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Controlling the waves of dynamic, far from equilibrium states: the NF-kB system of transcription regulation.

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I have recently commented on another thread:

about a paper that (very correctly) describes cells as dynamic, far from equilibrium systems, rather than as “traditional” machines.

That is true. But, of course, the cell implements the same functions as complex machines do, and much more. My simple point is that, to do that, you need much greater functional complexity than you need to realize a conventional machine.

IOWs, dynamic, far from equilibrium systems that can be as successful as a conventional machine, or more, must certainly be incredibly complex and amazing systems, systems that defy everything else that we already know and that we can conceive. They must not only implement their functional purposes, but they must do that by “harnessing” the constantly changing waves of change, of random noise, of improbability. I have commented on those ideas in the mentioned thread, at posts #5 and #8, and I have quoted at posts #11 and #12 a couple of interesting and pertinent papers, introducing the important concept of robustness: the ability to achieve reliable functional results in spite of random noise and disturbing variation.

In this OP, I would like to present in some detail a very interesting system that shows very well what we can understand, at present, of that kind of amazing systems.

The system I will discuss here is an old friend: it is the NF-kB system of transcription factors (nuclear factor kappa-light-chain-enhancer of activated B cells). We are speaking, therefore, of transcription regulation, a very complex topic that I have already discussed in some depth here:

I will remind here briefly that transcription regulation is the very complex process that allows cells to be completely different using the same genomic information: IOWs, each type of cell “reads” differently the genes in the common genome, and that allows the different types of cell differentiation and the different cell responses in the same cell type.

Transcription regulation relies on many different levels of control, that are summarized in the above quoted OP, but a key role is certainly played by Transcription Factors (TFs), proteins that bind DNA and act as activators or inhibitors of transcription at specific sites.

TFs are a fascinating class of proteins. There are a lot of them (1600 – 2000 in humans, almost 10% of all proteins), and they are usually medium sized proteins, about 500 AA long, containing at least one highly conserved domain, the DNA binding domain (DBD), and other, often less understood, functional components.

I quote again here a recent review about human TFs:

The Human Transcription Factors

The NK-kB system is a system of TFs. I have discussed it in some detail in the discussion following the Ubiquitin thread, but I will describe it in a more systematic way here.

In general, I will refer a lot to this very recent paper about it:

Considering Abundance, Affinity, and Binding Site Availability in the NF-kB Target Selection Puzzle

The NF-kB system relies essentially on 5 different TFs (see Fig. 1 A in the paper):

  1. RelA  (551 AAs)
  2. RelB  (579 AAs)
  3. c-Rel  (619 AAs)
  4. p105/p50
    (968 AAs)
  5. p100/p52  (900 AAs)

Those 5 TFs work forming dimers, homodimers or heterodimers, for a total of 15 possible compbinations, all of which have been found to work in the cell, even if some of them are much more common.

Then there are at least 4 inhibitor proteins, collectively called IkBs.

The mechanism is apparently simple enough. The dimers are inhibited by IkBs and therefore they remain in the cytoplasm in inactive form.

When an appropriate signal arrives to the cell and is received by a membrane receptor, the inhibitor (the IkB molecule) is phosphorylated and then ubiquinated and detached from the complex. This is done by a protein complex called IKK. The free dimer can then migrate to the nucleus and localize there, where it can act as a TF, binding DNA.

This is the canonical activation pathway, summarized in Fig. 1. There is also a non canonical activation pathway, that we will not discuss for the moment.


Mechanism of NF-κB action. In this figure, the NF-κB heterodimer consisting of Rel and p50 proteins is used as an example. While in an inactivated state, NF-κB is located in the cytosol complexed with the inhibitory protein IκBα. Through the intermediacy of integral membrane receptors, a variety of extracellular signals can activate the enzyme IκB kinase (IKK). IKK, in turn, phosphorylates the IκBα protein, which results in ubiquitination, dissociation of IκBα from NF-κB, and eventual degradation of IκBα by the proteasome. The activated NF-κB is then translocated into the nucleus where it binds to specific sequences of DNA called response elements (RE). The DNA/NF-κB complex then recruits other proteins such as coactivators and RNA polymerase, which transcribe downstream DNA into mRNA. In turn, mRNA is translated into protein, resulting in a change of cell function.

Attribution: Boghog2 at English Wikipedia [Public domain]

Now, the purpose of this OP is to show, in greater detail, how this mechanism, apparently moderately simple, is indeed extremely complex and dynamic. Let’s see.

The stimuli.

First of all, we must understand what are the stimuli that, arriving to the cell membrane, are capable to activate the NF-kB system. IOWs, what are the signals that work as inputs.

The main concept is: the NF-kB system is a central pathway activated by many stimuli:

  1. Inflammation
  2. Stress
  3. Free
    radicals
  4. Infections
  5. Radiation
  6. Immune
    stimulation

IOWs, a wide variety of aggressive stimuli can activate the system

The extracellular signal arrives to the cell usually through specific cytokines, for example TNF, IL1, or through pathogen associated molecules, like bacterial lipopolysaccharides (LPS). Of course there are different and specific membrane receptors, in particular IL-1R (for IL1) , TNF-R (for TNF), and many TLRs (Toll like receptors, for pathogen associated structures). A special kind of activation is implemented, in B and T lymphocytes, by the immune activation of the specific receptors for antigen epitopes (B cell receptor, BCR, and T cell receptor, TCR).

The process through which the activated receptor can activate the NF-kB dimer is rather complex: it involves, in the canonical pathway, a macromolecular complex called IKK (IkB kinase) complex, comprising two catalytic kinase subunits (IKKa and IKKb) and a regulatory protein (IKKg/NEMO), and involving in multiple and complex ways the ubiquitin system. The non canonical pathway is a variation of that. Finally, a specific protein complex (CBM complex or CBM signalosome) mediates the transmission from the immune BCR or TCR to the canonical pathway. See Fig. 2:

From: NF-κB Activation in Lymphoid Malignancies: Genetics, Signaling, and Targeted Therapy – Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Increased-activity-of-the-CARMA1-BCL10-MALT1-signalosome-drives-constitutive-NF-kB_fig2_324089636 [accessed 10 Jul, 2019]
Figure 3 – NF-κB Activation in Lymphoid Malignancies: Genetics, Signaling, and Targeted Therapy
available via license: Creative Commons Attribution 4.0 International

I will not go into further details about this part, but those interested can have a look at this very good paper:

TLR-4, IL-1R and TNF-R signaling to NF-kB: variations on a common theme

In particular, Figg. 1, 2, 3.

In the end, as a result of the activation process, the IkB inhibitor is degraded by the ubiquitin system, and the NK-kB dimer is free to migrate to the nucleus.

An important concept is that this is a “rapid-acting” response system, because the dimers are already present, in inactive form, in the cytoplasm, and must not be synthesized de novo: so the system is ready to respond to the activating signal.

The response.

But what is the cellular response?

Again, there are multiple and complex possible responses.

Essentially, this system is a major regulator of innate and adaptive immune responses. As such, it has a central role in the regulation of inflammation, in immunity, in autoimmune processes, and in cancer.

Moreover, the NF-kB system is rather ubiquitous, and is present and active in many different cell types. And, as we have seen, it can be activated by different stimuli, in different ways.

So, the important point is that the response to activation must be (at least):

  1. Lineage-specific
  2. Stimulus-specific

IOWs, different cells must be able to respond differently, and each cell type must respond differently to different stimuli. That gives a wide range of possible gene expression patterns at the transcription level.

The following paper is a good review of the topic:

Selectivity of the NF-κB Response

For example, IL2 is induced by NF-kB activayion in T cells, but not in B cells (lineage specific response). Moreover, specific cell types can undergo specific, and often different, cell destinies after NF-kB activation: for example, NK-kB is strongly involved in the control and regulation of T and B cell development.

From:

30 years of NF-κB: a blossoming of relevance to human pathobiology

“B and T lymphocytes induce NF-κB in adaptive immune responses through the CARD11:Bcl10:MALT1 (CBM) complex (Hayden and Ghosh, 2008). Newly expressed genes promote lymphocyte proliferation and specific immune functions including antibody production by B cells and the generation of cytokines and other anti-pathogen responses by T cells.”

And, in the same cell type, certain promoters regulated by NF-kB require additional signaling (for example,  in human dendritic cells promoters for Il6Il12b, and MCP-1 require additional p38 histone phosphorylation to be activated), while others can be activated directly (stimulus-specific response).

So, to sum up:

  1. A variety of stimuli can activate the system in different ways
  2. The system itself has its complexity (different dimers)
  3. The response can be widely different, according to the cell type where it happens, and to the type of stimuli that have activated the system, and probably according to other complex variables.
  4. The possible responses include a wide range of regulations of inflammation, of the immune system, of cell specifications or modifications, and so on.

How does it work?

So, what do we know about the working of such a system?

I will ignore, for the moment, the many complexities of the activation pathways, both canonical and non canonical, the role of cyotkines and receptors and IKK complexes, the many facets of NEMO and of the involvement of the ubiquitin system.

For simplicity, we will start with the activated system: the IkB inhibitor has been released from the inactive complex in the cytoplasm, and some form of NF-kB dimer is ready to migrate to the nucleus.

Let’s remember that the purpose of this OP is to show that the system works as a dynamic, far from equilibrium system, rather than as a “traditional” machine. And that such a way to work is an even more amazing example of design and functional complexity.

To do that; I will rely mainly on the recent paper quoted at the beginning:

Considering Abundance, Affinity, and Binding Site Availability in the NF-kB Target Selection Puzzle

The paper is essentially about the NF-kB Target Selection Puzzle. IOWs, it tries to analyze what we know about the specificity of the response. How are specific patterns of transcription achieved after the activation of the system? What mechanisms allow the selection of the right genes to be transcribed (the targets) to implement the specific patterns according to cell type, context, and type of stimuli?

A “traditional” view of the system as a machine would try to establish rather fixed connections. For example, some type of dimer is connected to specific stimuli, and evokes specific gene patterns. Or some other components modulate the effect of NK-kB, generate diversification and specificity of the response.

Well, those ideas are not completely wrong. In a sense, the system does work also that way. Dimer specificity has a role. Other components have a role. In a sense, but only in a sense, the system works as though it were a traditional machine, and uses some of the mechanisms that we find in the concept of a traditional biological machine.

But that is only a tiny part of the real thing.

The real thing is that the system really works as a dynamic, far from equilibrium system, harnessing huge random/stochastic components to achieve robustness and complexity and flexibility of behavior in spite of all those non finalistic parts.

Let’s see how that happens, at least for the limited understanding we have of it. It is important to consider that this is a system that has been studied a lot, for decades, because of its central role in so many physiological and pathological contexts, and so we know many things. But still, our understanding is very limited, as you will see.

So, let’s go back to the paper. I will try to summarize as simply as possible the main concepts. Anyone who is really interested can refer to the paper itself.

Essentially, the paper analyzes three important and different aspects that contribute to the selection of targets at the genomic level by our TFs (IOWs, our NF-kB dimers, ready to migrate to the nucleus. As the title itself summarizes, they are:

  1. Abundance
  2. Affinity
  3. Binding site availability

1. Abundance

Abundance is referred here to two different variables: abundance of NF-kB Binding Sites in the genome and abundance of Nucleus-Localized NF-kB Dimers. Let’s consider them separately.

1a) Abundance of NF-kB Binding Sites in the genome:

It is well known that TFs bind specific sites in the genome. For NF-kB TFs, the following consensus kB site pattern has been found:

 5′-GGGRNWYYCC-3′

where R, W, Y, and N, respectively denote purine, adenine or thymine, pyrimidine, and any nucleotide.

That simply means that any sequence corresponding to that pattern in the genome can, in principle, bind NF-kB dimers.

So the problem is: how many such sequences do exist in the human genome?

Well, a study based on RelA has evaluated about 10^4 consensus sequences in the whole genome, but as NF-kB dimers seem to bind even incomplete consensus sites, the total number of potential binding sites could be nearer to 10^6

1b) Abundance of Nucleus-Localized NF-kB Dimers:

An estimate of the abundance of dimers in the nucleus after activation of the system is that about 1.5 × 10^5 molecules can be found, but again that is derived from studies about RelA only. Moreover, the number of molecules and type of dimer can probably vary much according to cell type.

So, the crucial variable, that is the ratio between binding sites and available dimers, and which could help undertsand the rate of sites saturation in the nucleus, remains rather undecided, and it seems very likely that it can vary a lot in different circumstances.

But there is another very interesting aspect about the concentration of dimers in the nucleus. According to some studies, NF-kB seems to generate oscillations of its nuclear content in some cell types, and those oscillation can be a way to generate specific transcription patterns:

NF-kB oscillations translate into functionally related patterns of gene expression

For example, this very recent paper :

NF-κB Signaling in Macrophages: Dynamics, Crosstalk, and Signal Integration

shows at Fig. 3 the occupancy curve of binding sites at nuclear level after NF-kB activation in two different cell types.

In fibroblasts, the curve is a periodic oscillation, with a frequency that varies according to various factors, and translates into different transcription scenarios accordingly:

Gene expression dynamics scale with the period (g1) and amplitude (g2) of these oscillations, which are influenced by variables such as signal strength, duration, and receptor identity.


In macrophages, instead, the curve is rather:

a single, strong nuclear translocation event which persists for as long as the stimulus remains and tends to remain above baseline for an extended period of time.

In this case, the type of transcription will be probably regulated by the are under the curve, ratehr than by the period and amplitude of the oscialltions, as happened in fibroblasts.

Interestingly, while in previous studies it seemed that the concentration of nuclear dimers could be sufficient to saturate most or all binding sites, that has been found not to be the case in more recent studies. Again from the paper about abundance:

in fact, this lack of saturation of the system is necessary to generate stimulus- and cell-type specific gene expression profiles

Moreover, the binding itself seems to be rather short-lived:

Interestingly, it is now thought that most functional NF-kB interactions with chromatin—interactions that lead to a change in transcription—are fleeting… a subsequent study using FRAP in live cells expressing RelA-GFP showed that most RelA-DNA interactions are actually quite dynamic, with half-lives of a few seconds… Indeed, a recent study used single-molecule tracking of individual Halo-tagged RelA molecules in live cells to show that the majority (∼96%) of RelA undergoes short-lived interactions lasting on average ∼0.5 s, while just ∼4% of RelA molecules form more stable complexes with a lifetime of ∼4 s.

2. Affinity

Affinity of dimers for DNA sequences is not a clear cut matter. From the paper:

Biochemical DNA binding studies of a wide variety of 9–12 base-pair sequences have revealed that different NF-kB dimers bind far more sequences than previously thought, with different dimer species exhibiting specific but overlapping affinities for consensus and non-consensus kB site sequences.

IOWs, we have different dimers (15 different types) binding with varying affinity different DNA sequences (starting from the classical consensus sequence, but including also incomplete sequences). Remember that those sequences are rather short (the consensus sequence is 10 nucleotides long), and that there are thousands of such sequences in the genome.

Moreover, different bindings can affect transcription differently. Again, from the paper:

How might different consensus kB sites modulate the activity of the NF-kB dimers? Structure-function studies have shown that binding to different consensus kB sites can alter the conformation of the bound NF-kB dimers, thus dictating dimer function When an NF-kB dimer interacts with a DNA sequence, side chains of the amino  acids located in the DNA-binding domains of dimers contact the bases exposed in the groove of the DNA. For different consensus kB site sequences different bases are exposed in this groove, and NF-kB seems to alter its conformation to maximize interactions with the DNA and maintain high binding affinity. Changes in conformation may in turn impact NF-kB binding to co-regulators of transcription, whether these are activating or inhibitory, to specify the strength and dynamics of the transcriptional response. These findings again highlight how the huge array of kB binding site sequences must play a key role in modulating the transcription of target genes.

Quite a complex scenario, I would say!

But there is more:

Finally, as an additional layer of dimer and sequence-specific regulation, each of the subunits can be phosphorylated at multiple sites with, depending on the site, effects on nearly every step of NF-kB activation.

IOWs, the 15 dimers we have mentioned can be phosphorylated in many different ways, and that changes their binding affinities and their effects on transcription.

This section of the paper ends with a very interesting statement:

Overall, when considering the various ways in which NF-kB dimer abundances and their affinity for DNA can be modulated, it becomes clear that with these multiple cascading effects, small differences in consensus kB site sequences and small a priori differences in interaction affinities can ultimately have a large impact on the transcriptional response to NF-kB pathway activation.

Emphasis mine.

This is interesting, because in some way it seems to suggest that the whole system acts like a chaotic system, at least at some basic level. IOWs, small initial differences, maybe even random noise, can potentially affect deeply the general working of the whole systems.

Unless, of course, there is some higher, powerful level of control.

3. Availability of high affinity kB binding sequences

We have seen that there is a great abundance and variety of binding sequences for NF-kB dimers in the human genome. But, of course, those sequences are not necessarily available. Different cell types will have a different scenario of binding sites availability.

Why?

Because, as we know, the genome and chromatin are a very dynamic system, that can exist in many different states, continuosly changing in different cell types and, in the same cell type, in different conditions..

We know rather well the many levels of control that affect DNA and chromatin state. In brief, they are essentially:

  1. DNA methylation
  2. Histone modifications (methylation, acetylation, etc)
  3. Chromatin modifications
  4. Higher levels of organization, including nuclear localization and TADs (Topologically Associating Domains)

For example, from the paper:

The promoter regions of early response genes have abundant histone acetylation or trimethylation prior to stimulation [e.g., H3K27ac, (67) and H4K20me3, (66)], a chromatin state “poised” for immediate activation…  In contrast, promoters of late genes often have hypo-acetylated histones, requiring conformational changes to the chromatin to become accessible. They are therefore unable to recruit NF-kB for up to several hours after stimulation (68), due to the slow process of chromatin remodeling.

We must remember that each wave of NK-kB activation translates into the modified transcription of a lot of different genes at the genome level. It is therefore extremely important to consider what genes are available (IOWs, their promoters can be reached by the NF-kB signal) in each cell type and cell state.

The paper concludes:

Taken together, chromatin state and chromatin organization strongly influence the selection of DNA binding sites by NF-kB dimers and, most likely, the selection of the target genes that are regulated by these protein-DNA interaction events. Analyses that consider binding events in the context of three-dimensional nuclear organization and chromatin composition will be required to generate a more accurate view of the ways in which NF-kBDNA binding affects gene transcription.

This is the main scenario. But there are other components, that I have not considered in detail for the sake of brevity, for example competition between NF-kB dimers and the complex role and intervention of other co-regulators of transcription.

Does the system work?

But does the system work?

Of course it does. It is a central regulator, as we have said, of many extremely important biological processes, above all immunity. This is the system that decides how immune cells, T and B lymphocytes, have to behave, in terms of cell destiny and cell state. It is of huge relevance in all inflammatory responses, and in our defense against infections. It works, it works very well.

And what happens if it does not work properly?

Of course, like all very complex systems, errors can happen. Those interested can have a look at this recent paper:

30 years of NF-κB: a blossoming of relevance to human pathobiology

First of all, many serious genetic diseases have been linked to mutations in genes involved in the system. You can find a list in Table 1 of the above paper. Among them, for example, some forms of SCID, Severe combined immunodeficiency, one of the most severe genetic diseases of the immune system.

But, of course, a dysfunction of the NF-kB system has a very important role also in autoimmune diseases and in cancer.

Conclusions.

So, let’s try to sum up what we have seen here in the light of the original statement about biological systems that “are not machines”.

The NF-kB system is a perfect example. Even if we still understand very little of how it works, it is rather obvious that it is not a traditional machine.

A traditional machine would work differently. The signal would be transmitted from the membrane to the nucleus in the simplest possible way, without ambiguities and diversions. The Transcription Factor, once activated, would bind, at the level of the genome, very specific sites, each of them corresponding to a definite cascade of specific genes. The result would be clear cut, almost mechanical. Like a watch.

But that’s not the way things happen. There are myriads of variations, of ambiguities, of stochastic components.

The signal arrives to the membrane in multiple ways, very different one from the other: IL1, IL17, TNF, bacterial LPS, and immune activation of the B cell receptor (BCR) or the T cell receptor (TCR) are all possible signals.

The signal is translated to the NF-kB proteins in very different ways: canonical or non canonical activation, involving complex protein structures such as:

The CBM signalosome, intermediate between immune activation of BCR or TCR and canonical activation of the NF-kB. This complex is made of at least three proteins, CARD11, Bcl10 and MALT1.

The IKK complex in canonical activation: this is made of three proteins, IKK alpha, IKK beta, and NEMO. Its purpose is to phosphorylate the IkB, the inhibitor of the dimers, so that it can be ubiquinated and released from the dimer. Then the dimer can relocate to the nucleus.

Non canonical pathway: it involves the following phosphorylation cascade: NIK -> IKK alpha dimer -> Relb – p100 dimer -> Relb – p50 dimer (the final TF). It operates during the development of lymphoid organs and is responsible for the generation of B and T lymphocytes.

Different kinds of activated dimers relocate to the nucleus.

Different dimers, in varying abundance, interact with many different binding sites: complete or incomplete consensus sites, and probably others. The interaction is usually brief, and it can generate an oscillating pattern, or a more stable pattern

Completely different sets of genes are transcribed in different cell types and in different contexts, because of the interaction of NF-kB TFs with their promoters.

Many other factors and systems contribute to the final result.

The chromatin state of the cell at the moment of the NF-kB activation is essential to determine the accessibility of different binding sites, and therefore the final transcription pattern.

All these events and interactions are quick, unstable, far from equilibrium. A lot of possible random noise is involved.

In spite of that amazing complexity and potential stochastic nature of the system, reliable transcripion regulation and results are obtained in most cases. Those results are essential to immune cell differentiation, immune response, both innate and adaptive, inflammation, apoptosis, and many other crucial cellular processes.

So, let’s go back to our initial question.

Is this the working of a machine?

Of course it is! Because the results are purposeful, reasonably robust and reliable, and govern a lot of complex processes with remarkable elegance and efficiency.

But certainly, it is not a traditional machine. It is a lot more complex. It is a lot more beautiful and flexible.

It works with biological realities and not with transistors and switches. And biological realities are, by definition, far from equilibrium states, improbable forms of order that must continuously recreate themselves, fighting against the thermodynamic disorder and the intrinsic random noise that should apparently dominate any such scenario.

It is more similar to a set of extremely clever surfers who succeed in performing elegant and functional figures and motions in spite of the huge contrasting waves.

It is, from all points of view, amazing.

Now, Paley was absolutely right. No traditional machine, like a watch, could ever originate without design.

And if that is true of a watch, with its rather simple and fixed mechanisms, how much truer it must be for a system like NF-kB? Or, for that, like any cellular complex system?

Do you still have any doubts?

Added graphic: The evolutionary history, in terms of human conserved information, of the three proteins in the CBM signalosome.
On the y axis, homologies with the human protein as bits per aminoacid (bpa). On the x axis, approximate time of appearance in million of years.
The graphic shows the big information jump in vertebrates for all three protens , especially CARD11.


Added graphic: two very different proteins and their functional history


Added graphic (for Bill Cole). Functional history of Prp8, collagen, p53.
Comments
Steve Shaffner wrote:
Every functioning human immune system finds a target with FI>500 bits. The target is a set of antibodies that can effectively respond to hundreds of different pathogens. If an analysis concludes that hitting such a target is impossible, the analysis is wrong.
The immune system was intelligently designed with the ability to do that, Steve. Producing the immune system via blind and mindless processes is what is impossible. https://discourse.peacefulscience.org/t/comments-on-gpuccio-functional-information-methodology/7560/78ET
August 27, 2019
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To all: I have posted at PS a graph documenting the two examples given in the summary about my methodology posted there. The two proteins are: 1) ATP synthase beta chain: an old protein that presents amazing similarity to the human form alredy in bacteria. In metazoa, the curve is almost horizontal. The FI here is older than that. 2) CARD11, a protein that, as we well know, presents an amazing FI jump at the transition to vertebrates. I add the graph here, at the end of the OP.gpuccio
August 27, 2019
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Swamidass asks:
So what evidence can you produce for a pre-existing specification?
Even Richard Dawkins agrees that biological reproduction is the thing specified in advance: In The Blind Watchmaker Dawkins writes, “Complicated things have some quality, specifiable in advance, that is highly unlikely to have been acquired by random chance alone. In the case of living things, the quality is specified in advance is…the ability to propagate genes in reproduction.ET
August 27, 2019
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Then there is the scientifically illiterate and totally clueless Faizal Ali- thankfully gpuccio doesn't have to deal with that one:
Yes, but it is clear @gpuccio has lots and lots of things he wants to tell us about before he gets to the big reveal, if he ever does. You know and I know what a big disappointment that is going to be. But this should prove to be a very enlightening demonstration of how ID’ers think science works.
LoL! What empirical evidence do THEY have that demonstrates FI increases can happen via blind and mindless processes? What empirical evidence do THEY have that demonstrates FI can arise via blind and mindless processes? They have no way to even test such things...ET
August 27, 2019
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sfmatheson and Swamidass at PS: I have always been interested in the issue of functional divergence. One way to test for functional divergence could be to use my methodology in two separate branches of evolutionary history. The sequence configuration that is not shared in the two branches but is highly conserved in each branch would be a good candidate for functional divergence. I have tried something on that line in this OP of mine: Information Jumps Again: Some More Facts, And Thoughts, About Prickle 1 And Taxonomically Restricted Genes. https://uncommondescent.com/intelligent-design/information-jumps-again-some-more-facts-and-thoughts-about-prickle-1-and-taxonomically-restricted-genes/ Another way, of course, is to prove the function of the non conserved sequence directly. Transcription Factors are a very good example of that. They have one or more DNA binding domains that are usually highly conserved. The rest of the molecule (often half of the sequence or more) is not very conserved. However, there are many indications that important functions, different from DNA binding, are implemented by that part of the protein. I have discussed a recent, very interesting paper about RelA, an important TF, which demonstrates how much of the function is linked to the non DBD part of the molecule. If you are interested, you will find my comment about that at #29 in the thread linked at the start of this thread: Controlling The Waves Of Dynamic, Far From Equilibrium States: The NF-KB System Of Transcription Regulation. https://uncommondescent.com/intelligent-design/controlling-the-waves-of-dynamic-far-from-equilibrium-states-the-nf-kb-system-of-transcription-regulation/gpuccio
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sfmatheson at PS:
Your answer, I think, missed the point. The issue is not, as you seem to think (and I could be wrong) that neutral drift precludes design. (Edit for clarity: what I mean is that the challenge posed by @swamidass is not an assertion that drift cannot lead to design.)
Maybe I was not clear enough. My point about neutral drift is not that it precludes design, or not. Or that it leads to design, or not. My point is that neutral drift is irrelevant for FI and the design inference. I have also specified the reson for that. Neutral drift does not change any of the factors that influence FI and the design inference: a) It does not change the target space b) It does not change the search space c) It does not change the probabilistic resources of the system. IOWs, neutral drift neither precludes design nor leads to it, and it makes the generation of FI neither easier nor more difficult. I hope that is clear. But, of course, neutral variation (which is the result of neutral drift) is instead an important part of my methodology to measure FI in proteins. Indeed, as explained many times, my whole procedure is based on the two pillars of neutral variation and negative (purifying) selection. You say:
The issue is that drift (the key concept being neutral drift) will create sequence divergence that is uncoupled from function. In situations in which you have neutral sequence divergence, you have a nice negative control, which is one theme that @swamidass has emphasized without success. Any metric that supposes itself to measure “functional information” must be able to distinguish random drift from functional difference.
And you are right, of course. I have clarified those things myself, in comment #36. I quote myself: gpuccio at PS:
It should be clear that my methodology is not measuring the absolute FI present in a protein. It is only measuring the FI conserved up to humans, and specific to the vertebrate branch. So, let’s say that protein A has 800 bits of human conserved sequence similarity (conserved for 400+ million years). My methodology affirms that those 800 bits are a good estimator of specific FI. But let’s say that the same protein A, in bees, has only 400 bits of sequence similarity with the human form. Does it mean that the bee protein has less FI? Absolutely not. It probably just means that the bee protein has less vertebrate specific FI. But it can well have a lot of Hymenoptera specific FI. That can be verified by measuring the sequence similarity conserved in that branch for a few hundred million years, in that protein.
So, I will try to be even more clear. When we have a bitscore from Blast, we are measuring of course both conservation and divergence. We can divide that into 4 different components: a) Sequence conservation due to passive common descent. That is the component that we cancel, or minimize, by guaranteeing that we are blastin sequences separated by a very long evolutionary time, because practically all passive similarity will have been erased by neutral variation, if that part of the sequence is not functional. b) Sequence similarity preserved by negative (purifying) selection. This is what we measure by the bitscor, of the condition above mebtioned is satisfied. This is what I call a good estimator of FI. c) Divergence due to neutral variation and drift. d) Divergence due to different functional specificities in the two organisms. Let’s call this “functional divergence”. Now your point seems to be that my method cannot distinguish between c) and d). And I perfectly agree. But I have never claimed that it could. My method is aimed to measure that part of FI that is linked to long sequence conservation. Nothing more. That is of course only a part of the total FI. Let’s say that it is the part that can be detected by long sequence conservation. I have always made that very clear. My method underestimates FI, and this is one of the resons for that. So, we can consider my methodology as a good estimator of a lower threshold of FI in a protein. That’s perfectly fine for the purpose of making a design inference. When I find 1250 bits of detectable FI in CARD11, I can well infer design according to ID theory. If the FI is more than that, OK. But that value is much more than enough. As stated many times, ID is a procedure to infer design with no false positives, and many false negatives. False negatives are part of the game, and the threshold is conceived to guarantee the practical absence of false positives. Of course, functional divergence is a very interesting issue too. But it requires a different apporach to be detected. I will discuss that briefly in next post.gpuccio
August 27, 2019
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For those readers interested in following GP’s discussion with PS, here are the associated post numbers: 343 Bill Cole 351 GP to Bill Cole 354 Bill Cole 356 GP to Bill Cole and PS 357 Bill Cole 360 Bill Cole 368 GP to PS 369 GP to Davecarlson 370 GP to JS 374 GP to UD 375 GP to JS 381 GP to JS 387 GP to JS 388 GP to JS 395 GP to JS 398 GP to JS 401 GP to Art Hunt 402 GP to Rumracket 406 GP to JS 408 GP to JS 411 GP to PS 416 GP to sfmatheson and JS 431 GP to JS 432 GP to JS 433 GP to JS 434 GP to glipsnort 438 GP to Art HuntPeterA
August 26, 2019
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Intelligent Design doesn't stand a chance against the magical shape-shifting feedback of natural selection. So sayeth the Peaceful Science faithful. :)ET
August 26, 2019
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It is just getting worse. Now there is some sort of magic in natural selection and its feedback:
The same thing that’s wrong with every ID-Creationist probability calculation. You make the demonstrably false assumption extant proteins formed through purely random processes instead of the empirically observed process of gradual development shaped by selection feedback.
Cuz humans reproducing more humans will eventually lead to more humans... https://discourse.peacefulscience.org/t/comments-on-gpuccio-functional-information-methodology/7560/68ET
August 26, 2019
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@Mike1962. Link you requested: https://discourse.peacefulscience.org/t/gpuccio-functional-information-methodology/7549equate65
August 26, 2019
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@ Mike 439: https://discourse.peacefulscience.org/t/gpuccio-functional-information-methodology/7549equate65
August 26, 2019
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Someone please provide a link to the gpuccio/swamidass thread on the other website.mike1962
August 26, 2019
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Art at PS:
I would note that the bits calculated in this cited essay cannot be equated with bits derived from either BLAST analyses or the equation for FI that has been given above. They are quite completely different, and the calculation in the essay simply does not provide a bit-based estimate of probabilistic resources.
Why not? I have considered the probabilistic resources of our planet as a higher threshold of the number of possible states visited by a super population of bacteria inhabiting our planet for 5 billion years and reproducing at a very high rate. This is of course an exaggeration, and a big one, but the idea is correct, I believe. The probabilistic resources of a systerm are the number of states that can be randomly reached. It is similar to the number of times that i can toss a coin. They can be expressed as bits, just taking the positive log2 of the total number of states. So, if I have a sequence that has a FI of 500 bits, it means that there is a probability of 1:2^500 to get it in one random attempt. If my system has probabilistic resources of 120 bits (IOWs, 2^120 states can be reached), the probability of reaching the target using the whole probabilistic resources is still 1:2^380. What’s wrong with that? Of course, as I have said, the Blast bitscore is not the FI. But, provided that the conditions I have listed are satisfied, it is a good estimator of it. Look also at my answer to glipsnort, that I have just published. Please, let me know what you think. Thank you. :)gpuccio
August 26, 2019
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Yes, I get it and it will be frustrating for you. But you seem to have the patience required and it is always a good thing to have others look over your methodology. I am glad that they restricted the number of people that you have to respond to.ET
August 26, 2019
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ET: I am trying to clarisy a few points. Let's see how it goes. It's not easy. If I can agree with Swamidass at least on some basic ideas, I will try to show him why his reasoning about cancer is wrong. It's strange how a simple concept like FI is so often misunderstood. See #434, for example.gpuccio
August 26, 2019
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If you read Joshua's Computing the Functional Information in Cancer you can see that his concept of FI is NOT the same as what gpuccio is trying to discuss. It will be very difficult to continue the dialog until there is an agreement about what is being discussed. People are trying to grasp at straws to try to refute gpuccio's methodology and assumptions.ET
August 26, 2019
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glipsnort at PS:
Consider a cartoon piece of sequence that is 100 bp in length. The sequence has a function, every basepair contributes to the function, and loss of the function is lethal to the organism. 0.1% of all randomly chosen sequences could serve the same function equally well. This means the sequence has 7 bits of FI, correct? 300 different sequences can be reached from the functional sequence by one single-base substitution – which is the only kind of mutation in this cartoon world. The most likely case, then, is that all 100 bases will be conserved through evolution. Will blast return a bitscore of 7 bits for a 100 bp exact match? ETA: Sorry, that’s 10 bits – I was taking the natural log.
I don't follow your reasoning. A sequence of 100 bp where each bp must be specific for the function to be present has, of course, a FI of 200 bits: Target space = 1 Search space = 4^100 Target space/Search space = 6,22302E-61 FI = 200 bits The FI expresses the probability of finding the target space from an unrelated state in one attempt. In this case, it is 1:4^100 I don't follow your reasoning. Of course, the perfect conservation of that sequence would inform us that the sequence has 200 bits of FI. Indeed, the bitscore of a 100 bp sequence against itself is 185 bits. Which seems good enough.gpuccio
August 26, 2019
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Swamidass at PS:
This is helpful and distinguishes you from @Kirk. We agree that common descent can produce similarity, and that this would look like FI in your computation. We have had a hard time establishing this point with other ID luminaries. The way you account for this is by only looking at ancient proteins, where you hope that millions of years would be sufficient to erase this effect. How do you know 400,000 million years is long enough to erase this effect?
Usually, when you look at synonimous sites, it is very difficult to detect any passive sequence similarity after such a time. IOWs, we reach “saturation” of the Ks. The R function that I use to compute Ks usually gives me a saturation message for synonimous sites in proteins that have such an evolutionary distance. Moreover, the fact is rather obvious when we look at the very different behaviour of proteins in the transition to vertebrates. The example of CARD11 is really an extreme case. Many proteins have very low sequence similarity between fishes and humans. The human configuration, in those proteins, begins to grow at later steps. There are proteins that have big neutral components, and the neutral part in not conserved at all thorughout that evolutionary window. So, I have all reasons to believe that 400 million years are enough to make conserved information a good estimator of FI. Remember, we are not looking for perfection here. Just a good estimate.gpuccio
August 26, 2019
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Swamidass: I will start with an easy point: your question about neutral drift. I think that will be valid for coevolution also. First of all, I am well aware of nutralism and of drift as important actors in the game. Indeed, you can see that my whole reasoning to measure FI is based on the effects of neutral variation and neutral drift. What else erases non functional similarities given enough evolutionary time? So, I have no intention at all to deny the role of neutral variation, of neutral drift, and of anything else that is neutral. Or quasi neutral. My simple point is: all this neutral events, including drift, are irrelevant to ID and to FI. The reason is really very simple. FI is a measure of the probability to get one state from the target space by a random walk. High FI means an extremely low probability if reaching the target space. Well, neutral drift does not change anything. The number of states that is tested (the probabilistic resources of the system) remains the same. The ratio of the targte space to the search space remains the same. IOWs, neutral drift has no influence at all on the probabilistic barriers. Why? Because it is a random event, of course. Each neutral event that is fixed is a random variation. There is no reason to believe that the mutations that are fixed are better than those that are not fixed, in the persepctive of getting to the target. Nothing changes. Look, again I am trying to answer briefly. But I can deepen the discussion, if you let me know what you think. Just a hint. To compute FI in a well defined system, we havt to compute, durectly or indirectly, three different things: The search space. That is usually easy enough, with some practical approximations. The target space. This is usually the difficult part, and it usually requires indirect approximations. The probabilistic resources of the system. FI (-log2 the ratio of the target space to the search space) is a measure of the improbability of finding the target space in one random event. But of course the system can try many random events. So, we have to analyze the probabilistic resources of the system, in the defined time window. This can usually be donne by considering the number of reproductions in the population, and the number of mutations. IOWs, the total number of genetic states that will be available in the system in the time window. I have discussed many aspects of these things in this OP: What Are The Limits Of Random Variation? A Simple Evaluation Of The Probabilistic Resources Of Our Biological World https://uncommondescent.com/intelligent-design/what-are-the-limits-of-random-variation-a-simple-evaluation-of-the-probabilistic-resources-of-our-biological-world/ I give here also the link to my OP about the limits of NS: What Are The Limits Of Natural Selection? An Interesting Open Discussion With Gordon Davisson https://uncommondescent.com/intelligent-design/what-are-the-limits-of-natural-selection-an-interesting-open-discussion-with-gordon-davisson/ In those two OPs, and in the following discussions, I have discussed many aspects of the questions that are being raised here. Of course, I will try to make again the important points. But please help me. When what I say seems too brief or not well argumented, consider that I am trying to give the general scenario first. Ask, and I will try to answer.gpuccio
August 26, 2019
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(Swamidass has been trying to help me by implementing a new policy of moderation and limiting the number of contributors to the direct thread to a few. Here is my comment about that.) I thank Swamidass and the others for the attention to my “risk of being overwhelmed”. I would like to answer everyone, or at least all those who offer interesting contributions, but the risk os real. I am one, and my resources are limited. So, I will profit of this new “anti overwhelming” policy, but I will also try to have a look at waht others say, and if possible answer them. I will answer different points that have been raised as it comes. There is so much to say. I don’t want to convince anyone, but I would like very much to clarify what I believe as well as possible. So, let’s start.gpuccio
August 26, 2019
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Chris Falter chimes in unaware that evolutionary algorithms use telic processes- they do what they were intelligently designed to do. How do you deal with people like that?ET
August 26, 2019
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Alan Fox:
Maybe someone could offer a definition of “functional information”.
That has been explained to Alan on numerous occasions. Stat with Crick's definition of information with respect to biology:
Information means here the precise determination of sequence, either of bases in the nucleic acid or on amino acid residues in the protein.
Functional information would be in the sequence specificity required to produce functioning protein X (whatever protein is being investigated). Methinks Alan just wants to muddy the waters. Alan then points to this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4476321/ and apparently never read it. They start with an existing abundance of polypeptides- not one of which arose via blind and mindless processes- and found 4 out of 6x10^12 that bound to ATP- that is the function. Alan thinks that does something for blind watchmaker evolution and is somehow an argument against ID. Clueless.ET
August 26, 2019
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ET, Ok. Thanks. BTW, here’s another interesting piece of information provided by Alexa: UD 631,311 578 Total Sites Linking In PT 1,732,931 950 Total Sites Linking In TSZ 3,215,461 37 Total Sites Linking In PS 7,036,059 12 Total Sites Linking In Note that PT has a lot more sites linking in but a lot less traffic than UD?jawa
August 26, 2019
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OK Jawa. Yes I get it. I misread it as being the number of visits for whatever reason.ET
August 26, 2019
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ET @414: Did you see my comment @420? Did you understand it? Thanks.jawa
August 26, 2019
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It just gets worse. Now a Timothy Horton is bluffing and equivocating. It's sad, really: https://discourse.peacefulscience.org/t/comments-on-gpuccio-functional-information-methodology/7560/5 Timothy sez:
If computer code which produces the performance observed in the visual graphics isn’t a sequence then what is it?
Did nature write that computer code? No. Humans did. Meaning intelligent agencies did. Peaceful Science wallows in its own willful ignorance and cowardly equivocations.ET
August 26, 2019
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Oh my. The key itself was created by design, Neil. The random number generator worked as it was intelligently designed to work. Nature, operating freely, did not produce the key. See, it’s comments like that which demonstrate gpuccio faces a huge, steep hill.
The bigger issue is the number generator producing two like sequences is extremely remote where biology can do this. This goes to the heart of what functional information really is especially what gpuccio is observing when he sees information jumps and then long term preservation. What caused the jump? What caused the preservation?bill cole
August 26, 2019
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Oh my. The key itself was created by design, Neil. The random number generator worked as it was intelligently designed to work. Nature, operating freely, did not produce the key. See, it’s comments like that which demonstrate gpuccio faces a huge, steep hill.
The bigger issue is the number generator producing two like sequences is extremely remote where biology can do this. This goes to the heart of what functional information really is especially what gpuccio is observing when he sees information jumps and then long term preservation. What caused the jump? What caused the preservation?bill cole
August 26, 2019
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As for cancer, any cancer cells that utilize fermentation are more primitive than the normal cells. That alone means there isn't any gain in functional information. A rock is a primitive hammer. It contains less FI than a real hammer. Also, it is obvious that cancer cells lost some specificity along the way. Their coding no longer specifies the type of cell it should be. Joshua will not listen to any of that, though...ET
August 26, 2019
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The RSA key itself was mostly produced by a random number generator, with some filtering. Yes, you could say that the random number generator was designed. And you could say that the RSA cryptosystem was designed. Still, the key itself is mostly generated randomly, so does not seem designed.
Oh my. The key itself was created by design, Neil. The random number generator worked as it was intelligently designed to work. Nature, operating freely, did not produce the key. See, it's comments like that which demonstrate gpuccio faces a huge, steep hill.ET
August 26, 2019
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