinese coin tossers who should see 10 heads in a row out of the total 1,000,000,000 Chinese.

Now given that the number of molecules in any given reaction even in relatively dilute solutions is large (a 1 molar solution contains 6.023 × 1023 particles of interest per litre of solution, be they atoms, molecules or whatever) then we have scope for some serious participating numbers in terms of parallel trials. Even if we assume, for the sake of argument in a typical prebiotic scenario, that only the top 100 metres of ocean depth is available for parallel trials of this kind (which is a restriction that may prove to be too restrictive once the requisite experimental data are in from various places around the world with respect to this, and of course totally ignores processes around volcanic black smokers in deep ocean waters that could also fuel abiogenetic reactions) and we further assume that the concentration of substancers of interest is only of the order of millimoles per litre, then that still leaves us with the following calculation:

[1] Mean radius of Earth = 6,371,000 m, and 100 m down, that radius is 6,370,900 m

[2] Volume of sea water of interest is therefore 4/3π(R3-r3)

which equals 5.1005 × 1016 m3

1 litre of solution of 1 mmol l-1 will contain 6.023 × 1020 reacting particles of interest, which means that 1 m3 of solution will contain 6.023 × 1026 particles, and therefore the number of particles in the 100 metre layer of ocean around the world will be 3.0730 × 1043 particles. So already we're well into the territory where our number of parallel trials will make life a little bit easier. At this juncture, if we have this many interacting particles, then any reaction outcome that is computed to have a probability of greater than 1/(3.073 ×1043) is inevitable with the first reaction sequence.

Now, of course, this assumes that the reactions in question are, to use that much abused word by reality denialists, "random" (though their usage of this word tends to be woefully non-rigorous at the best of times). However, chemical reactions are not "random" by any stretch of the imagination (we wouldn't be able to do chemistry if they were!), which means that once we factor that into the picture alongside the fact that a parallel trial involving massive numbers of reacting molecules is taking place, the spurious nature of these probabilistic arguments against evolution rapidly become apparent.

The same parallel trials of course take place in reproducing populations of organisms. Of course, the notion falsely propagated by reality denialists is that we have to wait for one particular organism to develop one particular mutation, and that this is somehow "improbable". Whereas what we really have to wait for is any one organism among untold millions, or even billions, to develop that mutation, for evolution to have something to work with. If that mutation is considered to have a probability of 1/109, then we only have to wait for 109 DNA replications in germ cells to take place before that mutation happens. If our working population of organisms is already comprised of 1 billion individuals (last time I checked, the world human population had exceeded 6.6 billion) then that mutation is inevitable.

So, next time you see a spurious probability calculation appearing that purports to "disprove evolution", look out for two salient features, namely:

[1] Base assumptions that are either not stated altogether (thus conveniently preventing independent verification) or base assumptions that fail to withstand critical scrutiny, and

[2] The Serial Trials Fallacy above.