James post has caused me to reflect at bit on the different perspectives of the two groups, evolution and ID with his good questions. It has prompted me to go back and look at some to the assertions made by the evolutionists and their sources to try and understand the thinking behind their perspective. As you know, I have observed what I see as a significant levels of extrapolation of data to fit the evolution model. It’s concerning and should be seen by all as a risk. I have noted it in this forum and been challenged saying that extrapolation is an intrinsic part of the scientific method. No argument from my quarter. The important question, however, is, “What level of extrapolation is legitimate and what is the threshold where we see extrapolation lose its validity?” An analogy might be helpful. Assume one performs some experiments and plots the data on an X-Y graph to determine the relationship between two variables. If one draws a line through all the data points and observes that every point falls on a straight line, one would be tempted to conclude the relationship between the two variables is linear. But what if the real relationship between the variables was defined by a Nth order polynomial? If that were indeed the case, then clearly the problem is that the extrapolation was based on either plotting too few points because the researcher didn’t have enough data (badly designed experiment) or the researcher tossed out the data points that didn’t fit. Either way, concluding linearity was incorrect for the example. The evidence you have provided me supporting the argument for evolution leaves open this question, viz., do evolutionists have enough data/have they excluded data to justify concluding that evolution is the best explanation?
The ID folks claim that the evolution model is too simplistic to explain the observed data, i.e., relating to the analogy above, the data suggests that the relationship of the variables is not linear, but higher order (inferred from its complexity). As mentioned above there are two questions that must be answered if we’re to find a confident solution. First, we must ask, “Is there enough data?” Second, we must ask, “Are evolutionist excluding data that doesn’t fit their model because of bias, ideology, or other?”
As I thought about this, I thought that it would be appropriate to go back and look at some of the data from the examples pointed to in this discussion which supposedly support macro-evolution. Maybe I’m missing something? (I can see a few of you saying, “No kidding, Sherlock!) So, I went back to some of the early posts in this forum and notice a post (#12) from Scott Buchanan in April 2017 where he was revisiting the issue of nylonase. Apparently, there was some disagreement over whether nylonase arose from a frameshift mutation (evolution) or it was a simple micro-evolutionary change of two amino acids well within the scope of the bacteria’s designed adaptability. Regardless of the cause, the part that struck me as significant from a SE perspective is that the evolution side is assuming that some small mutations (frame shifted or other) resulted in a new protein fold which in turn, resulted in a new function. For a Systems Engineer, that’s a hard pill to swallow! But back to my point. The point is that some extrapolation is going on, and its scope appears to be unjustifiably large in my opinion.
More generally, my observations looking at evidence adduced by evolutionists is that they see linearity (remember the analogy) in their evidence (fossil data, gene maps, etc). All the data they accept is consistent with the evolution model. Ergo, they feel justified that evolution is the best explanation of the data. I find problems in both dimensions of their argument. First, they don’t have sufficient data to rule out that an Nth order polynomial is a better fit, and secondly, they exclude data that doesn’t fit.
Regarding the former (that there’s not enough data), Looking at it from an SE perspective, the nylonase example is revealing. What’s is left out in the Buchanan post and the NewScientist article is an acknowledgement of the details of integration of the two proteins involved in the nylonase capability with other proteins in the bacteria to make the nylonase enzyme capable of digesting nylon. What’s inferred is that these two mutated proteins have sufficient standalone function that no coordinated action from other proteins or protein structures including C4 functions (Command, Control, Communications, and Computing) is necessary for the bacteria to be able to digest nylon which it didn’t have before the mutation. As I said earlier, that’s a hard pill to swallow.
If, however, there is “rich interdependence” of protein function in the cell where many of the proteins cannot work and perform higher level function alone, but must do in synchrony with other proteins and possibly even synergy, it’s unlikely that all the proteins involved in a particular function each mutate to either a new protein (different sequence or more amino acids) or a new fold so that they could collectively perform the new function such as digesting nylon. This is the fundamental issue that the SE perspective identifies as problematic for the evolution narrative. Isolated random changes in one or a few proteins cannot by themselves bring about phenotypic change unless all the proteins necessary to bring about that new function are established in coordination with each other to make that function possible. I stated this at the beginning (post #13), and you all have pushed back, particularly Stephen (see post #14) saying that “we know this to be false ……because of robustness in molecularly biology.”
Apparently, my point is still not clear. Maybe that can be remedied if I explain the assumptions behind the statement, i.e., that the function involved results from coordinated activity of many proteins working together as a coordinated whole. If this is not true, as Stephen claims because of robustness, then you must admit that one of two conditions must exist in the cell: 1) The proteins are not interdependent, i.e., they (thinking of a bacterial cell) can each perform phenotypic function apart from dependence on any of the other proteins in the cell, or 2) if they are dependent on other proteins in the cell, they have (via evolution) built in fault tolerance so sophisticated that any protein in the cell can mutate and all the cell functions that depend on that protein will tolerate any aberrant function caused by that mutation. Said another way, in the cell (a “protein ecosystem”) where most cell functions require significant levels of protein coordinated action to perform a function, mutation(s) of a single protein may not bring about a new function unless all the other proteins that depend on the one mutated also mutate with serendipity if a new function is to be realized. Actually, it’s even more challenging if you think that a new function may arise from a single mutation when that new function is likely to require coordinated action (physically & temporally) of many proteins. Can we get a new function (F2) from a group of 500 proteins previously working together to produce an old function (F1) by randomly changing only one of the 500? If you say yes, are you not implying that there are a very large number (500 factorial – my computer will only calculate up to 170 factorial which gives a number over 1 x 10^306) of useful functions in the cell each the result of a single nucleotide mutation? If functional amino acid sequences are truly rare, doesn’t this suggest that a single nucleotide mutation giving rise to a new functional sequence is unlikely?
An example of the required coordinated action of proteins is found in one of articles in the New Scientist titled “Five Classic Examples of Gene Evolution.” (24Mar2009) where it suggested that our three color-sensitive retina pigments, or opsins, evolved from the two-colored version in other animals such as dogs. Here is the author’s logic:
So why do we have three? In the ancestors of apes and some monkeys, the gene MWS/LWS – which codes for one of the two pigments found in most mammal eyes – was duplicated. Spare gene copies usually degenerate quickly as they acquire mutations, but in this case mutations in one copy resulted in an opsin that could detect a different spectrum of light. In this way, we gained better, trichromatic color vision. (emphasis mine)
What I find remarkable in this example is that the author is assuming that a mutation in one copy of a gene resulted in an opsin that could detect a different spectrum of light. Think about that for a second. What does it take for the entire vision-brain system of systems combination of many proteins and functions to see a new spectrum (new range of light frequencies)? The engineering required to change the entire chain of hardware (proteins) from the eye to the brain to perceive and make use of new range of light frequencies is huge. In the NewScientist article the author suggests that a few mutated genes can make that happen and that that is evidence of evolution. That reflects in my engineering mind a stunning level of naivete. A huge amount of data is missing/ignored that suggests evolution is not a good model. To say that that our three color-sensitive retina pigments, or opsins, evolved from the two-colored version in other animals such as dogs from a mutated gene MWS/LWS is amazing.
This article illustrates well the problem of extrapolation. Biologists know well how vision occurs. The biochemistry of vision is astoundingly complex especially with the way it processes the light signal from initial detection of the photon into an image in the brain that results in an actionable abstraction at the phenotype level? (See Behe’s book “Darwin’s Black Box” for good overview of it.) If a mutation occurs only in the gene MWS/LWS and other very specific mutations don’t occur in all the other proteins and higher-level structures used in processing this new frequency of light, how could that single mutation possibly provide the functional advantage that would allow natural selection to enable it to endure? Hopefully, this example illustrates missing data problem, and how evolutionists draw sweeping conclusions when not enough data is available.
The other dimension to the problem regards excluding data that doesn’t fit their model. The best illustration of that is the book “Contested Bones.” It provides a large number of important scientific facts supported by original sources to confirm the data about hominids and also about radiometric dating. Yet you guys rejected it because it didn’t agree with your sources. That’s the point. There are other legitimate sources that disagree, and your response is to throw it out. If there were a major theme of that book, it would be “There’s data out there that evolutionists are ignoring.”
Don’t you see the risks to your model in tossing out data because it doesn’t fit your narrative? Is that scientific?
To sum up, after reading James’ post, I felt it appropriate to do some reflection and review our discussion over the past month. It seems that the differences we have are pretty much centered on the evidence/data we are using to support our arguments. In that regard, two issues arise: 1) is the data sufficient to infer a best explanation (evolution or ID), and 2) are we forcing the data to fit our model by excluding outliers? I offered an analogy that showed the problem of not enough data so that a linear relationship could be inferred rather than a Nth order polynomial. Failing to acknowledge there’s insufficient data is a legitimate concern. How is this manifesting in the evolution argument presented by the evolutionists? The NewScientist article illustrates well the problem of interpreting without having all the data. The second issue regards exclusion by evolutionists of legitimate data that doesn’t fit their narrative. The reaction to the book “Contested Bones” illustrates that well.