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Functional information defined

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What is function? What is functional information? Can it be measured?

Let’s try to clarify those points a little.

Function is often a controversial concept. It is one of those things that everybody apparently understands, but nobody dares to define. So it happens that, as soon as you try to use that concept in some reasoning, your kind interlocutor immediately stops you at the beginning, with the following smart request: “Yes, but what is function? How can you define it?

So, I will try to define it.

A premise. As we are not debating philosophy, but empirical science, we need to remain adherent to what can be observed. So, in defining function, we must stick to what can be observed: objects and events, in a word facts.

That’s what I will do.

But as usual I will include, in my list of observables, conscious beings, and in particular humans. And all the observable processes which take place in their consciousness, including the subjective experiences of understanding and purpose. Those things cannot be defined other than as specific experiences which happen in a conscious being, and which we all understand because we observe them in ourselves.

That said, I will try to begin introducing two slightly different, but connected, concepts:

a) A function (for an object)

b) A functionality (in a material object)

I define a function for an object as follows:

a) If a conscious observer connects some observed object to some possible desired result which can be obtained using the object in a context, then we say that the conscious observer conceives of a function for that object.

b) If an object can objectively be used by a conscious observer to obtain some specific desired result in a certain context, according to the conceived function, then we say that the object has objective functionality, referred to the specific conceived function.

The purpose of this distinction should be clear, but I will state it explicitly just the same: a function is a conception of a conscious being, it does not exist  in the material world outside of us, but it does exist in our subjective experience. Objective functionalities, instead, are properties of material objects. But we need a conscious observer to connect an objective functionality to a consciously defined function.

Let’s make an example.

Stones

I am a conscious observer. At the beach, I see various stones. In my consciousness, I represent the desire to use a stone as a chopping tool to obtain a specific result (to chop some kind of food). And I choose one particular stone which seems to be good for that.

So we have:

a) The function: chopping food as desired. This is a conscious representation in the observer, connecting a specific stone to the desired result. The function is not in the stone, but in the observer’s consciousness.

b) The functionality in the chosen stone: that stone can be used to obtain the desired result.

So, what makes that stone “good” to obtain the result? Its properties.

First of all, being a stone. Then, being in some range of dimensions and form and hardness. Not every stone will do. If it is too big, or too small, or with the wrong form, etc., it cannot be used for my purpose.

But many of them will be good.

So, let’s imagine that we have 10^6 stones on that beach, and that we try to use each of them to chop some definite food, and we classify each stone for a binary result: good – not good, defining objectively how much and how well the food must be chopped to give a “good” result. And we count the good stones.

I call the total number of stones: the Search space.

I call the total number of good stones: the Target space

I call –log2 of the ratio Target space/Search space:  Functionally Specified Information (FSI) for that function in the system of all the stones I can find in that beach. It is expressed in bits, because we take -log2 of the number.

So, for example, if 10^4 stones on the beach are good, the FSI for that function in that system is –log2 of 10^-2, that is  6,64386 bits.

What does that mean? It means that one stone out of 100 is good, in the sense we have defined, and if we choose randomly one stone in that beach we have a probability to find a good stone of 0.01 (2^-6,64386).

I hope that is clear.

So, the general definitions:

c) Specification. Given a well defined set of objects (the search space), we call “specification”, in relation to that set, any explicit objective rule that can divide the set in two non overlapping subsets:  the “specified” subset (target space) and the “non specified” subset.  IOWs, a specification is any well defined rule which generates a binary partition in a well defined set of objects.

d) Functional Specification. It is a special form of specification (in the sense defined above), where the rule that specifies is of the following type:  “The specified subset in this well defined set of objects includes all the objects in the set which can implement the following, well defined function…” .  IOWs, a functional specification is any well defined rule which generates a binary partition in a well defined set of objects using a function defined as in a) and verifying if the functionality, defined as in b), is present in each object of the set.

It should be clear that functional specification is a definite subset of specification. Other properties, different from function, can in principle be used  to specify. But for our purposes we will stick to functional specification, as defined here.

e) The ratio Target space/Search space  expresses the probability of getting an object from the search space by one random search attempt, in a system where each object has the same probability of being found by a random search (that is, a system with an uniform probability of finding those objects).

f) The Functionally Specified  Information  (FSI)  in bits is simply –log2 of that number. Please, note that I  imply  no specific  meaning of the word “information” here. We could call it any other way. What I mean is exactly what I have defined, and nothing more.

One last step. FSI is a continuous numerical value, different for each function and system.  But it is possible to categorize  the concept in order to have a binary variable (yes/no) for each function in a system.

So, we define a threshold (for some specific  system of objects). Let’s say 30 bits.  We compute different values of FSI for many different functions which can be conceived for the objects in that system. We say that those functions which have a value of FSI above the threshold we have chosen (for example, more than 30 bits) are complex. I will not discuss here how the threshold is chosen, because that is part of the application of these concepts to the design inference, which will be the object of another post.

g) Functionally Specified Complex Information is therefore a binary property defined for a function in a system by a threshold. A function, in a specific system, can be “complex” (having  FSI above the threshold). In that case, we say that the function implicates FSCI in that system, and if an object observed in that system implements that function we say that the object exhibits FSCI.

h) Finally, if the function for which we use our objects is linked to a digital sequence which can be read in the object, we simply speak of digital FSCI: dFSCI.

So, FSI is a subset of SI, and dFSI is a subset of FSI. Each of these can be expressed in categorical form (complex/non complex).

Some final notes:

1) In this post, I have said nothing about design. I will discuss in a future post how these concepts can be used for a design inference, and why dFSCI is the most useful concept to infer design for biological information.

2) As you can see, I have strictly avoided to discuss what information is or is not. I have used the word for a specific definition, with no general implications at all.

1030743_72733179

3) Different functionalities for different functions can be defined for the same object or set of objects. Each function will have different values of FSI. For example, a tablet computer can certainly be used as a paperweight. It can also be used to make complex computations. So, the same object has different functionalities. Obviously, the FSI will be very different for the two functions: very low for the paperweight function (any object in that range of dimensions and weight will do), and very high for the computational function (it’s not so easy to find a material object that can work as a computer).

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4) Although I have used a conscious observer to define function, there is no subjectivity in the procedures. The conscious observer can define any possible function he likes. He is absolutely free. But he has to define objectively the function, and how to measure the functionality, so that everyone can objectively verify the measurement. So, there is no subjectivity in the measurements, but each measurement is referred to a specific function, objectively defined by a subject.

Comments
Piotr:
The “rugged landscape” paper says nothing about the estimated proportion of functional proteins in the search space. You quote-mine it by truncating the final sentence in your quotation and omitting the explanation that follows. And of course the general tone of the article is far from pessimistic.
It's not quote mining. It's deliberate non inclusion of a statement which is not an explanation and which has nothing to do with the data in the paper. Here is what I "truncated": "The question remains regarding how large a population is required to reach the fitness of the wild-type phage. The relative fitness of the wild-type phage, or rather the native D2 domain, is almost equivalent to the global peak of the fitness landscape. By extrapolation, we estimated that adaptive walking requires a library size of 10^70 with 35 substitutions to reach comparable fitness. Such a huge search is impractical and implies that evolution of the wild-type phage must have involved not only random substitutions but also other mechanisms, such as homologous recombination. Recombination among neutral or surviving entities may suppress negative mutations and thus escape from mutation-selection-drift balance. Although the importance of recombination or DNA shuffling has been suggested [30], we did not include such mechanisms for the sake of simplicity. However, the obtained landscape structure is unaffected by the involvement of recombination mutation although it may affect the speed of search in the sequence space." The second part has absolutely no justification in the data of the paper, so I simply omitted it. And it is no "explanation" at all. What does it explain? With phrases such as "may suppress", "escape from mutation-selection-drift balance", "has been suggested", "we did not include such mechanisms for the sake of simplicity", "it may affect"? Good "explanations" indeed! And the “rugged landscape” paper says a lot about the structure of the protein functional landscape, and it says that the functional island of the wild type protein was not found, and could never be found with reasonable resources by random libraries and natural selection, not even NS in its most favorable context. More in next post.gpuccio
May 8, 2014
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Piotr, That is evolution by design regardless of what was expected, what occurred and what humans can or cannot do. Artificial = designed. It definitely has nothing to do with unguided evolution, including natural selection.Joe
May 8, 2014
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e) No case of macroevolution (evolution of a complex function by RV + NS) has ever been observed.
gpuccio, You've offered a different way of thinking about the difference between macro- and micro- evolution, and that has some implications. Traditionally macroevolution is shorthand for the dividing line between species and genus. For example the Canidae family can be divided into 11 different genera. In turn the genus Canis can be divided into 6 different species, of which the domesticated dog is considered to be a sub-species of the gray wolf. Your definition implies that there must be at least one complex functional difference that separates Canis from any other related genus, like Vulpes. Further, there must not be even one complex functional difference between related species, like the gray wolf and the coyote, let alone between dog breeds. Personally, it wouldn't surprise me if there happened to be a lone complex functional difference between certain dog breeds. If so, then this would be a case of RV originating new information (though artificial selection was critical to the creation of a sub-species population). Do you know if there is any genetic evidence to support your concept of macroevolution to the application of Biological classification?rhampton7
May 8, 2014
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The last (2) should be (3), of course. Press "Post" in haste, repent at leisure.Piotr
May 8, 2014
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Yes Piotr, that is evolution by design. O no, Joe, no, Joe, no, Joe, no. It's artificial evolution, but the solution found by the genetic algorithm was: (1) unexpected, (2) baffling (the experimenters themselves had problems understanding the evolved functions (such as self-timing, compensating for the absence of a clock signal), (2) not of the kind a human designer would even think of (one part of the circuit was disconnected and yet essential for its functioning.Piotr
May 8, 2014
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That’s how Szostac “evolved” his (completely useless) ATP binding protein starting with a random sequence with a minimal capacity of binding ATP and applying rounds of random variation followed by intelligent separation to it.
Szostak had no ambition to evolve a protein that really does something in a living cell, but one that does something specified in advance (just one concrete function, not any function). An experimental setting is of course artificial because the purpose of an experiment is not to replicate reality but to understand how things happen by artificially controlling the number of variables. Proton beam collisions in the LHC are also artificial. So are the observed Higgs boson events. It doesn't mean that the Higgs exists only at CERN.Piotr
May 8, 2014
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Gpuccio: The "rugged landscape" paper says nothing about the estimated proportion of functional proteins in the search space. You quote-mine it by truncating the final sentence in your quotation and omitting the explanation that follows. And of course the general tone of the article is far from pessimistic. There's randomness and randomness. Mutations, recombinations, frameshifts, deletions, inversions, etc. are "random" with regard to the adaptive value of their consequences. But they are not equally probable. Besides, the structure of the genome reflects its history in ways that may lead to a pro-functionality bias. Let's imagine that a point mutation disables a functional gene and turns it into a pseudogene -- one type of "junk DNA". A reverse point mutation (which is not astronomically unlikely) may then restore its functionality, "creating" a complete gene out of junk (but not random junk). Such latent functionality is invisible to natural selection, but is important if you want to calculate the likelihood of the emergence of function. It may increase the odds of getting something functional by God knows how many orders of magnitude. An algorithm is a step-by-step computation procedure. Neither drift nor selection are "algorithms" in the ordinary sense of the word. They are aspects of the evolution of populations, which is not an algorithm either. It can be modelled algorithmically (to an approximation), which justifies the metaphor but doesn't make the evolutionary process a sequence of calculations. The orbital motions of planets can also be so modelled, but nobody calls them algorithms. Drift can affect the emergence of functions by allowing populations (especially small ones) to "escape" from local peaks of fitness. Who says that proteins evolve only through tiny local "improvements", and that the effect of such minimal changes must be modest? The relationship between DNA sequence and protein function is non-linear. For example, small changes in the phylogenetically old and highly conserved FOXP2 gene are believed to have contributed importantly to the development of speech in humans. The FOXP2 protein regulates the expression of numerous other important genes (which is precisely the reason why it's so conserved). We differ from chimps by two amino acids in the protein, due to two non-synonymous substitutions in our lineage. We share one of those point mutations with Carnivora; only the other is really unique to humans (including neanderthals and denisovans), but I wouldn't describe the effect as minor. If a gene gets duplicated, and one copy is free to drift away from its original role, the effect can be quite dramatic. We and our primate relatives owe our trichromatic vision to a rather trivial duplication event followed by divergence between the original gene and its copy. I hope you don't deny the possibility of such a process. I mentioned in one of the earlier posts internal duplications, producing a protein that may retain its original functions while developing a potential for secondary ones -- not unlike gene duplications.Piotr
May 8, 2014
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Piotr: Let's be very clear: Joe is perfectly right. The application of RV followed by intelligent selection (IS) is a design strategy. And it is very efficient! That's how the best results in protein engineering are obtained: bottom up engineering. That's how antibody affinity increases in a few months after the first immunological response. That's how Szostac "evolved" his (completely useless) ATP binding protein starting with a random sequence with a minimal capacity of binding ATP and applying rounds of random variation followed by intelligent separation to it. Intelligent selection is very powerful. It starts from a clear knowledge of what function is desired, and applies RV to some suitable starting state, and measures repeatedly the desired function with very sensitive methods, so that it may be detected even in minimal form, and then selects and applies variation rounds to the selected results, each time amplifying exponentially the selected, desired improvements. Yes, RV + IS is a very powerful design strategy to obtain the desired result.gpuccio
May 8, 2014
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Yes Piotr, that is evolution by design.Joe
May 8, 2014
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Could you please clarify to what you are referring here?
Things like Adrian Thompson's evolvable voice-discriminating circuit: http://classes.yale.edu/fractals/CA/GA/GACircuit/GACircuit.html http://www.damninteresting.com/on-the-origin-of-circuits/ Thompson, A., P. Layzell, and R.S. Zebulum. 1999. Explorations in design space: unconventional electronics design through artificial evolution. IEEE Transactions on Evolutionary Computation 3: 167-196.Piotr
May 8, 2014
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Piotr at #145: I find this post rather vague. Your "complex and messy process " can and must be analyzed in its components, if we want to understand it. It's strange how non design theorists (including you) recur to the vague idea of a "complex and messy process" any time design theorists (including me) try to understand and model the process. OK, let's see. a) All kind of random variation are random. Mutations, recombinations, frameshifts, deletions, inversions, you name it. The result of random variation is that new states appear. The probabilistic resource of RV can well be describes as the total numeber of new states that can be generated in a system, in a time span. As I have tried to do. There is nothing messy in that. b) Drift is an algorithm which acts randomly on random variation. It expands algorithmic some new state, but it is completely random in the "selection" of which new state expands. So, it does not change the probabilities. Each new state still has the same probabilities to expand, There is no relationship with function. There is nothing messy in that. c) NS is the supposed process according to which new states which confer a reproductive advantage (versus the old states) are preferentially expanded and fixed. While deleterious new states are preferentially eliminated. This kind of process is algorithmic in relation to the function of the new state (it depends on it). But it can act only on those variations (new states) which confer a reproductive advantage (or disadvantage). For all the rest of the "new states" only random factors apply. d) The only examples really observed of positive NS are those few cases of microevolution where the variation is minimal (1-2 AAs), it is usually at the expense of an existing function, and confers an advantage only because of extreme environmental pressure. Simple anitibiotic resistance is a good example. e) No case of macroevolution (evolution of a complex function by RV + NS) has ever been observed. f) Complex functions are not deconstructable into simpler, functional, additive steps. That is not true for complex language (which depends on the meaning to be expressed), and it is not true for complex functionalities (which sepend on the function to be expressed). Least of all it is true for proteins, which are separated in isolated islands of sequences and structures (as can be seen in the SCOP classification), and cannot be deconstructed into simpler additive steps which are individually not only functional , but also naturally selectable. g) The number I toss about are based on what we know of biology, of proteins and of cells. They are based on the work of Behe and Axe. They are based on the SCOP classification of proteins. They are based on papers like the rugged landscape paper: "Experimental Rugged Fitness Landscape in Protein Sequence Space" http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0000096 which clearly shows the limits of NS and the properties of the functional landscape of proteins: "The question remains regarding how large a population is required to reach the fitness of the wild-type phage. The relative fitness of the wild-type phage, or rather the native D2 domain, is almost equivalent to the global peak of the fitness landscape. By extrapolation, we estimated that adaptive walking requires a library size of 10^70 with 35 substitutions to reach comparable fitness. Such a huge search is impractical and implies that evolution of the wild-type phage must have involved not only random substitutions but also other mechanisms" IOWs, they are based on science. Finally, I don't understand your last paragraph:
You can also try to see if an artificial genetic algorithm can produce functional complexity — of a kind neither designed nor predicted, by the programmer — under selective pressure. It can, as has been demonstrated, and the functionality achieved in this way is like that found in nature but not in human products: highly economic and dispersed in the system in a way that makes it hard to analyse and divide into discrete modules. It isn’t the way intelligent designers of the only kind known to me (humans) do their job.
Could you please clarify to what you are referring here?gpuccio
May 8, 2014
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Piotr @120:
There’s no way you can resolve this question unless somebody tells you whether it is part of coding DNA (it could be many other things, from a regulatory sequence to random junk), how the reading frame should be adjusted for the proper identification of codons, and whether the resulting amino acid sequence could be a fragment of a kind of protein known to be functional is some way. Once you have that information, you can feed it into your “explanatory filter” and — hurrah! — it says DESIGNED.
Let's assume, for sake of argument, that your description is correct. Let's assume that before we apply the explanatory filter we need to have some objective -- empirical -- assessment of the object in question. Why do you think the explanatory filter would kick out "DESIGNED"? (It would, but I'm asking you to think through why it would.) Here is the key, I repeat yet again, that you seem to be missing or glossing over: The explanatory filter is not primarily in the business of determining whether we have function. It is interested in inferring the likely origin. Function is often either self-evident, or empirically observable, or can be gleaned after some study and research. Then the question is: "How did this function arise? What is the most likely explanation for its origin?" You recognize and acknowledge that the explanatory filter will produce an answer of "designed" if we are dealing with a complex functional system. Great. That's all it is intended to do. It is performing its task properly. Welcome aboard! You are absolutely right that if we discover (or are told about) a complex, functional system, then we can infer design. That is the whole point. It is very simple, almost a "Well, Duh!" or a "hurrah!" as you have expressed it. It is so obvious that most of the time in our lives we don't even think through the specifics of the explanatory filter. So when we see such complex functional systems in living organisms, the correct inference -- the "hurrah!" as you rightly point out -- is "DESIGNED."Eric Anderson
May 8, 2014
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Dionisio, @142 Sorry, but I am no expert in biochemistry or developmental biology, so I can't provide you with a detailed description of the kind you need. I teach linguistics, and my opinions expressed here are those of a dilettante interested in biology. But this blog is visited from time to time by professionals who would probably be able to help you. Gdansk is a wonderful place -- one of my favourite cities. Next time I go there we can try to fix a meeting.Piotr
May 8, 2014
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Gpuccio: Is the sampling of the genetic pool from from generation to generation, by a combination of selection and drift, an algorithm? Are mutations, recombination, linkage, the history of the genome reflected in its structure, inbreeding, migration, environmental variation, etc. part of that algorithm? You can use a stochastic algorithm to model simple things like the distribution of competing alleles in an idealised population, but there's no easy way to estimate the probability that the complex and messy process we call evolution will produce something functional in the timescales available to it. The numbers you toss about -- like "let's say 100 proteins" -- are based on your intuitive judgement and represent nothing else than your private opinion. Why 100 rather that 10^10, or 10^90, or 10^180, or any other number? Where did the number come from? The universe of aperiodic polymers (and their potential functions) is vast. You can also try to see if an artificial genetic algorithm can produce functional complexity -- of a kind neither designed nor predicted, by the programmer -- under selective pressure. It can, as has been demonstrated, and the functionality achieved in this way is like that found in nature but not in human products: highly economic and dispersed in the system in a way that makes it hard to analyse and divide into discrete modules. It isn't the way intelligent designers of the only kind known to me (humans) do their job.Piotr
May 8, 2014
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PS: As another quick note, as indicated already, all that is required, BTW, for the design inference, is LOCAL isolation of islands of function in the config space, as that brings up fine tuning beyond the search capacity of relevant resources; cf. John Leslie's lone fly on a stretch of wall swatted by a bullet point, where that elsewhere there may be portions carpeted with flies (what an ugly picture . . . ) that pose no fine tuning issue. BTW, too, that speaks to cosmological fine tuning, and to just what it takes to set up a tack-driver of a rifle and a shooter able to make good use of it. At 500 bits, Solar system is overwhelmed [a 1 straw sample to a 1,000 LY cubical haystack], at 1,000 observed cosmos is simply drowned out to effectively zero -- no time to smoke a calculator on the calc this morning. The presence of ever so many singleton proteins and the isolation between fold domains fits right in here. Sorry to be so short, gotta go now, back to G-T power and T-s diagrams, binary vs flash plants etc.kairosfocus
May 8, 2014
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P: Pardon, but kindly cf. here -- and yes, that is notoriously materialistic Wiki speaking against interest. If you have to try to argue that design is so vague it can be dismissed speaks volumes. Just as a quick clue, every comment in this thread manifests dFSCI, and that is instantly recognisable. KFkairosfocus
May 8, 2014
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Piotr, [Off topic] gpuccio and other folks in this blog have graciously given me useful suggestions and provided links to sources of information I've started to use. But more info is still required, hence any help is highly welcome and appreciated. Since you're a scientist who works at a university, I assume you or someone within your professional network could provide some assistance. Dzieki! And please, don't forget my invitation 'na herbate albo kawe!' next time you go to Gdansk. You may contact me at dshared@ymail.com to coordinate details 'po polsku' (Polish language characters work fine on emails).Dionisio
May 8, 2014
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Piotr: Let's go to other aspects of your reasoning:
Design does not explain anything because it’s a nebulous, undefinable and untestable solution. It’s a synonym of our ignorance: we don’t know the details of the process, therefore design.
Not at all. The origin of dFSCI from conscious agents, and only from conscious agents, is absolutely grounded in innumerable empirical observations. We have not yet debated this aspect here, but we certainly will, before the end of it. The design inference is a strong, positive empirical inference.
Gpuccio’s target space is not definable even in principle, since it’s impossible to predict all possible “biological functions”.
This is the usual "any possible function" objection. For a simple answer, please look at my post #23, part of which I paste here for your convenience: "For the moment, I want to specify: a) That my definiton is relative to some specified function, not to any possible function. I will deal with the “any possible function” perspective, often raised by darwinists, when I will discuss the design inference for proteins. The FSI value is the improbability of getting that function in that system and in that search space, by one attempt and with a uniform probability distribution. I can anticipate that the “any possible function” argument is not valid, because in a neodarwinian perspective what we need is: - a function which is useful in the complex context of the existing cell - a function which is coordinated (in terms of quantity, sequence and location and regulation) with the complex context of the existing cell - a function which is so useful that it confers detectable reproductive advantage, and can be expanded and fixed by NS. That reduces the space of candidate functions a lot. Now, just consider that almost 4 billion years of evolution, whatever the causal mechanism, have found about 2000 basic functional structures (superfamilies) in the huge search space of protein sequences. And that new superfamilies appear at a constantly slower rate. We will see, when we debate the design inference for proteins, that even if many new complex functions were potentially available in a specific context, the result would not change much. For example, let’s say that 100 new protein structures of about 150 AAs are potentially useful in a cellular context (which, IMO, is highly unlikely), and that each of them has a functional complexity of 300 bits, and therefore a probability of being found of 1e-90. So, we have a search space of 1e195 (20^150), and we are assuming a target space of about 1e105. Let’s say that we want the probability of finding one of the 100 functional proteins, instead of the probability of finding one specific protein. We have to sum the 100 target spaces, while the search space remains the same. So, the total target space will be 1e105*10^2=1e107. So, the probability of finding one of the useful proteins has changed of only two orders of magnitude, and it is now 1e-88. Not a great problem, for the design inference." More in next post.gpuccio
May 8, 2014
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Piotr: Where did I say that "origin by design" is an algorithm? "Origin by design" means only one thing: that the specific form we observe, with its order, or meaning, or functionality, was first represented subjectively in the consciousness of a conscious being, and then outputted to the material object. The only algorithmic part is the chronological and most probably causal, relationship between the conscious representation and the form in the material object. Again, you can find the details in my post about design: https://uncommondescent.com/intelligent-design/defining-design/ We infer design because: a) A random origin of the pattern is rejected by the observation of dFSCI b) No known credible algorithm can explain the origin of the pattern in the system. c) The only known origin of dFSCI is a design process (this is the part I have not detailed yet, at least in this thread). It's as simple as that.gpuccio
May 8, 2014
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Piotr, Please, don't ask me, as someone else did in another thread, to Google it, because I've done gazillion Goodling to no avail. I want detailed coherent comprehensive step-by-step descriptions. In software development I can't get away with missing links. Next time you visit Gdansk, stop by for herbata or kawa. Zapraszam serdecznie.Dionisio
May 8, 2014
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Piotr:
Design does not explain anything because it’s a nebulous, undefinable and untestable solution.
Yet IDists have said how to test for design and other design-centric venues have shown us the importance of determining design is present.
It’s a synonym of our ignorance: we don’t know the details of the process, therefore design.
Now THAT is your ignorance showing. Design is based on our KNOWLEDGE of cause and effect relationships. OTOH darwinian and neo-darwinian evolution are based on our ignorance.Joe
May 8, 2014
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Piotr, Apparently Polish language-specific characters are not recognized by the text processor here. But that's fine, because this is a serious blog, where you scientists and other science-lovers discuss serious issues mainly in English, though when I read what gpuccio, you and others wrote in this thread, I wonder if that's really English, because the used terminology sometimes flies high over my poor ignorant mind ;-) Anyway, I'm not as interested in the OOL discussion as I'm in the detailed description of currently existing processes, so that I can represent them in a 4D simulation software (in silico) for interactive education. Currently, for example, I'm trying to focus in on the mechanisms behind the cell fate determination, differentiation, migration, that take place during human development, from fertilization to birth. Also, the mechanisms behind the genotype-phenotype association. Also the mechanisms behind neurological processes. Also, the detailed mechanisms behind physiological processes. This software development project requires accuracy in the descriptions, because it's for writing the detailed programming specs. Is this something you or someone you know could provide helpful information or point to sources? Dzieki! P.S. obviously, if y'all here can also provide a coherent explanation on how those processes came to be, I wouldn't mind to read it too, but that's not required for my software project. Wszystkiego najlepszego! Serdecznie pozdrawiam.Dionisio
May 8, 2014
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However, the short conclusion is that neo darwinism is not a known and credible algorithm which can explain the origin of even one protein superfamily. It is neither known nor credible. And I am not aware of any other algorithm ever proposed to explain (without design) the origin of functional, non regular sequences.
Leaving for a moment the question whether my own views can be described as "neo-darwinian" (I don't think so), let me just briefly note, for the record, that "origin by design" (performed by a basically unknown but probably supernatural and practically omnipotent intelligent entity) is not an "algorithm" at all. Design does not explain anything because it's a nebulous, undefinable and untestable solution. It's a synonym of our ignorance: we don't know the details of the process, therefore design.Piotr
May 8, 2014
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Piotr: You have misunderstood me! I was not suggesting that you are a strict neutralist. :) Perhaps I have expressed myself badly. I was only making a reasoning, and I was "dramatizing" between "you" and "me" (which were rather impersonal actor) the hypothesis testing process. So, I assigned to "you" the role of "supporter" of the null hypothesis (the effect we observe is due to chance). I apologize for coopting you in my reasoning! :) Seriously, I am perfectly aware of the role of NS in the neo darwinian model. I quote from my post #57 here:
You may be aware that excluding a necessity (algorithmic) explanation is an integral part of ID from the beginning, it’s already there in Dembski’s explanatory filter. Complexity due to order is often (but not always) generate by algorithms. That is not true of complexity due to function (prescriptive information) or to meaning (descriptive information). Those types of complexity are scarcely compressible, and cannot be generated by simple algorithms. I have not dealt with this part in detail, in this thread, but it is an important part. It includes explaining why protein sequences can never be generated by NS acting on RV.
Well, it seems that I have to deal with this part in detail. :) In brief, my reasoning for the design detection by dFSCI is only an application of the principles of Dembski's explanatory filter. Beware, now I will only give a brief outline of the reasoning. In this post: a)I am not giving all the details of the process b)I am not explaining the empirical verification of why it is 100% specific. We can do those two things later. Let's proceed one step at a time. So, the outline: 1) We observe some object with some digital content (I will stick to digital information, but the concepts can be applied also to analog information). We don't know directly if it is the product of design or not. For my definition of what design is, please see here: https://uncommondescent.com/intelligent-design/defining-design/ 2) We recognize / observe a function for the object, in particular for its digital sequence, and we define it objectively, and define objectively how to measure / assess it. 3) Considering what we know of the system where the object arose, the time span in which it arose, and the probabilistic resources of that system, and we (no more treacherous cooption of innocent bystanders! :) ) formulate the null hypothesis that the object and its particular sequence originated in the system randomly. 4) Analyzing what we know of the system, time span and probabilistic resources, we set an appropriate threshold of functional complexity for that system. If we don't know better, we can always use Dembski's UPB of 500 bits, which is appropriate for our whole universe and its whole time span. 5) We try to make an assessment (usually by some indirect method) of the dFSI for that function. If it is higher than our threshold, we say that the object exhibits dFSCI for that system. 6) If our conclusion is yes, we must still do one thing. We observe carefully the object and what we know of the system, and we ask if there is any known and credible algorithmic explanation of the sequence in that system. Usually, that is easily done by excluding regularity, which is easily done for functional specification. However, as in the particular case of functional proteins a special algorithm has been proposed, neo darwininism, which is intended to explain non regular functional sequences by a mix of chance and regularity, for this special case we must show that such an explanation is not credible, and that it is not supported by facts. That is a part which I have not yet discussed in detail here. The necessity part of the algorithm (NS) is not analyzed by dFSCI alone, but by other approaches and considerations. dFSCI is essential to evaluate the random part of the algorithm (RV). However, the short conclusion is that neo darwinism is not a known and credible algorithm which can explain the origin of even one protein superfamily. It is neither known nor credible. And I am not aware of any other algorithm ever proposed to explain (without design) the origin of functional, non regular sequences. 7) If we have assessed that the object exhibits dFSCI in that system, and that there is no known and credible algorithm in that system which can explain its functional sequence, we make a design inference for the origin of that object's functional information in that system. That means that as far as we know design is the best scientific explanation of what we observe. So, to sum up, dFSCI is necessary to evaluate and eventually reject the role of RV, both as the only cause of what we observe and as part of the specific neo darwinian algorithm. dFSCI alone cannot be used to reject the proposed role of NS. Other considerations are needed for that. Well, this is the brief outline. :) Now, let's discuss it. There are other aspects of your last posts which I want to discuss, obviously, but I wanted first to give you a complete scenario of what we are debating here.gpuccio
May 8, 2014
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Hej, Dionisio, mi?o mi ci? spotka?. Mieszkam w Poznaniu i pracuj? na Uniwersytecie Adama Mickiewicza. [let's see if Polish diacritics work here]Piotr
May 7, 2014
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Gpuccio: It's too late for long replies, so just one small thing before I go to sleep:
Now, you suggest that the protein originated by chance /the null hypothesis).
Nope. I don't suggest that proteins normally originate as completely random amino acid sequences that serendipitously acquire a function (though chance surely plays a role in the process). First, old proteins evolve and undergo selection, and selection is not random. Even de novo proteins do not have to be entirely random. After all, a good proportion of our junk DNA consists of former functional sequences (like decaying pseudogenes, disabled and fragmented ex-genes that used to encode for viral proteins, etc.). I am not an expert, but it seems intuitively possible that this genetic flotsam and jetsam, although generally non-functional, is full of "cryptic functionality" (meaning that its easier to recycle than truly random sequences). That, after all, is why we call it "junk", not "garbage". You may ask about the very first proteins, at the beginning of life as we know it. I have no idea how they originated, but totally random self-assembly is not a likely solution, and no modern OOL hypothesis known to me takes such a possibility seriously. It's a topic for a different discussion, anyway, perhaps in a new thread. Once we get life working and evolving, nothing originates from scratch any more. New structures are built on pre-existing ones. Calculating the probability of the formation of functional proteins as if they had no history is absurd. Purely random origin is not so much the null hypothesis as a straw man.Piotr
May 7, 2014
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Piotr, gpuccio napisa? ze mieszkasz w Polsce? Ja mieszkam w Gda?sku i na Florydzie. Troch? tam, troch? tu. Gdzie w Polsce mieszkasz? Polski nie jest mój pierwszy j?zyk.Dionisio
May 7, 2014
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gpuccio @ 129
More in next post.
I look forward to reading your next post on this highly interesting subject. Good job! Mile grazie mio caro amico.Dionisio
May 7, 2014
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Piotr at #116: Please, don't misunderstand me. I am well aware that protein engineering, both top down and bottom up, has made great progress. I am aware of those small results. And I am sure that, in time, we will be able to engineer proteins very well. In no way I am trying to diminish the capabilities of humans to design proteins, which are indeed a very good argument for the design theory. If I wrote: "Designing a true working protein algorithmically is so difficult that we still cannot do that, not even with all our “intelligence”. And even if we could, the algorithm would be infinitely more complex than the final sequence." it's because I think that describes well our present state. Please note that I said "Designing a true working protein". The results listed in the Wikipedia page are important, but are really far form what we usually mean by "a true working protein". For example, the first paper referenced by wikipedia concludes: "Although our results demonstrate that novel enzyme activities can be designed from scratch and indicate the catalytic strategies that are most accessible to nascent enzymes, there is still a significant gap between the activities of our designed catalysts and those of naturally occurring enzymes. Narrowing this gap presents an exciting prospect for future work" Again, I am not trying to underestimates these results. And I see no potential limitations to our protein engineering abilities. We just need more time. But my point was different. My point was: "And even if we could, the algorithm would be infinitely more complex than the final sequence." That remains true even if we engineer perfect proteins. The algorithm will be extremely complex, it will require a lot of intelligent premises and a lot of highly directed computational power. It will not be a "compression" of the sequence complexity, in any sense. OK, I think that's enough for now. Time for sleep.gpuccio
May 7, 2014
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Piotr:
The problem is that “the explanatory filter” is quite helpless unless I inform you first where this pattern came from — how it arose and what function it really plays. Once you know that, you won’t get a false positive, but it’s no big deal if you know the answer in advance.
Simply false. I don't need to know how the pattern originated. I need to know, as I have already stated clearly: The search space, which for sequences can be easily computed. The physical system, the time span and the probabilistic resource of the system: but that is necessary not to define the function, but simply to evaluate the null hypothesis that the object originated by chance. Let's make an example. I have a protein of a certain length, and I observe / define its biological function. OK? I am not asking "how it arose". Not at all. That is the answer I want to find. Now, you suggest that the protein originated by chance /the null hypothesis). And I ask: "OK, let's evaluate your null hypothesis. But I need to know where the proteins originated (the system), in what time span, and what are the random variations which can take place in that system in the time span (the probabilistic resources). Please note, I am not asking "how it arose". I am asking: "how do you think it arose by chance?" IOWs, I am clarifying the context of the null hypothesis according to what we know and what you assume. The only purpose is to evaluate if the null hypothesis can be rejected. In no way I am asking "how it arose". In no way I am trying to "know the answer in advance". That is completely false. And again, I am not asking "what function it really plays". I can try to understand that by myself. Or you can say that to me, if you know it. It does not matter. If I recognize a function, by myself or with your help, I will try to compute its complexity and, if the complexity is high enough to reject the null hypothesis, I will reject it. If no algorithmic explanation of the sequence in the system is known, or even plausible, I will infer design. And believe me, I will have no false positives. But I never asked "how it arose". More in next post.gpuccio
May 7, 2014
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