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Homologies, differences and information jumps

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shark-553666_1280In recent posts, I have been discussing some important points about the reasonable meaning of homologies and differences in the proteome in the course of natural history. For the following discussion, just to be clear, I will accept a scenario of Common Descent (as explained in many recent posts) in the context of an ID approach. I will also accept the very reasonable concept that neutral or quasi-neutral random variation happens in time, and that negative (purifying) selection is the main principle which limits random variation in functional sequences.

My main points are the following:

  1. Given those premises, homologies through natural history are certainly an indicator of functional constraints, because they mean that some sequence cannot be significantly transformed by random variation. Another way to express this concept is that variation in a functional sequence with strong functional constraints is not neutral, but negative, and therefore negative selection will in mot cases suppress variation and conserve the functional sequence through time. This is a very important point, because it means that strong homologies through time point to high functional complexity, and therefore to design. I have used this kind of argument, for example, for proteins like the beta chain of ATP synthase (highly conserved from LUCA to humans) and Histone H3 (highly conserved in all eukaryotes).
  2. Differences between homologues, instead, can have two completely different meanings:
  •  2a) They can be the result of accumulating neutral variation in parts of the molecule which are not functionally constrained
  • 2b) They can be the expression of differences in function in different species and contexts

I do believe that both 2a and 2b happen and have an important role in shaping the proteome. 2b, in particular, is often underestimated. It is also, in many cases, a very good argument for ID.

 

Now, I will try to apply this reasoning to one example. I have chosen a regulatory protein, one which is not really well understood, but which has certainly an important role in epigenetic regulation. The protein is called “Prickle”, and we will consider in particular the one known as “Prickle 1”. It has come to my attention trough an interesting paper linked by Dionisio (to whom go my sincere thanks and appreciation):

Planar polarization of Vangl2 in the vertebrate neural plate is controlled by Wnt and Myosin II signaling

In brief, Prickle is a molecule implied, among other things, in planar polarization events and in the regulation of neural system in vertebrates.

Let’s have a look at the protein. From Wikipedia:

Prickle is part of the non-canonical Wnt signaling pathway that establishes planar cell polarity.[2] A gain or loss of function of Prickle1 causes defects in the convergent extension movements of gastrulation.[3] In epithelial cells, Prickle2 establishes and maintains cell apical/basal polarity.[4] Prickle1 plays an important role in the development of the nervous system by regulating the movement of nerve cells.[5

And:

Mutations in Prickle genes can cause epilepsy in humans by perturbing Prickle function.[12] One mutation in Prickle1 gene can result in Prickle1-Related Progressive Myoclonus Epilepsy-Ataxia Syndrome.[2] This mutation disrupts the interaction between prickle-like 1 and REST, which results in the inability to suppress REST.[2] Gene knockdown of Prickle1 by shRNA or dominant-negative constructs results in decreased axonal and dendritic extension in neurons in the hippocampus.[5] Prickle1 gene knockdown in neonatal retina causes defects in axon terminals of photoreceptors and in inner and outer segments.[5]

The human protein is 831 AAs long.

Its structure is interesting: according to Uniprot, in the first part of the molecule we can recognize 4 domains:

1 PET domain:  AAs 14 – 122

3 LIM zinc-binding doamins:  AAs 124 – 313

In the rest of the sequence (AAs 314 – 831) no known domain is recognized.

Here is the FASTA sequence of the human protein, divided in the two parts (red: 4 domain part; blue: no domain part):

 

>sp|Q96MT3|PRIC1_HUMAN Prickle-like protein 1 OS=Homo sapiens GN=PRICKLE1 PE=1 SV=2
MPLEMEPKMSKLAFGCQRSSTSDDDSGCALEEYAWVPPGLRPEQIQLYFACLPEEKVPYV
NSPGEKHRIKQLLYQLPPHDNEVRYCQSLSEEEKKELQVFSAQRKKEALGRGTIKLLSRA
VMHAVCEQCGLKINGGEVAVFASRAGPGVCWHPSCFVCFTCNELLVDLIYFYQDGKIHCG
RHHAELLKPRCSACDEIIFADECTEAEGRHWHMKHFCCLECETVLGGQRYIMKDGRPFCC
GCFESLYAEYCETCGEHIGVDHAQMTYDGQHWHATEACFSCAQCKASLLGCPFLPKQGQI
YCSKTCSLGEDVHASDSSDSAFQSARSRDSRRSVRMGKSSRSADQCRQSLLLSPALNYKF
PGLSGNADDTLSRKLDDLSLSRQGTSFASEEFWKGRVEQETPEDPEEWADHEDYMTQLLL
KFGDKSLFQPQPNEMDIRASEHWISDNMVKSKTELKQNNQSLASKKYQSDMYWAQSQDGL
GDSAYGSHPGPASSRRLQELELDHGASGYNHDETQWYEDSLECLSDLKPEQSVRDSMDSL
ALSNITGASVDGENKPRPSLYSLQNFEEMETEDCEKMSNMGTLNSSMLHRSAESLKSLSS
ELCPEKILPEEKPVHLPVLRRSKSQSRPQQVKFSDDVIDNGNYDIEIRQPPMSERTRRRV
YNFEERGSRSHHHRRRRSRKSRSDNALNLVTERKYSPKDRLRLYTPDNYEKFIQNKSARE
IQAYIQNADLYGQYAHATSDYGLQNPGMNRFLGLYGEDDDSWCSSSSSSSDSEEEGYFLG
QPIPQPRPQRFAYYTDDLSSPPSALPTPQFGQRTTKSKKKKGHKGKNCIIS

So, this is a very interesting situation, which is not so rare. We have the first part of the sequence (313 AAs) which configures well known and conserved domains, while “the rest”(517 AAs)  is apparently not understood in terms of structure and function.

So, to better understand what all this could mean, I have blasted those two parts of the human molecule separately.

(Those who are not interested in the technical details, can choose here to go on to the conclusions  🙂 )

The first part of the sequence (AAs 1 – 313) shows no homologies in prokaryotes. So, we are apparently in the presence of domains which appear in eukaryotes.

In fungi, we find some significant, but weak, homologues. The best hit is an expect of 2e-21, with 56 identities and 93 positives (99.4 bits).

Multicellular organisms have definitely stronger homologies:

C. elegans:  144 identities, 186 positives, expect 2e-90 (282 bits)

Drosophila melanogaster:  202 identities, 244 positives, expect 5e-152 (447 bits)

Let’s go to non vertebrate chordates:

Cephalochordata (Branchiostoma floridae):  222 identities, 256 positives, expect 6e-165 (484 bits)

Tunicata (Ciona intestinalis): 196 identities, 241 positives, expect 2e-149 (442 bits)

Now, vertebrates:

Cartilaginous fishes (Callorhincus milii): 266 identities, 290 positives, expect 0.0 (588 bits)

Bony fishes (Lepisosteus oculatus): 274 identities, 292 positives, expect 0.0 (598 bits)

Mammals (Mouse): 309 identities, 312 positives, expect 0.0 (664 bits)

IOWs, what we see here is that the 4 domain part of the molecule, absent in prokaryotes, is already partially observable in single celled eukaryotes, and is strongly recognizable in all multicellular beings. It is interesting that homology with the human form is not very different between drosophila and non vertebrate chordates, while there is a significant increase in vertebrates, and practical identity already in mouse. That is a very common pattern, and IMO it can be explained as a mixed result of different functional constraints and neutral evolution in different time splits.

Now, let’s go to “the rest” of the molecule: AAs 314 – 831 (518 AAs). No recognizable domains here.

What is the behaviour of this sequence in natural history?

Again, let’s start again from the human sequence and blast it.

With Prokaryotes: no homologies

With Fungi: no homologies

C. elegans: no homologies

Drosophila melanogaster: no homologies

Let’s go to non vertebrate chordates:

Cephalochordata (Branchiostoma floridae):  no significant homologies

Tunicata (Ciona intestinalis): no significant homologies

So, there is no significant homology in the whole range of eukaryotes, excluding vertebrates and including chordates which are not vertebrates.

Now, what happens with vertebrates?

Here are the numbers:

Cartilaginous fishes (Callorhincus milii): 350 identities, 429 positives, expect 0.0 (597 bits)

Bony fishes (Lepisosteus oculatus): 396 identities, 446 positives, expect 0.0 (662 bits)

Mammals (Mouse): 466 identities, 491 positives, expect 0.0 (832 bits)

IOWs, what we see here is that the no domain part of the molecule is practically non existent in prokaryotes, in single celled eukaryotes and in all multicellular beings which are not vertebrates. In vertebrates, the sequence is not only present in practically all vertebrates, but it is also extremely conserved, from sharks to humans. So, we have a steep informational jump from non chordates and non vertebrate chordates, where the sequence is practically absent, to the very first vertebrates, where the sequence is already highly specific.

What does that mean from an ID point of view? It’s simple:

a) The sequence of 517 AAs which represents the major part of the human protein must be reasonably considered highly functional, because it is strongly conserved throughout vertebrate evolution. As we have said in the beginning, the only reasonable explanation for high conservation throughout a span of time which must be more than 400 million years long is the presence of strong functional constraints in the sequence.

b) The sequence and its function, whatever it may be (but it is probably an important regulatory function) is highly specific of vertebrates.

We have here a very good example of a part of a protein which practically appears in vertebrates while it is absent before, and which is reasonably highly functional in vertebrates.

So, to sum up:

  1. Prickle 1 is a functional protein which is found in all eukaryotes.
  2. The human sequence can be divided in two parts, with different properties.
  3. The first part, while undergoing evolutionary changes, is rather well conserved in all eukaryotes. Its function can be better understood, because it is made of known domains with known structure.
  4. The second part does not include any known domain or structure, and is practically absent in all eukaryotes except vertebrates.
  5. In vertebrates, it is highly conserved and almost certainly highly functional. Probably as a regulatory epigenetic sequence.
  6. For its properties, this second part, and its functional sequence, are a very reasonable object for a strong design inference.

 

I have added a graph to show better what is described in the conclusions, in particular the information jump in vertebrates for the second part of the sequence:

Graph3

Note: Thanks to the careful checking of Alicia Cartelli, I have corrected a couple of minor imprecisions in the data and the graph (see posts #83 and #136). Thank you, Alicia, for your commitment. The sense of the post, however, does not change.

Those who are interested in the evolutionary behaviour of protein Prickle 2 could give a look at my posts #127 and #137.

Comments
Mung: You certainly know the answer, but if you allow me (again, if I remember well) to use you as a comfortable interlocutor to answer questions made by others, I would like to clarify that the bit scores I used are those computed by the Blast online service. The documentation can be found here: http://www.ncbi.nlm.nih.gov/BLAST/tutorial/gpuccio
February 5, 2016
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GaryGaulin: I appreciate your post, but I am still ill at ease with what you say. First of all, I definitely do not agree with your comment about classical ID. ID is not an argument from ignorance. It is an argument from facts and reasonable inference. If you don't understand that, you are no better than dogmatic neo darwinists. Regarding your "definitions", I find them tragically biased. In essence, it seems to me that you, like materialists, try to ignore the role of consciousness in conscious activities, first of all intelligence. For example, you solve with the introduction of a RAM what can only be explained by conscious representations. That said, I suppose that there is really no point going into further detail, if we don't clarify the fundamental differences in our way of thinking.gpuccio
February 5, 2016
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From my lack of understanding: classical ID is an argument from ignorance: Fixed that fer ya!Mung
February 4, 2016
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So random numbers were punched into a calculator until pucci's fingers got tired or what?Alicia Cartelli
February 4, 2016
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It was calculated with a precisely crafted and carefully calibrated bit-score calculator.Mung
February 4, 2016
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Does anybody know how the "bit score" was calculated? Keep in mind, I am the lay-est of laymen.Alicia Cartelli
February 4, 2016
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gpuccio at 57:
I am not sure that I understand what your position is, and how it differs from classical ID.
From my understanding: classical ID is an argument from ignorance:
https://en.wikipedia.org/wiki/Argument_from_ignorance Argument from ignorance (Latin: argumentum ad ignorantiam), also known as appeal to ignorance (in which ignorance stands for "a lack of contrary evidence"), is a fallacy in informal logic. It asserts that a proposition is true because it has not yet been proven false (or vice versa). This represents a type of false dichotomy in that it excludes a third option, which is that: there may have been an insufficient investigation, and therefore there is insufficient information to prove the proposition be either true or false. Nor does it allow the admission that the choices may in fact not be two (true or false), but may be as many as four, 1. true 2. false 3. unknown between true or false 4. being unknowable (among the first three). In debates, appeals to ignorance are sometimes used in an attempt to shift the burden of proof.
What I have given this forum to study is a testable theory that this forum should now be helping to test. The lack of interest in doing so indicates that the ID movement is happy promoting a logical fallacy. In that case I'm best off to not waste what little free time I have with those who honestly look to me that they are arguing that a magical Santa Claus (of new species) has a hidden workshop somewhere (possibly the North Pole or even outer space) and once a year (or so) secretly flies around delivering presents. I provided a very nice scientific operational definition for intelligence that's based on machine intelligence and cognitive theory 101:
Behavior from a system or device qualifies as intelligent by meeting all four circuit requirements for this ability, which are: [1] Something to control (a body, either real or virtual representation) with motor muscles (proteins, electric speaker, electronic write to a screen). [2] Random Access Memory (RAM) addressed by its sensory sensors where each motor action and its associated confidence value are stored as separate data elements. [3] Confidence (central hedonic) system that increments the confidence level of successful motor actions and decrements the confidence value of actions that fail. [4] Ability to guess a new memory action when associated confidence level sufficiently decreases. For flagella powered cells a random guess response (to a new heading) is designed into the motor system by the action of reversing motor direction causing it to “tumble”. At all biological intelligence levels whatever sensory the system has to work with addresses a memory that works like a random access memory chip used in a computer. It is possible to put the contents of a RAM into a Read Only Memory (ROM) but using a ROM instead of RAM takes away the system's ability to self-learn, it cannot form new memories that are needed to adapt to new environments. The result is more of a zombie that may at first appear to be a fully functional intelligence but they are missing something necessary, a RAM in the circuit, not a ROM. Behavior of matter does not need to be intelligent, a fully trained (all knowing) ROM could be used to produce atomic/molecular behavior. But a ROM would not work where intelligent behavior is needed. Unless the ROM contains all-knowing knowledge of the future and all the humans it will ever meet in its lifetime it can never recall memories of meeting them, or their name and what they look like. For machine intelligence the IBM Watson system that won at Jeopardy qualifies as intelligent. Word combinations for hypotheses were guessed then tested against memory for confidence in each being a hypothesis that is true and whether confident enough in its best answer to push a button/buzzer. The Watson platform had a speaker (for vocal muscles) and muscles guiding a pen was simulated by an electric powered writing device. For computer modeling purposes the behavior of matter can be thought of as being “all-knowing” in the sense that the behavior is inherent, does not have to learn its responses. A computer model then starts off with this behavior already in memory and has no GUESS or CONFIDENCE included in the algorithm, as does intelligence. Memory contents then never changes. Only a GUESS can write new data to memory and GUESS must here be taken out of the algorithm. But it is possible to leave the CONFIDENCE in the algorithm, it will still work the exact same way. Where this in time proves to be true for real matter it would be a valuable clue as to how consciousness works and possibly how to model it, which may in turn help answer the “big questions” including those pertaining to afterlife. Reciprocal cause/causation between levels goes in both the forward and reverse direction. These communicative behavioral pathways cause all of our complex intelligence related behaviors to connect back to the behavior of matter, which does not necessarily need to be intelligent to be the fundamental source of consciousness. Multicellular and cellular level individuals are born then die while the genetic molecular level lives on, by this self-replication of itself. We are part of a molecular learning process that keeps itself going through time by replicating previous contents of genetic memory along with good (better than random) guesses what may work better in the next replication, for our children. The resulting cladogram shows a progression of adapting designs evidenced by the fossil record where never once was there not a predecessor of similar design (which can at times lead to entirely new function) present in memory for the descendant design to have come from.
If anyone is serious about writing a scientific theory then that is the operational definition for intelligence to study and test: http://intelligencegenerator.blogspot.com/ I'm always happy to answer good questions but science does not obligate me to entertain a logical fallacy that attempts to argue for what sounds more like Santa Claus.GaryGaulin
February 4, 2016
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Zachriel: "The quantitative argument assumes that there are no selectable intermediaries." Well, I would rather say: "The quantitative argument acknowledges that there is no evidence of selectable intermediaries." Maybe you assume that there could be selectable intermediaries, but unless you find trace of them, that is just your unsupported assumption. I think we must work with the facts we have.gpuccio
February 4, 2016
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gpuccio: Neo-darwinists are strange people: they always complain that IDists are never quantitative, that they do not offer scientific analyses, that CSI or its similar concepts is never measured, and so on. Then, when one tries to do exactly that, they suddenly become disinterested, distracted, dismissive, and they don’t seem at all happy. The quantitative argument assumes that there are no selectable intermediaries.Zachriel
February 4, 2016
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I'd be satisfied seeing a land mammal with a fluke and a blowhole.Mung
February 4, 2016
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Alicia Cartelli:
Wow! Groundbreaking!
I know! Isn't biology fun!Mung
February 4, 2016
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"Sort of like, if whales evolved from land mammals, where are the whales with legs." We should remember that what we are discussing here is sequences, digital information, and that allows to be much more quantitative. After all, we cannot blast legs or body forms. Neo-darwinists are strange people: they always complain that IDists are never quantitative, that they do not offer scientific analyses, that CSI or its similar concepts is never measured, and so on. Then, when one tries to do exactly that, they suddenly become disinterested, distracted, dismissive, and they don't seem at all happy. How strange is human nature.gpuccio
February 4, 2016
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Sort of like, if whales evolved from land mammals, where are the whales with legs. I see whales with legs at the beach all the time. :Dmike1962
February 4, 2016
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EugeneS, Dionisio: Thank you for your very kind attention, which makes the effort to go into details absolutely worth!gpuccio
February 4, 2016
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Zachriel: "To be fair, the point raised concerns the supposedly large gap." Thank you for being fair! :) I suppose that's the difference between you and Alicia Cartelli. And it is a big difference (a jump? :) ).gpuccio
February 4, 2016
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gpuccio @58 Clear explanation! Even I understood it*! BTW, #58 seems to nicely expand what you wrote @46 in response to #43. This thread looks like what the doctor prescribed to help me learn quite a few technical issues. Mile grazie caro Dottore! (*) perhaps struggled trying to grasp a few details though. :)Dionisio
February 4, 2016
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GP, Many Thanks. No need to apologize. I just needed a bit more detail. It is clear. My take on UPB is a bit different. I got it from David Abel's paper of 2009. His approach is broadly the same, but his point (unless I misinterpret it) is given the system and a certain type of interactions in it, we can derive a max number of bits bound for these interactions irrespective of all possible combinations of factors interplaying. I take it to mean that regardless of whether chance and necessity act on their own in sequence or in combination, the net effect is only so many bits of information (under certain assumptions), not more. Essentially, this is saying that all natural processes can provide is little more than information noise. But as you said, the probability of a series of consecutive successful steps can be significantly higher than that of a single sudden jump (again, under certain assumptions).EugeneS
February 4, 2016
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Alicia Cartelli: So during the same time that some of the chordates were evolving a bony spine, a protein now known to be involved in spinal development also evolved? That seems to be it. To be fair, the point raised concerns the supposedly large gap. Sort of like, if whales evolved from land mammals, where are the whales with legs.Zachriel
February 4, 2016
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GaryGaulin: You ask: "The question is now: how do you know you are not seeing a genetic memory for a working solution to a morphological design problem that was figured out by a billions of years old intelligent (and possibly conscious) system that has always been inside of all of us, which has a mind that works using the same systematics as our brain?" I am sorry, but "how do you know you are not seeing" is not a good scientific question, IMO. The right question is: why should I think that I am seeing what you describe? What are the empirical facts which support your explanation?gpuccio
February 4, 2016
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EugeneS: You raise an important point, which deserves some in depth discussion. It is true that the absolute probabilty of finding a specific functional sequence (assuming for the moment a target space of 1) is 1/search space. Therefore, for both sequences, red and blue, we have the same absolute probability of finding a single functional sequence in the time span which goes from prokaryotes to sharks, because both sequences exhibit about 600 bits of information in sharks which are then conserved in vertebrates. As we have to consider the probabilistic resources of the system, the computation would go this way: a) Absolute probability of finding the sequence by a random search: 2e-600 (for both red and blue sequence) b) Probabilistic resources from prokaryotes to sharks: let's say 2e140 (240 bits, which is more or less the number of bacterial reproductions in 1 billion years. This is opbviously an approximation, but it will do for our discussion). These resources are again the same for the red and the blue sequence. So, the absolute probability of finding a functional sequence of A or B in 1 billion years would be, at most, 2e-466 (466 bits, which comes from 2e-600 / 2e140) So, where is the difference? Why is the "information jump" so important, and whu does ot make a design inference muck more necessary fpr the blue sequence? The point is, our adversaries, IOWs "smart" neo-darwinists, will argue that positive NS can lower the probabilistic barriers (beware, now we are talking of positive NS, which is completely different from negative, purifying NS. Purifying NS is an important concept. Positive NS ia practically almost a myth). Now, is true that positive NS, if and when it happens, can lower the probabilistic barriers, but what is needed is the demostration (real demonstration) of intermediates at sequence level which are naturally selectable. IOWs, we need scientific evidence of positive NS, not only wishful thinking and personal faith. But, in the case of the red sequence, our adversaries can argue that the "gradual" forms of the sequence, in which the similarity to the human sequence "gradually" increases, can be considered as functional intermediates, expanded by positive NS. Now, I don't believe that such a concept is true: IMO, the differences here are due to both random neutral variation and different functional constraints in different species. However, it is difficult to exclude a role of positive NS, ans so we must take seriously the possibility that those different forms can be considerate as functional intermediates expanded from time to time by positive NS. OK, but we are still discussing "jumps" which are rather big: of the order of 100 - 200 bits each. Now, I will show that if we can decostruct a transition into intermediate steps, assuming that each step undergoes perfect expansion to the whole population by positive NS and in a very short time, then probabilistic barriers are significantly lowered (but in no way cancelled). Let's make an example. Let's say, for the sake of simplicity, that our functional target are the 600 bits which both sequences exhibit in sharks. Let's say that the blue sequence goes from 0 to 600 bits in one step (let's even assume that the time window is the same, 1 billion years, even if it is not true). The red sequence, instead, reaches the same target in 4 intermediate steps, and each step is supposed to undergo perfect positive NS. In this scenario, the probability to get the blue sequence in that time span is, as explained, 2e-466: IOWs, the functional information of the transition in that system is 466 bits. What is the probability of the transition of the red sequence from 0 to 600? With our assumptions, it is equal to the probability of having 4 successes, each with a probability of 2e-150, in 2e140 attempts (the probabilistic resources). That can be computed by the binomial distribution, and the total probability of the transition (under those extremely favourable assumptions) is: 2e-129 (129 bits). As you can see, there is a very big difference: from 466 bits to 129 bits. OK, our assumptions are obviously too favorable to darwinism. OK, those forms of the red proteins are probably not really selectable intermediates. OK, I would still infer design for the global transition of the red sequence, with a probability which is still 2e-129 after taking into account maximal probabilistic resource. OK. But still, we can see that the case of the blue sequence, where no intermediate form can be shown, and the time span is certainly shorter (one thing which I have not considered in this reasoning for the sake of simplicity), is much stronger as an example of design inference. I apologize for the technical details, but your question was important, and it was important to answer it.gpuccio
February 4, 2016
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GaryGaulin: I have given a look, many times, at your pages, but I must confess that, like Virgil Cain, I am perplexed. I am not sure that I understand what your position is, and how it differs from classical ID. Maybe you could try to explain the main points in simple words, just to start. If you are really interested in the discussion. And, if possible, give the empirical support for your theories, as I have tried to do.gpuccio
February 4, 2016
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I know this is "ID". That's why I had to title it "Theory of Intelligent Design".GaryGaulin
February 4, 2016
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GaryGaulin:
The question is now: how do you know you are not seeing a genetic memory for a working solution to a morphological design problem that was figured out by a billions of years old intelligent (and possibly conscious) system that has always been inside of all of us, which has a mind that works using the same systematics as our brain?
That is still design, Gary. Organisms designed to evolve is still ID, Gary. Not sure of your point.Virgil Cain
February 4, 2016
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I have given the facts that support the design inference based on dFSCI.
The question is now: how do you know you are not seeing a genetic memory for a working solution to a morphological design problem that was figured out by a billions of years old intelligent (and possibly conscious) system that has always been inside of all of us, which has a mind that works using the same systematics as our brain?GaryGaulin
February 4, 2016
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What facts have you given to support any other kind of explanation? Just to know.
Start here: http://theoryofid.blogspot.com/GaryGaulin
February 4, 2016
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GPuccio, It occurred to me. As you obviously refer to a universal plausibility bound, the crucial piece of evidence is not so much the jump, but the number of bits acquired (be it gradually or abruptly). The reason I am asking is because from the UPB perspective, as far as I understand it, it does not really matter what the graph is. Judging by that, the red graph is also displaying design because it reaches the same magnitude in bits. Am I right? Thanks.EugeneS
February 4, 2016
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Alicia Cartelli:
So during the same time that some of the chordates were evolving a bony spine,...
How can we test that claim? My bet is that Alicia doesn't have a clue.Virgil Cain
February 4, 2016
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gpuccio @48 Agree. Your time is too precious to be squandered on senseless discussions that lead nowhere. I'd rather see you investing part of your limited spare time on preparing an insightful article on the mysteriously hidden 'procedures'. :)Dionisio
February 4, 2016
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gpuccio @46 I see what you mean. Thank you.Dionisio
February 3, 2016
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Someone will probably remember that I don't discuss with Alicia Cartelli. Happy to have drawn her attention, however.gpuccio
February 3, 2016
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