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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
ET @414:
what are you talking about? Last I checked 7 million (PS) > 643,000 (UD)- Or am I misreading 405?
Sorry, my fault. I should have explained the meaning of the values shown in that table. I should not have assumed that others will know what they mean without my clarification. As far as I understand it, those numbers mean website ranking in the entire internet. I admit that those numbers are hard to believe. I could not believe that there can be more than 7 million websites out there! That's a big wow!!! But I convinced myself using this easy approach: open the links provided @346 and read the information written in there. Then compare those 4 links with this: https://www.alexa.com/siteinfo/google.com Note that the Alexa Ranking value for Google is 1. Yes, ONE. That's it. That means that there are over 600K websites out there that are ranked higher than UD. That's why the ranking comparison @346 is done among comparable peers. Otherwise, it wouldn't make much sense, at least to me. Again, read the information provided by Alexa in those links for UD, PT, TSZ, PS and see more interesting stuff that is in there. Do you understand this now? If not, I'll try to explain it another way. Now you should understand what I wrote about UD having incomparably much more internet traffic than PS does. Hence this discussion with GP allows PS to be exposed to at least the viewers at UD. Now UD is exposed to PS viewers, but that shouldn't make a big difference in number of viewers, because they don't have that many anyway.jawa
August 26, 2019
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ET
gpuccio and Bill Cole need to ask them for examples from their position that falls in with what they are saying about science and testability.
As far as I am concerned this discussion has been successful. Gpuccio's method was generally accepted as a way to estimate FI. The 500bit issue is secondary. The competing methods (to design or mind) that claim they can generate FI like neutral mutations will fall away in my opinion given a little time. I have asked them for a model several times and they have not produced one any better then Dawkins Weasel.bill cole
August 26, 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 to be continued...PeterA
August 26, 2019
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Jawa- It is easy to see what is going on over there. And it is obvious that Joshua is bluffing and equivocating. And now Rumraket wants gpuccio to demonstrate a negative all the while they never ante up and never demonstrate anything. Where is the demonstration that blind and mindless processes can produce 500 bits of FI? It doesn't exist.ET
August 26, 2019
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sfmatheson at PS:
Speaking only for myself, I don’t think there is any reason to discuss design, or “ID theory,” in this context until the very basic questions asked by @swamidass are addressed. And I would reiterate that asking this question outside of even a basic phylogenetic analysis/approach is futile. It is not possible to talk meaningfully about “functional information” without these basic foundational tasks being done.
Swamidass at PS:
I agree. Rather than a primer on ID generalities, let’s focus on what specifically you @gpuccio are doing. For example, with your definition of design, there must be a pre-existing design. You are empirically based. So what evidence can you produce for a pre-existing specification? As @nwrickert, it seems obvious that this does not exist, at least not in an human accessible form.
I was answering to the remarks made here asking how I got to the design inference from the simple observation of complex FI. If you are not interested in the theory you seem to discuss so often here, please clarify that. But that seems not to be the case. I see that, as expected, my “primer on ID generalities” has already generated some fierce response. So, I think I will go on, and answer them.gpuccio
August 26, 2019
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ET @412:
Joshua is already bluffing and equivocating. So unless he is called on his obviously false statements, this is going to go exactly as I predicted- nothing will come of it and PS will claim victory over ID.
This discussion is just starting. I think it's too early to draw conclusions. Let's wait and see.jawa
August 26, 2019
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Jawa, what are you talking about? Last I checked 7 million (PS) > 643,000 (UD)- Or am I misreading 405?ET
August 26, 2019
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ET @407:
UD could easily increase the number of viewers just by going toe-to-toe with PS. Meaning UD should take on the anti-ID diatribe and put PS in its place.
Looking at the data @405 we may see that the number of viewers at PS is incomparably much lower than the number of viewers here at UD. Therefore technically PS may benefit more -in number of viewers- from this discussion, because their website is now exposed to the incomparably much larger number of viewers at UD. However, UD viewers should benefit from the learning experience that this discussion may result in. Since probably most of what GP may present to the PS folks is already known to us, the main learning in this case may come from seeing how different arguments are being presented and how different folks approach this kind of discussion, depending on their individual perspectives. Any person interested in the topic of human communication should enjoy this inter-blogging chat. :)jawa
August 26, 2019
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Joshua is already bluffing and equivocating. So unless he is called on his obviously false statements, this is going to go exactly as I predicted- nothing will come of it and PS will claim victory over ID. Joshua actually believes that cancer demonstrates a gain in FI. And yet all evidence says the opposite- that cancer cells are more primitive than the normal cells they evolved from.ET
August 26, 2019
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To all at PS: So, the central core of ID theory is the following: Leaving aside biological objects (for the moment), there is not one single example in the whole known universe where FI higher than 500 bits arises without any intervention of design. On the contrary, FI higher than 500 bits (often much higher than that) abunds in designed objects. I mean human artifacts here. Therefore, if we observe FI in any object (leaving aside for the moment biological objects) we can safely infer a design origin for that object. That procedure will generate no false positives. Of course, it will generate a lot of false negatives. The threshold of 500 bits has been chosen exactly to get that type of result. If those points are not clear, we are not really discussing iD theory, but something else. This strong connection between high FI levels and a design origin has, of course, a rationale. But its foundation is completey empirical. We can observe that connection practically everywhere. The rationale could be expressed as follows: there is no known necessity law that generates those levels of FI without any design intervention. Therefore, FI in non design systems can arise only by chance. But a threshold of 500 bits is so much higher than the probabilistic resources of the known universe, that we can be sure that such an event is empirically impossible. The probabilistic barriers of getting 500 bits of FI are simply too high to be overcome. Well, that’s ID theory in a nutshell. I will come to the application to biology later. But I am confident that this simple summary will be enough for the moment to generate some answers.gpuccio
August 26, 2019
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This discussion between GP and the folks at PS is really interesting. Let's hope that GP's interlocutors at PS remain polite and that they all -but specially Dr. JS- focus in on understanding well the important concepts GP is trying to explain.PeterA
August 26, 2019
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John Mercer:
In science, the term “theory” refers to a scientific hypothesis whose empirical predictions have a long track record of being correct. Nothing of the sort exists for ID. You might have a hypothesis, but only if it makes clear empirical predictions.
None of that exists for blind watchmaker evolution. It seems gpuccio's opponents do not even understand how lame their position is. There aren't any predictions borne from blind and mindless processes- well maybe genetic diseases and deformities. There isn't any way to test the claim that those types of processes produced vision systems. gpuccio and Bill Cole need to ask them for examples from their position that falls in with what they are saying about science and testability.ET
August 26, 2019
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Swamidass: Please, let me go on with some linear explanation of ID theory and my approach to it. Then I will answer your three questions. You may have noticed that I have proposed two different questions about what ID theory is about: What is the connection between complex FI and the design inference? How does that apply to biological objects? Now, if we want to understand each other, we have to focus first on the first question. To do that, we must for the moment forget biological objects. After all, they are the object we are discussing about: are they designed or do they arise by other mechanisms? So, we will for the moment consider the origin of biological objects undecided, and try to understand ID theory without any reference to biology. To do that, we need an explicit definition of design and of functional information. I have offered a lnk to my two OPs about those two definitions. So, I will just remind here that: Design is any process where some conscious intelligent and purposeful agent imprints some specific configuration to a material object deriving it from subjective representations in his consciousness. The key point here is that the subjective representation must precede its output to the materila oobject. FI is the number of bits required to implement some explicitly defined function. Any function can be used. FI is always defined in relation to the defined function, whatever it is. n object exhibits the level of FI linked to the function if it can be used to implement the explicitly defined function at the explicitly defined level. In general, an explicitly defined function generates a binary partition in a well defined system and set of possible objects: those that can implement it, and those that cannot. FI, in general, is computed as -log2 of the ratio of the target space (the number of objects that can implement the function) to the search space (the number of possible objects) in the defined system. More in next post.gpuccio
August 26, 2019
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Jawa, UD could easily increase the number of viewers just by going toe-to-toe with PS. Meaning UD should take on the anti-ID diatribe and put PS in its place.ET
August 26, 2019
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Swamidass at PS:
gpuccio: IOWs, this protein was highly and specifically engineered during the transition to vertebrates, and that precious FI has then been preserved up to now. Swamidass: Well that inference is not warranted. As we know, there are several mechanisms that produce FI in biological sequences. He would have to rule all of these out to make this inference.
I will start from this statement to deal with what I call the central/core of ID theory. You must understand that when I was requested to write a summary of my methodology to measure FI in proteins, I did exactly that. I did not include a complete description of ID theory. Of course, being a very convinced supporter of ID theory, it was very natural for me to conflate the measurement of very high values of FI with an inference to design, because that’s exactly what ID theory warrants. But now, having discussed in some detail the rationale of my measurement of FI in proteins, the focus can shift to ID theory itself. In brief, what is the connection between complex FI and design? And how does that connection apply to biological objects? An important premise is that my personal approach to ID theory is completely empirical. It requires no special philosophy or worldview, except some good epistemology and philosophy of science. It is, I believe, completely scientific. And it has no connections with any theology. It has always been my personal choice to avoid any reference to theological arguments in all my scientific discussions about ID theory. And I will stick to that choice here too. More in next post.gpuccio
August 26, 2019
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Alexa Global Internet Traffic Ranking for UD peers: Site.........Aug 23.........Aug 26...........Last 3 days...........Last 90 days UD.........643,052........631,311...........UP.......11K.........UP..........127K PT........1,693,214......1,732,931..........DOWN..39K.........DOWN....270K TSZ......3,205,404......3,215,461.........DOWN..10K..........DOWN...702K PS........7,057,312.......7,036,059.........UP........21K (*).....DOWN....4.22M (**) Note that PS relative position has deteriorated considerably the last 3 months (**), but then it has improved the last 3 days (*). Could (*) be related to their recent exposure to UD readers in GP's thread? Hard to tell, but it could be related. :) PS. See the corresponding Alexa links @346.jawa
August 26, 2019
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Rumracket at PS:
Whether some protein grew in size during the evolution of some clade or along some branch, or how conserved that protein is in that clade, seems to me to have little to nothing to do with whether it was designed. What am I missing?
He's missing: 1. "the central core of ID theory, which connects complex FI to the design inference." - GP 2. a comprehensive and coherent explanation for an unguided cause, including RV+NS+whatever else... 3. perhaps open-mindedness and willingness to understand? 4. maybe some basic logic reasoning too? :)jawa
August 26, 2019
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OLV at #399: Sometimes I post at strange times! :)gpuccio
August 26, 2019
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Rumracket at PS:
Whether some protein grew in size during the evolution of some clade or along some branch, or how conserved that protein is in that clade, seems to me to have little to nothing to do with whether it was designed. What am I missing?
You are missing the central core of ID theory, which connects complex FI to the design inference. I will get to that soon enough.gpuccio
August 26, 2019
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Art at PS:
I may have more later, but for now it should be noted that the bit score from a BLAST search and the informational number of bits used to identify design may be two rather different things. The usage adopted by gpuccio and most ID proponents is just a -log2 transformation of the ratio of functional to all possible sequences. I don’t believe the bit score from a BLAST search is the same thing.
Hi Art, thank you for your contribution, that allows me to clarify some important point. You are right of course, the bitscore of a BLAST search and the value of FI as -log2 of the ratio between target space and search space are not the same thing. But the point is: the first is a very good estimator of the second, provided that some conditions are satisfied. The idea of using conserved sequence similarity to estimate FI is not mine. I owe it completely to Durston, and probably others have pointed to that concept before. It is, indeed, a direct consequence of some basic ideas of evolutionary theory. I have just developed a simple method to apply that concept to get a quantitative foundation to the design inference in appropriate contexts. The condition that essentially has to be satisfied is: sequence conservation for long evolutionary periods. I have always tried to emphasize that it is not simply sequence conservation, but that long evolutionary periods are absolutely needed. But sometimes that aspect is not understood well,/ so i am happy that i can emphasize it here. I will be more clear. A strong sequence similarity between, say, a human protein and the chimp homologue of course is not a good estimator of FI. The reason for that should be clear enough: the split between chimps and humans is very recent. Any sequence configuration that was present in the common ancestor, be it functionally constrained or not, will probably still be there in humans and chimps, well detectable by BLAST, just because there has not been enough evolutionary time after he split for the sequences to diverge because of neutral variation. IOWs, we cannot distinguish between similarity due to functional constraint and passive similarity, if the time after split is too short. But what if the time after split is 400+ million years, like in the case of the transition to vertebrates, or maybe a couple billion years, like in the case of ATP synthase beta chain in E. coli and humans? According to what we know about divergence of synonimous sites, I would say that time windows higher than 200 million years begin to be interesting, and probably 400+ million years are more than enough to guarantee that most of all the sequence similarity can be attributed to strong functional constraint. For 2 billion years, I would say that there can be no possible doubt. So, in this particular case of long conservation, the degree of similarity becomes a good estimator of functional constraint, and therefore of FI. The unit is the same (bits). The meaning is the same, in this special context. Technically, the bitscore measures the improbability of finding that similarity by chance in the specific protein database we are using. FI measures the improbability of finding that specific sequence by a random walk from some unrelated starting point. If the sequence similarity can be attributed only to functional constraint, because of the long evolutionary separation, then the two measures are strongly connected. Of course, there are differences and technical problems. We can discuss them, if you want. The general idea is that the BLAST bitscore is a biased estimator, because it always underestimates the true FI. But that is not the important point, because we are not trying to measure FI with great precision. We just need some reliable approximation and order of magnitude. Why? Because in the biological world, a lot of objects (in this case, proteins) exhibit FI well beyond the threshold of 500 bits, that can be conventionally be assumed as safe for any physical system to infer design. So, when I get a result of 1250 bits of new FI added to CARD11 at the start of vertebrate evolution, I don’t really need absolute precision. The true FI is certainly much more than that, but who cares? 1250 bits are more than enough to infer design. To all those who have expressed doubts about the value of long conserved sequence similarity to estimate FI, I would simply ask the following simple question. Let’s take again the beta chain of ATP synthase. Let’s BLAST again the E. coli sequence against the human sequence. And, for a moment, let’s forget the bitscore, and just look at identities. P06576 vs WP_106631526. We get 335 identities. 72%. Conserved for, say, a couple billion years. My simple question is: if we are not measuring FI, what are we measuring here? IOWs, how can you explain that amazing conserved sequence similarity, if not as an estimate of functional specificity? Just to know.gpuccio
August 26, 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 354 Bill Cole 356 GP 357 Bill Cole 360 368 GP 369 370 374 375 381 387 388 395 398 to be continued...PeterA
August 26, 2019
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GP @ 397: Please, don't pay attention to my posts until after you're done with the interesting discussion you're engaged in with the folks at another website. That should keep you quite busy for some time. I don't know how long that discussion may take, but it could last longer than some of us may expect. BTW, I noticed that apparently you posted @387-388 after 1:00 am (Aug 26) in your time zone. Then you posted @395-397 before 11:00 am (Aug 26) in your time zone. I think you should not be pressured to post your comments or answers. You should do it at your own convenience. Whoever might be interested in what you have to say should wait patiently. I look forward to learning from your polite discussion with another website. I appreciate your time and effort to post your comments here too for the rest of us. Thus we don't have to visit another website to follow your discussion. That's very nice of you. I'm also posting information for the other folks that comment here and for your relatively numerous anonymous readers in this thread. :)OLV
August 26, 2019
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Swamidass et al. : I see that many comments here are about the relationship of my analysis with a possible philogenetic analysis. I am trying to understand better what you mnean and why you suggest that. Maybe some further feedback from you would help. I will try to clarify a few points about my analysis that, apparently, are not well understood. My analysis is focusing on the vertebrate transition only because it is very easy to study it. A number of circumstances are particularly favorable, as I have tried to explain. In particular, the time pttern of the pertinent splits, and the presence of sufficient protein sequences in the NCBI database to make the comparisons, and of course, the very good data about the human proteome. But in no way I am trying to affirm that there is something special in the vertebrate transition. There is a lot of functional information added at that time, and we can easily check the sequence conservation of that information up to humans. However, the same thing probably happens at many other transitions. So, why do I find a lot of FI at the vertebrate transition? It’s because I am looking for FI specific to the vertebrate branch. Indeed, I am using human proteins as a probe, and humans are of course vertebrate. My analysis shows that a big part of the specific FI found in vertebrates was added at the initial transition. It is not cpmparing that to what happens in other branches of natural history. Just to be clear, I could analyze in a similat way the transition to hymenoptera. In that case, I would take as probes the protein sequences in some bee, for example, and blast them against pre-hymenoptera and some common ancestor of the main branches in that tree. I have not done that, but it can be done, and it would have the same meaning of my vertebrate analysis: to quantify how much specific FI was added at the beginning of that evolutionary branch. I am not saying that vertebrates are in any way special. I am not saying that humans are in any way special (well, they are, but for different reasons). 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. OK, time has expired. More in next post.gpuccio
August 26, 2019
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OLV: Thank you for the interesting links. I appreciate them. As you can see, I am rather busy now with our kind interlocutors at PS, but I hope I can have a look at them as soon as possible! :)gpuccio
August 26, 2019
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ET at #389: You are correct, there is some difference between Swamidass'approach and mine, and I believe it is due to some misunderstanding on his part of the concept of FI. At least, that was my impression when I had a look at his posts about cancer, mentioned above. It is true that I talk of single proteins, however in principle one can consider the possible states of a whole genome in reference to some function, and that's what he seems to do in the cancer example. However, one must be very careful about FI computation. But I don't want to anticipate this discussion, because I will deal with it after I have answered other comments at PS. Thank you for your continued attention. As expected, this parallel discussion is requiring much of my time, so I apologize if I will maybe be slower in answering comments.gpuccio
August 26, 2019
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Joshua Swamidass at PS:
Great. That is really helpful. You are agreeing then that: Human-to-human genetic variation is a negative control. Viral evolution is a negative control. Cancer evolution is a negative control. You add the, appropriate, caveat that the horizontal transfer of genes is not a design based infusion of information. You also suggest as a negative control: Experiments that do not include “intelligent” selection (for example, Lenski’s experiment) “Intelligent selection” is poorly defined by I think I get what you mean. It seems also that, correctly, this would include both in silico simulations and in vitro experiments. Both approaches are valid “experiments” if conducted correctly. That is great news. From here, there are two ways forward I see. First, I want to hear your response to what we have written already about your analysis. That seems the place to start. This should clarify a great deal about your methodology and what you are precisely claiming. Second, after your methodology and refined and clarified, perhaps we will circle back to looking at some of these other cases. To have a preview of what this might look like, see here: Computing the Functional Information in Cancer . https://discourse.peacefulscience.org/t/computing-the-functional-information-in-cancer/1646 However, let’s not distract from the first point too soon. Looking forward to it.
OK, now I will answer the main points raised in the comments here, and then we can discuss your arguments about cancer. It will be a good way to explain better what FI is, how it should be used, and its role in design inference. I am looking forward to it, too! :)gpuccio
August 26, 2019
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The Standard Graphical Notation for Biological NetworksOLV
August 25, 2019
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Check this out: Hit and Run Transcriptional Repressors Are Difficult to Catch in the Act Video  
Transcriptional repressors and activators may function by different mechanisms and may be resident or absent at different stages of gene regulation. In this contribution, an example is shown of how an activator and a “hit and run” repressor may function and how this would affect detection by assays such as chromatin immunoprecipitation.
Transcriptional silencing may not necessarily depend on the continuous residence of a sequence?specific repressor at a control element and may act via a “hit and run” mechanism. Due to limitations in assays that detect transcription factor (TF) binding, such as chromatin immunoprecipitation followed by high?throughput sequencing (ChIP?seq), this phenomenon may be challenging to detect and therefore its prevalence may be underappreciated. To explore this possibility, erythroid gene promoters that are regulated directly by GATA1 in an inducible system are analyzed. It is found that many regulated genes are bound immediately after induction of GATA1 but the residency of GATA1 decreases over time, particularly at repressed genes. Furthermore, it is shown that the repressive mark H3K27me3 is seldom associated with bound repressors, whereas, in contrast, the active (H3K4me3) histone mark is overwhelmingly associated with TF binding. It is hypothesized that during cellular differentiation and development, certain genes are silenced by repressive TFs that subsequently vacate the region. Catching such repressor TFs in the act of silencing via assays such as ChIP?seq is thus a temporally challenging prospect. The use of inducible systems, epitope tags, and alternative techniques may provide opportunities for detecting elusive “hit and run” transcriptional silencing.
OLV
August 25, 2019
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More on comment @390: Pathway StudioOLV
August 25, 2019
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Check this out: Biological Pathway Specificity in the Cell—Does Molecular Diversity Matter? Video
Biology arises from the crowded molecular environment of the cell, rendering it a challenge to understand biological pathways based on the reductionist, low?concentration in vitro conditions generally employed for mechanistic studies. Recent evidence suggests that low?affinity interactions between cellular biopolymers abound, with still poorly defined effects on the complex interaction networks that lead to the emergent properties and plasticity of life. Mass?action considerations are used here to underscore that the sheer number of weak interactions expected from the complex mixture of cellular components significantly shapes biological pathway specificity. In particular, on?pathway—i.e., “functional”—become those interactions thermodynamically and kinetically stable enough to survive the incessant onslaught of the many off?pathway (“nonfunctional”) interactions. Consequently, to better understand the molecular biology of the cell a further paradigm shift is needed toward mechanistic experimental and computational approaches that probe intracellular diversity and complexity more directly.
 OLV
August 25, 2019
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