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Author Topic:   Recent paper with an ID spin? Abel and Trevors (2005).
Percy
Member
Posts: 22392
From: New Hampshire
Joined: 12-23-2000
Member Rating: 5.3


Message 61 of 85 (521459)
08-27-2009 2:20 PM
Reply to: Message 60 by Wounded King
08-27-2009 11:36 AM


Re: Durston et al . program
Something's not right. If their measures were fundamentally still just Shannon information, then the greatest measures of information or complexity should have corresponded to the random proteins. I must not understand what Fit is actually measuring, but if it isn't somehow a function of information then I don't see the relevance to any ID claims.
Let me know if I'm just not doing enough homework and I'll try to find some time to dig into the details of the Durston paper.
--Percy

This message is a reply to:
 Message 60 by Wounded King, posted 08-27-2009 11:36 AM Wounded King has replied

Replies to this message:
 Message 62 by Wounded King, posted 08-28-2009 4:52 AM Percy has replied

  
Wounded King
Member
Posts: 4149
From: Cincinnati, Ohio, USA
Joined: 04-09-2003


Message 62 of 85 (521577)
08-28-2009 4:52 AM
Reply to: Message 61 by Percy
08-27-2009 2:20 PM


Re: Durston et al . program
From the program what they do is calculate what they call Functional site entropy. This is based on the conservation of no just the most prevalent amino acid at a position but conserved alternative amino acids, i.e. traditional conservation will score a site which amongst 10 sequences has 8 tryptophans, and 2 alanines the same as one with 8 tryptophans, 1 cysteine and 1 alanine while the Durston et al program would score the 1st example as higher due to the recurrence of tryptophan.
The thing is to calculate the FCS for any sequence you need a 'ground state' sequence to compare it to which is described as ...
Durston et al writes:
The ground state g (an outcome of F) of a system is the state of presumed highest uncertainty (not necessarily equally probable) permitted by the constraints of the physical system, when no specified biological function is required or present.
They term a random sequence such as you describe as the 'null state' and state that it can be functionally substituted for the ground state since ...
Durston et al writes:
actual dipeptide frequencies and single nucleotide frequencies in proteins are closer to random than ordered
You then calculate functional uncertainty for the sequence by comparing thee ground or null state to your actual state or states. This is the bit I have trouble understanding it is where they introduce actual biological function in to the mix ...
Durston et al writes:
Xf denotes the conditional variable of the given sequence data (X) on the described biological function f which is an outcome of the variable (F). For example, a set of 2,442 aligned sequences of proteins belonging to the ubiquitin protein family (used in the experiment later) can be assumed to satisfy the same specified function f, where f might represent the known 3-D structure of the ubiquitin protein family, or some other function common to ubiquitin. The entire set of aligned sequences that satisfies that function, therefore, constitutes the outcomes of Xf.
I'm still very unclear what actual sort of variable they are deriving for their functions? Is it simply all the variability in a set of sequences which fulfill a specific functional criteria? How can you determine such a figure for a single sequence? The only example they give is for the poly-a sequence, which they say has no functional uncertainty since it is always the same.
This seems to leave the determination of function so subjective as to allow one to plug in almost anything.
If you have the time someone with a bit more mathematical /computational knowledge having a look would definitely be good. I'm only an incidental sort of bioinformatician.
TTFN,
WK

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 Message 61 by Percy, posted 08-27-2009 2:20 PM Percy has replied

Replies to this message:
 Message 63 by Percy, posted 08-28-2009 6:59 AM Wounded King has not replied

  
Percy
Member
Posts: 22392
From: New Hampshire
Joined: 12-23-2000
Member Rating: 5.3


Message 63 of 85 (521590)
08-28-2009 6:59 AM
Reply to: Message 62 by Wounded King
08-28-2009 4:52 AM


Re: Durston et al . program
Thanks for the information. I have a minor release to get out today and then we've got to prepare to leave for vacation beginning tomorrow, but we often just wind down in the room at the end of the day, so let me plan to see what I can figure out regarding the variable they derive for their functions.
--Percy

This message is a reply to:
 Message 62 by Wounded King, posted 08-28-2009 4:52 AM Wounded King has not replied

  
Smooth Operator
Member (Idle past 5114 days)
Posts: 630
Joined: 07-24-2009


Message 64 of 85 (522913)
09-06-2009 9:39 AM
Reply to: Message 53 by Wounded King
08-22-2009 4:15 AM


quote:
So where are the usable criteria for function?
What doy ou mean by this?
quote:
A PFAM family is based upon structural similarity not function per se. I think the problem with this method is that they claim that it can be used for genes/proteins with similar functions but highly divergent molecular structures, but given their method is based on sequence similarity they give no explanation of how one could do this. Their method seems redundant when compared to more sophisticated metrics like BLOSUM which take into account the physicochemical properties of the amino acids.
But they already assume a function in the first place. That is why they said that the genome of a certain organism has to be sequenced and studied first. That you can apply the method to other proteins.
What exactly does the BLOSUM measure? The functionality of the protein, or just the complexity?
quote:
Well of course they can. There is nothing creationists and IDers love better than multiplying a whole lot of probabilities together to come up with one really tiny probability and then declaring that it shows evolution is impossible. The question is whether doing so actually has any sort of meaning or utility.
Why wouldn't is? Are you saying that nature can take apart a problem and deal with it bit by bit? No it's can't it has to deal with the whole thing. When you add up all the probabilities, you come up with a really small probability, and yes, random chances has to resolve it all.
quote:
The FSC is really nothing more than a slightly modified measure of conservation. It in no way accounts for all the possible unknown sequences which can also fulfil a particular function.
That is true, but it gives us an estimate. The only thing that we shoudl be really interested there is that it can tell the difference from OSC and RSC.
quote:
So as usual the probability measurement is only ever going to be for the exact set of genetic structures that have been fed into it. This means that rather than the likelihood of any molecule evolving to perform a particular function you are only looking at the likelihood of the exact molecular set you were studying evolving. So you will no doubt have a very small number but sets of die rolls with infinitesimally small probabilities are generated every day.
As I said, you will have an estimate, but again, you can't just say that ANY sequence could have evolved to be the right one. Because we know for a fact that a lot of sequences have no biological meaning. They are useless. So we are basicly estimating where is an island of functionality in a sea of meaninglessness.

This message is a reply to:
 Message 53 by Wounded King, posted 08-22-2009 4:15 AM Wounded King has replied

Replies to this message:
 Message 66 by Wounded King, posted 09-07-2009 8:00 AM Smooth Operator has replied

  
Smooth Operator
Member (Idle past 5114 days)
Posts: 630
Joined: 07-24-2009


Message 65 of 85 (522915)
09-06-2009 9:45 AM
Reply to: Message 54 by AdminNosy
08-22-2009 9:35 AM


Re: bare links again
quote:
This is your whole explanation?
Yes, it is. Look at how short the method is. What else should I say?
They weere basicly going AA by AA and trying to find how may times it repeats itself in other protein structures. The more you find that specific AA, in teh right place, the more likely it is that it is contributing to the said function. And of course, you have the cutoff that represents the minimum AAs you should have. If there are not enough of them they could be contributing to the FSC count, but reality they are random sequences.
quote:
The ∆H for each column in the array was computed by moving through each column in the set of aligned sequences and compiling the number of occurrences of each amino acid in each column. The estimated probability P for each amino acid represented in the column was equal to the number of occurrences of that amino acid in that column divided by the total number of aligned sequences in the set. There are numerous columns in a flat set of aligned sequences that contain insertions or deletions. These are usually in regions that have considerable flexibility and may, or may not contribute to the functional complexity of the protein. To avoid the effect of the columns representing indels, a cutoff value was input into the program. The cutoff value represented the minimum number of amino acids occurring in a column divided by the total number of aligned sequences. If the total number of amino acids in a given column was below the cut-off value, due to a large number of indel-produced vacancies in the column, then the value of ∆H was automatically set to zero. Since these regions indicate little or no sequence conservation, they are already close to the null state, so setting ∆H = 0 is a reasonable move and prevents such columns from a spurious contribution to the overall FSC of the protein. The cutoff value was adjusted from a minimum of .55 to a maximum of .89 such that the number of remaining sites to be evaluated was very close to the standard protein length suggested by Pfam. The total number of remaining sites was output as the size of the protein. The ∆H for each column was summed and then output as the estimation in Fits of the FSC of the protein. The FSC density of each protein was computed by dividing the estimated number of Fits by the size N of the protein.

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 Message 54 by AdminNosy, posted 08-22-2009 9:35 AM AdminNosy has not replied

  
Wounded King
Member
Posts: 4149
From: Cincinnati, Ohio, USA
Joined: 04-09-2003


Message 66 of 85 (522971)
09-07-2009 8:00 AM
Reply to: Message 64 by Smooth Operator
09-06-2009 9:39 AM


What doy ou mean by this?
I mean other than saying, 'this PFAM family are all likely to share a common function', they give no usable criteria for how one can investigate any other aspect of biological function.
They give lots of examples of what they think it could be applied to, but none of them are doable the way they describe their process working. I can't derive an FSC value for the FGF signaling pathway by making an alignment of all the genes/proteins in the pathway, because they won't align. Am I supposed to make alignments for every element with whatever relatives I can find? Should they be within the same organism? From different species? They say you can compare functionally similar structurally distinct proteins, but they don't say how.
The main problem with this approach is that your measures could be entirely wrong because you just don't know what sequences could perform a particular biological function, you only know which ones you have which putatively do perform that function. So you will always be overestimating the FSC.
That is why they said that the genome of a certain organism has to be sequenced and studied first. That you can apply the method to other proteins.
Except their examples use proteins from multiple different species, not just one genome. And the proteins haven't just been sequenced they have also been aligned and assigned to families based on structural similarity. It seems like all of the work has already been done here, what is Durston et al.'s program adding to the mix, except a slightly varied form of conservation metric, of which several already exist?
What exactly does the BLOSUM measure? The functionality of the protein, or just the complexity?
It is a measure of the conservation of amino acids which takes into account the nature of the substitutions which aren't most conserved, as the Durston et al. program does, but also takes into account the physiochemical/functional properties of the amino acids by scoring substitutions based on a matrix of amino acid substitutions scores derived from a number of highly conserved protein sequences. There are different Blosum matrices depending on the similarity of the sequences under investigation. Like Durston et al.'s FSC measure it is calculated for each residue.
When you add up all the probabilities, you come up with a really small probability, and yes, random chances has to resolve it all.
No it didn't. Random chance at worst had to generate all of the genetic variability involved, something that mutational mechanisms do all the time. Be that as it may it still doesn't obviate the grossly mistaken assumption that the way things are currently is the only possible functional conformation for things. So all you are calculating is the probability that exactly this form of biological complexity evolved. This sort of posterior probability calculation is completely meaningless, even if we were to accept that all the values you might wish to plug in were accurate.
The only thing that we shoudl be really interested there is that it can tell the difference from OSC and RSC.
There were already plenty of methods available for distinguishing totally random sequences (RSC) and highly repetitive sequences (OSC), like the examples that were given, from functional coding sequences and functional non-coding sequences. AS for the OSC example, they calculate that in a completely different way to all of the other ones, since if you actually use it with their program, i.e. give it a dozen sequences of poly-A from montmorillonite clay. it will give you a maximal FSC result.
So we are basicly estimating where is an island of functionality in a sea of meaninglessness.
Indeed, with the most conservative estimate possible based on highly structurally similar proteins. And you are then adding up all of these conservative estimates to make a number with essentially no scientific meaning. One has to wonder why? It doesn't show that the system couldn't evolve. It doesn't even show that it is impossibly unlikely. It certainly isn't positive evidence for intelligent design. The only purpose of this seems to be for rhetorical purposes to generate big numbers to impress people with.
TTFN,
WK

This message is a reply to:
 Message 64 by Smooth Operator, posted 09-06-2009 9:39 AM Smooth Operator has replied

Replies to this message:
 Message 67 by Smooth Operator, posted 09-09-2009 3:20 PM Wounded King has replied

  
Smooth Operator
Member (Idle past 5114 days)
Posts: 630
Joined: 07-24-2009


Message 67 of 85 (523333)
09-09-2009 3:20 PM
Reply to: Message 66 by Wounded King
09-07-2009 8:00 AM


quote:
I mean other than saying, 'this PFAM family are all likely to share a common function', they give no usable criteria for how one can investigate any other aspect of biological function.
They give lots of examples of what they think it could be applied to, but none of them are doable the way they describe their process working. I can't derive an FSC value for the FGF signaling pathway by making an alignment of all the genes/proteins in the pathway, because they won't align. Am I supposed to make alignments for every element with whatever relatives I can find? Should they be within the same organism? From different species? They say you can compare functionally similar structurally distinct proteins, but they don't say how.
The main problem with this approach is that your measures could be entirely wrong because you just don't know what sequences could perform a particular biological function, you only know which ones you have which putatively do perform that function. So you will always be overestimating the FSC.
Yes, well, you are supposed to measure the sequences of the functions you do know what they are for. What would be the point of measuring a sequence for which you do not know a function? Maybe it's totally useless.
quote:
Except their examples use proteins from multiple different species, not just one genome. And the proteins haven't just been sequenced they have also been aligned and assigned to families based on structural similarity. It seems like all of the work has already been done here, what is Durston et al.'s program adding to the mix, except a slightly varied form of conservation metric, of which several already exist?
Exactly, it does just that. Because they are trying to measure the conservation of specific AA through evolutionary history. I think that's not such a great approach, but what can I say... The more the specific AA is conserved, that is, the more it is a part of some sequence in more species, the more probable it is that it is a part of a biological function.
As for what they are adding, well, the measure of functionality. Shannon's model of information is not good enough for this job. It enough if you want to measure how much bits you need in a communication channel, but not enough for biological functions.
quote:
It is a measure of the conservation of amino acids which takes into account the nature of the substitutions which aren't most conserved, as the Durston et al. program does, but also takes into account the physiochemical/functional properties of the amino acids by scoring substitutions based on a matrix of amino acid substitutions scores derived from a number of highly conserved protein sequences. There are different Blosum matrices depending on the similarity of the sequences under investigation. Like Durston et al.'s FSC measure it is calculated for each residue.
Hmm, well, that basicly seems like the same thing as the Durston's model. What's the measure of functionality in the BLOSUM uses?
quote:
No it didn't. Random chance at worst had to generate all of the genetic variability involved, something that mutational mechanisms do all the time.
That is basicly all of it. Do you think one part of genetic variability would come from nowhere? And no, mutational mechanisms have never been seen to increase the functional variability. They do cause differences in the genome and make different genomes, yes. But they have not been seen to create new functions.
quote:
Be that as it may it still doesn't obviate the grossly mistaken assumption that the way things are currently is the only possible functional conformation for things. So all you are calculating is the probability that exactly this form of biological complexity evolved. This sort of posterior probability calculation is completely meaningless, even if we were to accept that all the values you might wish to plug in were accurate.
But nobody said that. There are lot's of possible sequences that can code for one particular function.
https://www.youtube.com/watch?v=vUeCgTN7pOo
Here, look at this presentation by Durston himslef. He guves out a formula in which M(Ex) is the number of different configurations that can perform a specific function. It's explained in the 01:30 into the video.
quote:
There were already plenty of methods available for distinguishing totally random sequences (RSC) and highly repetitive sequences (OSC), like the examples that were given, from functional coding sequences and functional non-coding sequences. AS for the OSC example, they calculate that in a completely different way to all of the other ones, since if you actually use it with their program, i.e. give it a dozen sequences of poly-A from montmorillonite clay. it will give you a maximal FSC result.
Okay, I know. Now we have a new one, is that bad?
quote:
Indeed, with the most conservative estimate possible based on highly structurally similar proteins. And you are then adding up all of these conservative estimates to make a number with essentially no scientific meaning. One has to wonder why? It doesn't show that the system couldn't evolve. It doesn't even show that it is impossibly unlikely. It certainly isn't positive evidence for intelligent design. The only purpose of this seems to be for rhetorical purposes to generate big numbers to impress people with.
If you look at the video I posted above you will also notice that there is a limit what natural processes can do. That is, how much Fits they can produce since life began. If the number of Fits we find in living organisms exceeds the number of Fits nature can produce, than the best explanation is that an intelligence did it.

This message is a reply to:
 Message 66 by Wounded King, posted 09-07-2009 8:00 AM Wounded King has replied

Replies to this message:
 Message 68 by Wounded King, posted 09-10-2009 6:40 AM Smooth Operator has replied

  
Wounded King
Member
Posts: 4149
From: Cincinnati, Ohio, USA
Joined: 04-09-2003


Message 68 of 85 (523418)
09-10-2009 6:40 AM
Reply to: Message 67 by Smooth Operator
09-09-2009 3:20 PM


SO writes:
Yes, well, you are supposed to measure the sequences of the functions you do know what they are for. What would be the point of measuring a sequence for which you do not know a function? Maybe it's totally useless.
But while they claim you can derive an FSC value for a single biosequence they only show how to do it in the context of a pre-existing alignment. I suspect this is because outwith the modified conservation metric they have no way of setting the function variable that isn't totally arbitrary. I could identify 6 non aligning structurally diverse proteins with similar functions, would this method let me compare their FSC? The paper seem to claim it would but the method certainly doesn't and the paper doesn't make it clear how it can be used in such a way.
SO writes:
Hmm, well, that basicly seems like the same thing as the Durston's model. What's the measure of functionality in the BLOSUM uses?
It's derived from the conservation of amino acids across multiple highly conserved proteins, the matrix is weighted so amino acids with similar functional physicochemical properties are scored higher than disimilar ones. This is why I think it is superior to Durston et al's technique. They treat all substitutions as equal with only the proportions at each individual site affecting the Fits for that site, the Blosum score on the other hand takes into account the likely functional effects of a particular amino acid substitution. You can go to the PFAM database directly and see an alignment of the Ubiquitin family (amongst others), simply click on 'Alignments' in the sidebar and then on the 'View' button in the first section 'View options'. This will, after clicking through another window, bring you up an alignment with scores for consensus, conservation and quality. Consensus just shows what proportion of the sequences have the most common amino acid at that site and what it is. Conservation shows a measure similar to BLOSUM but based directly on the known physico-chemical properties of the amino-acids rather than on substitution rates. If you go here you can get an idea of the matrix of physico-chemical properties used. The Quality track is the one base off the BLOSUM62 matrix (this was derived from looking at substitution rates among aligned proteins with >62% conservation of identity. If you can get the Durston et al. program running you could use that to generate a 'Fits' track as well, I still haven't worked out a good way to distribute the analysis I did.
SO writes:
Okay, I know. Now we have a new one, is that bad?
No, just redundant. Why re-invent the wheel more crudely?
SO writes:
Here, look at this presentation by Durston himself. He gives out a formula in which M(Ex) is the number of different configurations that can perform a specific function. It's explained in the 01:30 into the video.
I have to ask, why do you think he used the equation from the Hazen et al. (2007) paper rather than his own? I suggest that it is precisely because Hazen et al. clearly state how they derive their measure of functionality.
Aside form that this is exactly what I suggested, simply an argument from big numbers where Duston plugs in lots of assumed values which are highly questionable, i.e. he uses the calculations from his paper for RecA and SecY even though he has no idea what the actual possible number of functional sequences is. He seems to have done a little bait and switch between his equation and the, albeit similar, one in the Hazen et al. paper. Durston is eliding over what Hazen et al. identify as a crucial step ...
Hazen writes:
In the preceding sections we demonstrated that the extension of functional information analysis to one-dimensional systems of letters or Avida computer code is conceptually straightforward, requiring only specification of the degree of function of each possible sequence.
Hazen writes:
The analytical challenge remains to determine the degree of function of a statistically significant random fraction of all possible configurations of the system so that the relationship between I(Ex) and Ex can be deduced.
Durston et al.'s method skips over this step and just takes the conservation of amino acid sites in PFAM alignments as a good enough estimate, which naturally leads them to overestimate the degree of specification, I can't say how much because I have no idea of what all the sequences which fulfill a specific function are.
It is the difficulty of doing this that lead Hazen et al. to use the Avida artificial life simulation as their main example, a system in which they could know in much more depth than than in an organismal system what the distribution of functional sequences was. They say ..
Hazen writes:
Note, however, that this type of random sampling is not possible with living organisms because the portion of genome space explored in an evolution experiment will be constrained by the topology of the underlying fitness landscape and the particular configuration of the environment maxima
The Hazen et al, paper is very interesting, thanks for bringing it to my attention.
SO writes:
Do you think one part of genetic variability would come from nowhere?
In as much as it is a stochastic process then yes. Genetic variability comes from errors in genetic replication and repair, from crossovers that swap domain around and from multiple other sources with no apparent source outside of the statistical nature of biochemistry and its interactions with the environment.
SO writes:
But they have not been seen to create new functions.
You would have to define 'functions' quite clearly before w e could even agree to begin to discuss this. There are numerous instances of populations gaining new functions in in vitro experiments. Antibiotic resistance and other similar examples spring immediately to mind, but the RNA polymer experiments the Hazen et al. paper refer to shows that random mutation can generate and improve functionality.
SO writes:
If you look at the video I posted above you will also notice that there is a limit what natural processes can do.
I understand that that is Durston's argument, the question is can we accept his estimates of where those limits are, I don't think we can given what he presents. Of course whether the correct response if this was true is to immediately leap to the conclusion of intelligent design is another matter which is still open for discussion.
TTFN,
WK

This message is a reply to:
 Message 67 by Smooth Operator, posted 09-09-2009 3:20 PM Smooth Operator has replied

Replies to this message:
 Message 69 by Wounded King, posted 09-11-2009 11:15 AM Wounded King has not replied
 Message 70 by Smooth Operator, posted 09-14-2009 4:31 PM Wounded King has replied

  
Wounded King
Member
Posts: 4149
From: Cincinnati, Ohio, USA
Joined: 04-09-2003


Message 69 of 85 (523594)
09-11-2009 11:15 AM
Reply to: Message 68 by Wounded King
09-10-2009 6:40 AM


Who would live in a functional sequence space like this?
I'm not sure that I'm entirely convinced by the argument but Dryden et al. (2008) make an interesting case for the functional sequence space that life on earth actually needs to have explored is much reduced compared to how it is generally calculated.
Dryden et al. writes:
We conclude that rather than life having explored only an infinitesimally small part of sequence space in the last 4 Gyr, it is instead quite plausible for all of functional protein sequence space to have been explored and that furthermore, at the molecular level, there is no role for contingency.
The areas where they argue for drastic reduction in required possible sequences are ...
As an extreme method to reduce the size of sequence space, Dill (1999) suggested that only two types of amino acid were needed to form a protein structure, hydrophilic and hydrophobic, and that furthermore it was critical to define only the surface of the protein. These two suggestions reduce the size of sequence space to 2100 and 233, respectively (i.e. approx. 1030 and approx. 1010).
...
The assumption that a protein chain needs to be at least 100 amino acids in length also rather inflates the size of sequence space when it is known that many proteins are modular and contain domains of as few as approximately 50 amino acids thereby reducing the space to 2050 or approximately 1065
This presents an interesting counterpoint to Doug Axe's estimates of the likelihood of the evolution of functional protein folds.
TTFN,
WK

This message is a reply to:
 Message 68 by Wounded King, posted 09-10-2009 6:40 AM Wounded King has not replied

  
Smooth Operator
Member (Idle past 5114 days)
Posts: 630
Joined: 07-24-2009


Message 70 of 85 (524157)
09-14-2009 4:31 PM
Reply to: Message 68 by Wounded King
09-10-2009 6:40 AM


quote:
But while they claim you can derive an FSC value for a single biosequence they only show how to do it in the context of a pre-existing alignment. I suspect this is because outwith the modified conservation metric they have no way of setting the function variable that isn't totally arbitrary. I could identify 6 non aligning structurally diverse proteins with similar functions, would this method let me compare their FSC? The paper seem to claim it would but the method certainly doesn't and the paper doesn't make it clear how it can be used in such a way.
From what I've see seen, they are only talking about the PFAM family of proteins. You need those proteins that are similar and can be aligned. It probably doesn't work any other way.
quote:
It's derived from the conservation of amino acids across multiple highly conserved proteins, the matrix is weighted so amino acids with similar functional physicochemical properties are scored higher than disimilar ones. This is why I think it is superior to Durston et al's technique. They treat all substitutions as equal with only the proportions at each individual site affecting the Fits for that site, the Blosum score on the other hand takes into account the likely functional effects of a particular amino acid substitution.
How exactly is this better than Durston's model? And how do they tell apart functional and non-functional sequences?
quote:
No, just redundant. Why re-invent the wheel more crudely?
Simply because there is time to improve it and to become better.
quote:
I have to ask, why do you think he used the equation from the Hazen et al. (2007) paper rather than his own? I suggest that it is precisely because Hazen et al. clearly state how they derive their measure of functionality.
Aside form that this is exactly what I suggested, simply an argument from big numbers where Duston plugs in lots of assumed values which are highly questionable, i.e. he uses the calculations from his paper for RecA and SecY even though he has no idea what the actual possible number of functional sequences is. He seems to have done a little bait and switch between his equation and the, albeit similar, one in the Hazen et al. paper. Durston is eliding over what Hazen et al. identify as a crucial step ...
He is explaining how he got to his equation. His work is based on Hazen's.
I don't see any assumptions. We know the number fo proteins the said structures have. We know what RecA does, so there is nothing left to assume.
Not only that, but he cited Doug Axe, who dealt with the modifications to the proteins. What he actually did, was to modify proteins in such a way to show how much change they can take, but still perform the function they did. Now we know that there is a subset that is between 10^-64 and 10^-77 of all possible sequences that will still give you the same function in the modified protein. It's at around 06:05.
quote:
Durston et al.'s method skips over this step and just takes the conservation of amino acid sites in PFAM alignments as a good enough estimate, which naturally leads them to overestimate the degree of specification, I can't say how much because I have no idea of what all the sequences which fulfill a specific function are.
Didn't Durston actually mention that whaen they measure the AAs, that they specifically deal the cutoff to certain parts of the genome, so not to inflate the number of Fits?
quote:
In as much as it is a stochastic process then yes. Genetic variability comes from errors in genetic replication and repair, from crossovers that swap domain around and from multiple other sources with no apparent source outside of the statistical nature of biochemistry and its interactions with the environment.
You do know that errors may casue variability, but no functional variability. All mutations either tune the function or degrade it. We haven't actually observed, something liek ATP syntase arise de novo. Have you got any examples?
quote:
You would have to define 'functions' quite clearly before w e could even agree to begin to discuss this.
A biological function is a process that takes in an input an gives an output within an organism. For an example. The turning of the flagellar motor is a function. Energy production form the ATP synthase is a function. Food degradation is a function.
quote:
There are numerous instances of populations gaining new functions in in vitro experiments. Antibiotic resistance and other similar examples spring immediately to mind, but the RNA polymer experiments the Hazen et al. paper refer to shows that random mutation can generate and improve functionality.
We have only seen modification to existing functions. A so called fine tuning that is already present withing the genetic code. Or a degradation. Antibiotic resistance is gained by either degradation or fine tuning of the genes. Not by producing new molecular machines and structures.
quote:
Thus, the organism develops a resistance to an antibiotic by eliminating certain systems, such as transport proteins, enzymatic activity, and binding affinity. For example, ampicillin resistance can result from an SOS response that halts cell division,66 loss of nitroreductase activity can provide resistance to metronidazole and amoxicillin,67 and kanamycin resistance can result from loss of a specific transport protein.68
A Creationist Perspective of Beneficial Mutations in Bacteria | Answers in Genesis
As you can see, all the known cases of resistance to antibiotics have been either degradations or simple fine tuning. Antibiotic resistance is not a biological function. It is an inability to perform something. There is no input or output. No new structures were produced that perform something that bacteria was not able to do before.
For an example. ATP synthase does take inputs, and produces outputs, the energy in this case. This is done by a specific molecular machine. On teh other hand, resistance is gained when something is either turned off or destroyed. You will not get any genetic variability, in the sense of more genetic functions this way. You will get genetic variability only in the fact that different bacteria have different sequences now. But the new sequence in resistent bacteria do not produce anything new.
quote:
I understand that that is Durston's argument, the question is can we accept his estimates of where those limits are, I don't think we can given what he presents. Of course whether the correct response if this was true is to immediately leap to the conclusion of intelligent design is another matter which is still open for discussion.
If you assume 4.6 billion year old Earth and you assume how many organisms have lived since than. Than you could have some idea of what nature could have produced in that time. It seems he does not say this in this video, so I understand why you said you do not agree with it.
Here, have a look at the full video:
http://www.seraphmedia.org.uk/ID.xml
quote:
This presents an interesting counterpoint to Doug Axe's estimates of the likelihood of the evolution of functional protein folds.
This would just mean that more known proteins could have been produced by chance. But the point remains that there is a limit to what nature could have produced. And that is below 10^42.

This message is a reply to:
 Message 68 by Wounded King, posted 09-10-2009 6:40 AM Wounded King has replied

Replies to this message:
 Message 71 by Wounded King, posted 09-14-2009 4:55 PM Smooth Operator has not replied
 Message 72 by Wounded King, posted 09-15-2009 6:09 AM Smooth Operator has replied

  
Wounded King
Member
Posts: 4149
From: Cincinnati, Ohio, USA
Joined: 04-09-2003


Message 71 of 85 (524162)
09-14-2009 4:55 PM
Reply to: Message 70 by Smooth Operator
09-14-2009 4:31 PM


Hey SO,
I'll try and address your post in some depth tomorrow. But just to remind you we already discussed the AIG page about beneficial mutations in some detail in [thread=-1588].
And far as I can see from looking back there your 'fine tuning' just appears to be the name you give to beneficial mutations which can also encompass maintenance and presumably even increases in information. I assume you accept that if a fine tuning mutation increases the functionality that is being used in the fitness calculation, i.e. if it were to catalyse a specific reaction at an increased rate if that were the function of the particular enzyme in question, then it could be an increase in functional information? Or would you contend that whatever the initial enzymatic rate was was the optimal rate and would therefore represent the peak possible functional information?
TTFN,
WK

This message is a reply to:
 Message 70 by Smooth Operator, posted 09-14-2009 4:31 PM Smooth Operator has not replied

  
Wounded King
Member
Posts: 4149
From: Cincinnati, Ohio, USA
Joined: 04-09-2003


Message 72 of 85 (524231)
09-15-2009 6:09 AM
Reply to: Message 70 by Smooth Operator
09-14-2009 4:31 PM


It probably doesn't work any other way.
I agree, but that isn't what they say. They claim you can apply their metric to individual biosequences and use it to compare functionally similar but structurally distinct proteins.
Durston et al. writes:
Consider further, when a biosequence is mutated, the mutating sequence can be compared at two different time states going from ti to tj. For example, ti could represent an ancestral gene and tj a current mutant allele. Different sequences sharing the same function f (as outcomes of the variables denoted respectively as Xf, Yf) can also be compared at the same time t.
Perhaps what they mean is that you can compare the FSCs of two distinct alignments having a common function. It isn't clear from the paper.
How exactly is this better than Durston's model?
It is better because it actually looks at the frequencies of substitutions in amino acids and identifies common substitutions that presumably allow functional conservation since they are maintained. In contrast as I said, Durston et al. simply look at the distribution of amino acids at each particular site, treating all amino acids as equal.
And how do they tell apart functional and non-functional sequences?
How do Durston et al.? I'm not sure if you are talking here about when the initial alignment is generated, in which case since I directed you to the PFAM database it is exactly the same functional criteria as Durston et al. use, i.e. whatever criteria PFAM used to define their structural families. If you mean how do they get the functionality criteria for individual amino acid substitutions then it is by looking at multiple highly conserved sets of sequences and looking at the distribution of tolerated amino acid substitutions and using that to infer functional physico-chemical similarities between amino acids.
Simply because there is time to improve it and to become better.
But they dont. How is their method an improvement on BLOSUM or other methods which actually consider the biological properties of the amino acids? Both of these generate a metric you could use for similar calculation to the ones Durston et al. perform.
He is explaining how he got to his equation. His work is based on Hazen's.
In this video perhaps but not in the Durston et al. paper. they don't reference Hazen's work at all, which is understandable since it apparently wasn't yet published when their paper was submitted. You might say that they both build on the work of Jack Szostak, but as I said I think that in that case Hazen did it the right way and Durston et al. failed to make their functional criteria in the least bit useful. Indeed if you look at what Szostak has written (Szostak, 2003)...
Szostak writes:
Approaches such as algorithmic complexity further define the amount of information needed to specify sequences with internal order or structure, but fail to account for the redundancy inherent in the fact that many related sequences are structurally and functionally equivalent. This objection is dealt with by physical complexity, a rigorously defined measure of the information content of such degenerate sequences, which is based on functional criteria and is measured by comparing alignable sequences that encode functionally equivalent structures. But different molecular structures may be functionally equivalent. A new measure of information functional information is required to account for all possible sequences that could potentially carry out an equivalent biochemical function, independent of the structure or mechanism used.
His key points chime with my precise concerns with Durston's work, the failure to take into account structurally dissimilar but functionally equivalent sequences. The problem is that Durston et al. don't seem to have taken the extra step necessary to move beyond looking at functional complexity over aligned sequences.
We know the number fo proteins the said structures have. We know what RecA does, so there is nothing left to assume.
Yes there is, we need to assume that we know a high enough proportion of the extant functional sequences of RecA for our estimates based on those we do know to be meaningful.
Not only that, but he cited Doug Axe, who dealt with the modifications to the proteins. What he actually did, was to modify proteins in such a way to show how much change they can take, but still perform the function they did. Now we know that there is a subset that is between 10^-64 and 10^-77 of all possible sequences that will still give you the same function in the modified protein.
I'm familiar with Axe's work. He is extrapolating from one particular functional fold in one enzyme to all of the possible functional folds of all proteins. Not only that but he is doing so based on estimates derived from a highly proscriptive experimental set up using a protein variant already mutated to put it at the borderlines of functionality. As with Durston et al. one of the big flaws with Axe's approach is that it entirely ignores the existence of structurally dissimilar proteins which can perform the same function. The probability of evolving a particular functional fold is not so relevant if there are 10 other folds out there which can perform the same function.
Didn't Durston actually mention that whaen they measure the AAs, that they specifically deal the cutoff to certain parts of the genome, so not to inflate the number of Fits?
They have a cutoff value to eliminate stretches of indels which produce gaps in the alignment. But that in no way addresses what I am talking about. The sampling they analyse is only a small subset of the possible functional variants for the sequence but they effectively assume it represents the entire functional sequence space.
You do know that errors may casue variability, but no functional variability.
This is simply not true unless you are using the word 'functional' in a highly novel way. Of course the cause functional variability, even producing a loss of function is causing functional variability.
We haven't actually observed, something liek ATP syntase arise de novo. Have you got any examples?
Not of that specifically, but looking at Selex experiments will show you that randomly generated pools of RNA oligonucleotides produce multiple functional motif including binding and catalytic activities. Subsequently sequences encoding RNAs with similar structures have been found in many organisms.
I'm not sure how you think one could force the de novo production of a catalytic activity like ATP synthase in the lab. Obviously the answer is you don't but I also don't see why you think this is relevant.
A biological function is a process that takes in an input an gives an output within an organism. For an example. The turning of the flagellar motor is a function. Energy production form the ATP synthase is a function. Food degradation is a function.
And to the extent that we can measure those functions we can incorporate them into an equation like Hazen's and maybe Durston's but I'm still not clear how. So if we found a mutation that improved motility of the flagellum would that be sufficient? What exact criteria would you use to measure flagellar functionality?
But the point remains that there is a limit to what nature could have produced. And that is below 10^42.
Even accepting that as the upper bound this still would allow the entire sequence space of a simplified amino acid repertoire to be explored for shorter sequence lengths, once functional sequences are extant their modification and recombination with other functional sequences is able to occur. Even once we have reached an agreement on upper bound we still need some agreement on the actual size of functional space that is needed to be searched, IDists tend to maximise this and perhaps evolutionists to minimise it, certainly the Dryden paper uses some pretty radical minimisation for its lowest estimates.
TTFN,
WK
Edited by Wounded King, : Messed up quote formatting

This message is a reply to:
 Message 70 by Smooth Operator, posted 09-14-2009 4:31 PM Smooth Operator has replied

Replies to this message:
 Message 73 by Smooth Operator, posted 09-20-2009 3:14 PM Wounded King has replied

  
Smooth Operator
Member (Idle past 5114 days)
Posts: 630
Joined: 07-24-2009


Message 73 of 85 (524952)
09-20-2009 3:14 PM
Reply to: Message 72 by Wounded King
09-15-2009 6:09 AM


quote:
And far as I can see from looking back there your 'fine tuning' just appears to be the name you give to beneficial mutations which can also encompass maintenance and presumably even increases in information.
Do you increase the complexity of a light switch if you flip it on or off? No, you do not. You simply tune it to a position you want. It didn't gain any new information or new functions. The same happens with some mutations. Other mutations which are deleterious reduce the information in the genome. None of them makes a gain.
To be sure. A genetic duplication does increase the Shannon information in the genome. But such a measure can not be used for biological functions. Simply because it does not take into consideration the function of the information it is measuring. Much better measure is Dembski's CSI. So no, natural causes do not increase CSI.
quote:
Perhaps what they mean is that you can compare the FSCs of two distinct alignments having a common function. It isn't clear from the paper.
They are talking about measuring differnet sequences with the same function. It can't be mutated enough to either lose, or change it's function. Because than, you would be measuring different functions.
quote:
It is better because it actually looks at the frequencies of substitutions in amino acids and identifies common substitutions that presumably allow functional conservation since they are maintained. In contrast as I said, Durston et al. simply look at the distribution of amino acids at each particular site, treating all amino acids as equal.
Are you saying that some amino acids are more important than other? From what I've read they are talking about measuring the conservation of amino acids over all sequences.
quote:
How do Durston et al.? I'm not sure if you are talking here about when the initial alignment is generated, in which case since I directed you to the PFAM database it is exactly the same functional criteria as Durston et al. use, i.e. whatever criteria PFAM used to define their structural families. If you mean how do they get the functionality criteria for individual amino acid substitutions then it is by looking at multiple highly conserved sets of sequences and looking at the distribution of tolerated amino acid substitutions and using that to infer functional physico-chemical similarities between amino acids.
Exactly. They look for conserved amino acids. This is the same thing Durston's model does. I see no difference here. This may be, as you said reinventioning the wheel. But hey, I see no problem with it if it get improved later on.
quote:
But they dont. How is their method an improvement on BLOSUM or other methods which actually consider the biological properties of the amino acids? Both of these generate a metric you could use for similar calculation to the ones Durston et al. perform.
Well it's seems that other model's metric does not take into account the functional part of the sequence. They all may be based on Shannon's information. That is what Durston was talking about in his paper.
quote:
His key points chime with my precise concerns with Durston's work, the failure to take into account structurally dissimilar but functionally equivalent sequences. The problem is that Durston et al. don't seem to have taken the extra step necessary to move beyond looking at functional complexity over aligned sequences.
But I showed you where he said that Doug Axe has done this with his experiments with proteins. And how much you can change them before they lose their function. Durston simply builds on Axe's work and plugs in Axe's numbers into his equation.
And what Szostak is actually saying here. Is that current way in which we are measuring information is not good enough. Becuse it does not take into account functional information.
quote:
Yes there is, we need to assume that we know a high enough proportion of the extant functional sequences of RecA for our estimates based on those we do know to be meaningful.
What exactly is missing?
quote:
I'm familiar with Axe's work. He is extrapolating from one particular functional fold in one enzyme to all of the possible functional folds of all proteins. Not only that but he is doing so based on estimates derived from a highly proscriptive experimental set up using a protein variant already mutated to put it at the borderlines of functionality. As with Durston et al. one of the big flaws with Axe's approach is that it entirely ignores the existence of structurally dissimilar proteins which can perform the same function. The probability of evolving a particular functional fold is not so relevant if there are 10 other folds out there which can perform the same function.
1.) Yes, that was the point of his research. To see how much change a protein can take unitll it loses it's function. You can extrapolate this on other proteins.
2.) No, the point of his work to show just that. If he mutates the proteins enough, he will show exactly how many different combinations, i.e. different sequences of proteins will work that same function. So yes, in this way you can calculate this one function even if there are 10 different protein that can do the same function.
quote:
They have a cutoff value to eliminate stretches of indels which produce gaps in the alignment. But that in no way addresses what I am talking about. The sampling they analyse is only a small subset of the possible functional variants for the sequence but they effectively assume it represents the entire functional sequence space.
It says in the paper that they deal the cutoff to those parts of the sequence so that they would not be counted as functional information. They don't assume that the whole sequence is functional.
quote:
This is simply not true unless you are using the word 'functional' in a highly novel way. Of course the cause functional variability, even producing a loss of function is causing functional variability.
I understand what you mean. I worded my sentence the wrong way. What I meant to say is that errors, i.e. mutations will not give you new biological functions. For an example, they will not produce a flagellum from something vastly different.
quote:
Not of that specifically, but looking at Selex experiments will show you that randomly generated pools of RNA oligonucleotides produce multiple functional motif including binding and catalytic activities. Subsequently sequences encoding RNAs with similar structures have been found in many organisms.
Binding and catalysis is not a biological function, it is a chemical process, and as such, a natural law. This is algorithmic information, as Szostak said where you quoted him. This kind of processes do not produce biological information.
quote:
I'm not sure how you think one could force the de novo production of a catalytic activity like ATP synthase in the lab. Obviously the answer is you don't but I also don't see why you think this is relevant.
It's very important if you want to be extrapolate changes in the biological organisms to account for all the diversity of life we observe today. If you have no observational evidene for such changes, than how can you claim that natural processes can produce them?
quote:
And to the extent that we can measure those functions we can incorporate them into an equation like Hazen's and maybe Durston's but I'm still not clear how. So if we found a mutation that improved motility of the flagellum would that be sufficient? What exact criteria would you use to measure flagellar functionality?
1.) Nope. The function is already there. It would be a simple case of fine-tuning.
2.) I like to use Dembski's CSI.
quote:
Even accepting that as the upper bound this still would allow the entire sequence space of a simplified amino acid repertoire to be explored for shorter sequence lengths, once functional sequences are extant their modification and recombination with other functional sequences is able to occur. Even once we have reached an agreement on upper bound we still need some agreement on the actual size of functional space that is needed to be searched, IDists tend to maximise this and perhaps evolutionists to minimise it, certainly the Dryden paper uses some pretty radical minimisation for its lowest estimates.
Again, here I like to use what Dembski uses. His number is derived from Seth Lloyd's work. And that's 10^120. A much higher number. This is the amount of bit operations the whole observable universe could have produced from it's origin. And that is about 15 billion years ago. So there is nothing more to explore here.

This message is a reply to:
 Message 72 by Wounded King, posted 09-15-2009 6:09 AM Wounded King has replied

Replies to this message:
 Message 74 by Straggler, posted 09-20-2009 6:53 PM Smooth Operator has replied
 Message 75 by Wounded King, posted 09-22-2009 12:07 PM Smooth Operator has replied
 Message 76 by Coyote, posted 09-22-2009 12:54 PM Smooth Operator has replied

  
Straggler
Member
Posts: 10333
From: London England
Joined: 09-30-2006


Message 74 of 85 (524976)
09-20-2009 6:53 PM
Reply to: Message 73 by Smooth Operator
09-20-2009 3:14 PM


Information
Straggler writes:
Define information
Information in general is knowledge about something.
Message 76
SO writes:
Straggler writes:
Given that you have defined an increase in information as an increase in knowledge whose knowledge was increased by the creation/formation of humanity?
The said intelligence that would have created life on Earth would be th one that increased the information content.
So you agree that information has increased. But you have defined information as an increase in knowledge. Smooth Operator whose knowledge was increased by the formation of life on Earth?
Message 82
So SO you seem to have reached the same unanswered point in a seperate thread. Whose knowledge was increased by the information increase that constitutes the formation of humanity (or life on Earth more generally)?
Edited by Straggler, : No reason given.

This message is a reply to:
 Message 73 by Smooth Operator, posted 09-20-2009 3:14 PM Smooth Operator has replied

Replies to this message:
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Wounded King
Member
Posts: 4149
From: Cincinnati, Ohio, USA
Joined: 04-09-2003


Message 75 of 85 (525202)
09-22-2009 12:07 PM
Reply to: Message 73 by Smooth Operator
09-20-2009 3:14 PM


The same happens with some mutations. Other mutations which are deleterious reduce the information in the genome. None of them makes a gain.
This is an assertion with no evidence to support it. A mutation is not a light switch. Genes do not exist in a simple binary state of on or off like a light switch. You are simply making an assertion, surely you realise that the very equations we are discussing show that mutations can increase functional information, at least in theory. You seem to be denying even the theoretical possibility of an increase in information, but it isn't clear on what basis.
So no, natural causes do not increase CSI.
Again, a blank assertion with no evidence.
They are talking about measuring differnet sequences with the same function. It can't be mutated enough to either lose, or change it's function. Because than, you would be measuring different functions.
This is why the Durston et al. measure of function is meaningless, it doesn't measure anything as they use it. The Hazen paper gives specific examples of how to measure the function of specific sequences and use that to weight the functional information. For Durston et al, where 'function' is only a proxy measure of conservation in PFAM families it is true that a single novel de novo point mutation will not increase the FSC because maximal FSC in their scheme is represented by residues with 100% conservation. So only a mutation in a population which brought the sequence further into line with the consensus sequence, or made it more similar to another sequence in the alignment, could increase the overall FSC of the whole alignment. This is entirely divorced from the actual function of the protein however, and is merely a measure of conservation.
It seems strange that the IDists who harp on about the importance of functional information don't want to produce a usable concept of function.
Are you saying that some amino acids are more important than other?
Yes, of course they are. Both in terms of biochemistry as a whole and in terms of specific amino acid sequences for individual proteins. Surely this is one obvious corrollary we can both agree on of Doug Axe's work. Some amino acid substitutions have larger functional effects than others and some amino acid positions are more sensitive to changes than others. All amino acid substitutions are far from equal. There are arguably some amino acids we could do without entirely, that is one of Dryden's main points when they say that you could produce the majority of known functional folds from a repertoire of only a handful of amino acids, in extreme cases possibly only 2.
This is the same thing Durston's model does. I see no difference here.
No it isn't if you see no difference then you are either blind or can't read. I spelled it out for you right in the paragraph you quoted. Durston et al. only look at the conservation within the PFAM alignment they are studying. BLOSUM does it on a large number of highly conserved aligned sequences to draw general functional relationships which they use to weight specific substitutions.
But hey, I see no problem with it if it get improved later on.
They aren't improving, they are ignoring the already existing methods which have improved on what they are doing.
Well it's seems that other model's metric does not take into account the functional part of the sequence.
It does to the same extent as Durston et al. do, by conservation, but they also taks into account the functional effects of the substitutions at the various amino acid positions, measured in two different ways.
They all may be based on Shannon's information.
Well so is Durston et al. look at the program they wrote and you will see that calculating shannon entropy is part of the algorithm. That doesn't mean they are only looking at shannon information. As I have argued they have more connection to functionality than the Duston et al. method, and the Hazen method has more again.
Durston simply builds on Axe's work and plugs in Axe's numbers into his equation.
And as I said, Axe's numbers are no more widely applicable than Durston's for the reasons I gave previously that you have yet to address.
And what Szostak is actually saying here. Is that current way in which we are measuring information is not good enough. Becuse it does not take into account functional information.
I agree that is what he is saying but eh is also actually sayin what it actually says in my quote. You can't just handwave away the fact that Szostak says that Functional information " is required to account for all possible sequences that could potentially carry out an equivalent biochemical function, independent of the structure or mechanism used". Which Durston et al.'s approach simply does not give us a framework for.
What exactly is missing?
Any knowledge about the full set of possible sequences functionally equivalent to RecA.
You can extrapolate this on other proteins.
Maybe you can, but that doesn't mean that such an extrapolation is reliable. Is 1 protein really a suitable proxy for all the possible proteins in existence? Not to mention it being a protein already mutated to be on the edge of functionality.
If he mutates the proteins enough, he will show exactly how many different combinations, i.e. different sequences of proteins will work that same function.
Except he won't, he isn't doing an exhaustive screen of all possible functional sequences even in that one protein. He is pushing some of the limits of functionality in one protein and extrapolating from them to the entire functionality space of all proteins.
So yes, in this way you can calculate this one function even if there are 10 different protein that can do the same function.
Please explain how he does this. We aren't talking about 10 sequence variants of one protein, we are talking about 10 totally distinct 1ary sequences with functional equivalence. How does Axe's work even begin to address the existence of these functionally equivalent proteins?
It says in the paper that they deal the cutoff to those parts of the sequence so that they would not be counted as functional information. They don't assume that the whole sequence is functional.
Saying this over and over again doesn't change anything. They quite explicitly say this is to remove indels because those indel regions could indeed inflate the FSC measure.
This is completely divorced from the concept of the entire functional sequence space that I am talking about, which would be every single possible sequence, alignable or not, which would fulfill the functionality that is being used as the F criterion in the analysis. Durston et al. assume that the PFAM family alignments are a sufficient proxy for this, but they are making a massive and obviously wrong assumption.
What I meant to say is that errors, i.e. mutations will not give you new biological functions. For an example, they will not produce a flagellum from something vastly different.
Well those are two very distinct ideas. Evolutionary theory would generally hold that it would produce a flagellum gradually from elements that are similar. There are some cases of apparent radical de novo generation of new genes, but those are rare cases. As I said before your concept of biological function doesn't accord with either Szostak or Durston's.
Binding and catalysis is not a biological function, it is a chemical process, and as such, a natural law.
Again you make your definitions up for yourself. Szostak clearly considers both of these suitable biological functions since he presumably approves the use of them as examples of such in the Hazen paper. Indeed the functional sequence that Durston et al. are happy to have pulled out in their analysis of Ubiquitins is a DNA binding site.
This is algorithmic information, as Szostak said where you quoted him.
That isn't what he said, I recommend you read it again.
Szostak writes:
Approaches such as algorithmic complexity further define the amount of information needed to specify sequences with internal order or structure
What does this have to do with catalysis and binding affinity?He talks about ...
Szostak writes:
sequences that could potentially carry out an equivalent biochemical function
But you dismiss biochemistry as mere natural law and not connected to function. You aren't talking about function as any biologist understands the term.
This kind of processes do not produce biological information.
Except they do and the Hazen paper quantifies how much in at least one instance.
It's very important if you want to be extrapolate changes in the biological organisms to account for all the diversity of life we observe today.
You don't want us to extrapolate, you want us to recapitulate the evolution of one specific functionality.
I like to use Dembski's CSI.
Unfortunately this doesn't actually let you measure anything objectively. And it is surely a measure of information rather than functionality? The two things are distinct but related in terms of functional information, but one surely cannot substitute for the other?
TTFN,
WK

This message is a reply to:
 Message 73 by Smooth Operator, posted 09-20-2009 3:14 PM Smooth Operator has replied

Replies to this message:
 Message 81 by Smooth Operator, posted 09-23-2009 12:08 PM Wounded King has replied

  
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