Genetic Toolkits and Multicellularity

[alloytoo]

Nice South African site about South African news and South African Broadband. I don't pretend to know much about IT, therefor I do not post in IT sections telling people what IT is and what it is not. Trolling? Talk about genetic toolkits in the science section of an IT site is trolling now? Logic?

Logic is something you should avoid. Your inadequacy is proven.

IT folks congregate on IT sites, some like to shoot the breeze in the offtopic and general discussions.

This is not a science site.

Here is the problem for you. These topics have been discussed in other forums without any animosity or mud-slinging. Why is it that here the main protaganists for such behaviour are you, wizzy, cyghost and rwenzori? You do the math . Pretend to know what science is but like to fling mud...mmmm pretenders. I guess that is the kind of behaviour one can expect from those who really truely believe it in their hearts that all our actions in the greater scheme of things is pointless. Sad really.

Yes your persistant derision is indeed sad.

 

[Phronesis]

IT folks congregate on IT sites, some like to shoot the breeze in the offtopic and general discussions.

This is not a science site.

Ah, chasing away people that are not IT folk? Look, I think it is pretty obvious you are not an IT person (accounting seems to be your thing), so if that is your attitude, perhaps you should apply it to yourself as well. This is not a site to sharpen your mudslinging talents.

Yes your persistant derision is indeed sad.

Sad that you don't grasp the magnitude of your own hypocricy. And you have the audacity to critique others on their logic.

 

[alloytoo]

Ah, chasing away people that are not IT folk?

Not at all, but we do politely (and in your case repeatedly) ask them to summarise in their own words.

Look, I think it is pretty obvious you are not an IT person (accounting seems to be your thing), so if that is your attitude, perhaps you should apply it to yourself as well. This is not a site to sharpen your mudslinging talents.

Again, logic is not your thing. Best not make "obvious" assumptions.

Sad that you don't grasp the magnitude of your own hypocricy. And you have the audacity to critique others on their logic.

Hmmm perceived hypocrisy based on your flawed logic and assumptions.

 

[Phronesis]

Not at all, but we do politely (and in your case repeatedly) ask them to summarise in their own words.




Again, logic is not your thing. Best not make "obvious" assumptions.



Hmmm perceived hypocrisy based on your flawed logic and assumptions.

And back and forth the mudslinging goes (yes I am guilty). Take this as an apology, please forgive me. Hopefully the rest of this thread can be free of animosity and mudslinging. To get this back on track, a few points to ponder and discuss.

1) Most of the tools for the hedghog signaling pathway toolkit are present in prokaryotes (bacteria and archaea). Note that this toolkit plays a crucial role in cell patterning, cell proliferation and participates in the development of tissues and organs during the stages of animal development.
2) Note that the origin of multicellular body plans roughly coincide with an increase in atmospheric oxygen pressure (this is what is discussed in the paper you never discussed, just alluded to) as well as the first bona fide hedgling (Read up on hedgelings). Remember, hedglings are the only examples of post-translational sterolation (addition of cholesterol) of proteins in contempory biology. Why is this interesting? Well, oxygen is needed for cholesterol synthesis, more importantly, oxygen is needed for placing the hydroxyl group in the 3-position of cholesterol which plays a crucial role in subsequent transformations (including sterolation). Thus, while large parts of the hh-signaling pathway was present, a little extra oxygen was needed to unlock multicellular signaling capabilities of hedglings.
3) Therefore, words like “pre-existing”, “latent” and “potential” seem apt in describing the hedghog signaling pathway and the unfolding of multicellular body plans in relation to the increase in atmospheric oxygen pressure. “Innovation” perhaps not so much, seeing that only real innovation was bought on about by life itself namely the increase in atmospheric oxygen. This increase in atmospheric oxygen in turn seemed to have unlocked the pathways to multicellular body plans (>3 cell types).

Point three is perhaps relevant to why the author chose those words.

4) In addition to point 3, not only were a bulk of the toolkits for multicellularity present in prokaryotes, but the increase in oxygen bought about by life unlocked the signaling capabilities of the hedgehog signaling pathway which relies on oxygen. Now to further understand why this is, consider the following article:
Acquisti C, J Kleffe & S Collins (2007). Oxygen content of transmembrane proteins over macroevolutionary time scales. Nature 445: 47-52.

Conclusion
In this study, we have shown that transmembrane proteins can be divided into two groups according to their oxygen content. Independent topology prediction reveals these same two groups. We have shown that the proportion of receptors to channels increases over time and coincides with a change in cellular organization. In addition, older proteomes contain less oxygen per residue and produce fewer high-oxygen proteins. Taken together, this suggests that oxygen use was selected against in these proteomes. This constraint lessened over time as the concentration of atmospheric oxygen increased, which resulted in the extracellular domains of transmembrane proteins increasing in size over time faster than the internal domains. Consequently, we propose the following hypothetical mechanism: atmospheric oxygen concentration constrained the topology of ancient transmembrane proteins by limiting the number and size of external domains that could be formed. Any mechanistic explanation of how atmospheric oxygen concentration limited the number and size of external domains is necessarily speculative. One possibility is that it was simply futile to exude large, oxygen-rich domains in a reducing atmosphere where oxidized amino acids could have been rapidly reduced. In this case, the use of oxygen-rich amino acids would have been selected against by natural selection because protein structure would have been more robust when fewer oxidized residues were exuded.

Stated differently, eukaryotic proteomes were more receptive and robust in accepting oxygen-rich amino acids.

Linking this to the timing of appearance of eukaryotic cells implies that the oxygen content is preferentially increased in receptors, and that this increase affects receptor function. This makes intuitive sense because the external domains of receptors required for communication have specific secondary and tertiary structures, many of which have some minimum size [23]. This is consistent with the bias we found towards having both longer and more oxygen-dense external domains in receptors relative to channels, and with the fact that eukaryotic genomes encode more and larger receptors than do prokaryotes. This suggests that protein oxygen content itself is important, rather than being a proxy for some other property.

Therefore, eukaryote proteomes were more receptive of oxygen-rich amino acids, this in turn aided in the development of secondary and tertiary structures needed for communication. A pre-existing property of eukaryotes was able to take more advantage of the change in atmospheric oxygen pressure. This lead to the unfolding of toolkits for multicellular body plans. But this change was bought on by life itself (increase in atmospheric oxygen pressure).

So yes, the author of the article can consider words like "pre-existing" and "latent evolutionary potential" to be apt in describing these transitions.

See, the questions for discussion of this thread are the following (but not limited to it):
1) Why does an increase in atmospheric oxygen seem to have the effect of driving eukaryotic multilcellular life but not bacteria and archaea? Is an intrinsic and latent property present in this domain?
2) Gene loss vs innovation: How much gene loss and how much innovation (not just co-option) has occured from the LUCA? (Speculating)
3) Why did all the toolkit parts for the hh-pathway converge on a single sterolation pathway when so many other possibilities are available? Or is it the optimal possibility and random variation and selection processes used by life hit a global optimum?

 

[alloytoo]

And back and forth the mudslinging goes (yes I am guilty). Take this as an apology, please forgive me. Hopefully the rest of this thread can be free of animosity and mudslinging. To get this back on track, a few points to ponder and discuss.

Apology accepted.

Now tell me what happened to the early single celled organisms which did not have these genetic toolkits?

 

[Phronesis]

With regards to the hedghog signaling pathway, almost the whole set was present in bacterial and archaeal lineages (see OP). Some genes seem to have been lost and later arose in eukaryotic lineages. Some genes were just co-opted into a new function.

 

[alloytoo]

With regards to the hedghog signaling pathway, almost the whole set was present in bacterial and archaeal lineages (see OP). Some genes seem to have been lost and later arose in eukaryotic lineages. Some genes were just co-opted into a new function.

What happened to primative organisms (whatever they were) which did not have these toolkits?

 

[Phronesis]

What happened to primative organisms (whatever they were) which did not have these toolkits?

Prokaryotic cells had many parts. They are considered the first organisms (+-4bya). Unless abiogenesis can explain how processes spectacularly converged on a very optimal code and organisms with these toolkits that are able to respond to future changes, we won't know which cells had them and which cells did not. At present there is deep homology that stretches all the way to the beginning of life.

 

[alloytoo]

Prokaryotic cells had many parts. They are considered the first organisms (+-4bya). Unless abiogenesis can explain how processes spectacularly converged on a very optimal code and organisms with these toolkits that are able to respond to future changes, we won't know which cells had them and which cells did not. At present there is deep homology that stretches all the way to the beginning of life.

Which leaves you with personal increduality.

A random events can happen succesfully on the very first attempt, or the 2nd, or the 17th billion/trillion.

Certainly there would have been simpler organisms subject to Natural selection.

 

[Phronesis]

Which leaves you with personal increduality.

A random events can happen succesfully on the very first attempt, or the 2nd, or the 17th billion/trillion.

Certainly there would have been simpler organisms subject to Natural selection.

There could have been? No evidence of them or that it is plausible. Possible sure. But that does not matter. The origins of these toolkits are moot, though still interesting (hardly personal incredulity). The fact the these toolkits are so primitive shows how biased evolution is towards a few outcomes as a result of these toolkits. If you replayed evolution, you would probably end up with the same result or very similar results, even if the gene sequences are highly divergent as a result of random noise. A case in point is the structural similarity of sliding clamps and clamp loaders in all the domains of life. Yet there is very little sequence similarity.

Perhaps even a better example is the following:
'Reverse Evolution' In Real Time Provides Key Insights Into Basic Mechanisms Of Evolution

ScienceDaily (Jan. 12, 2009) — Scientists have turned back the clock on the evolution in the fruit fly to provide key insights into the basic mechanisms of evolution.

In his book, Wonderful World, Stephen Jay Gould writes about an experiment of 'replaying life's tape', wherein one could go back in time, let the tape of life play again and see if 'the repetition looks at all like the original'. Evolutionary biology tells us that it wouldn't look the same – the outcome of evolution is contingent on everything that came before. Now, scientists at the Instituto Gulbenkian de Ciência (IGC) in Portugal, New York University and the University of California Irvine, provide the first quantitative genetic evidence of why this is so.

Well it would not look exactly the same because random variation is, well random. But look at what the study found:

Says Henrique, 'In 2001 we showed that evolution is reversible in as far as phenotypes are concerned, but even then, only to a point. Indeed, not all the characteristics evolved back to the ancestral state. Furthermore, some characteristics reverse-evolved rapidly, while others took longer. Reverse evolution seems to stop when the populations of flies achieve adaptation to the ancestral environment, which may not coincide with the ancestral state. In this study, we have shown that underlying these phenomena is the fact that, at the genetic level, convergence to the ancestral state is on the order of 50%, that is, on average, only half of the gene frequencies revert to the ancestral gene frequencies – evolution is contingent upon history at the genetic level too'.

These findings provide further insights into the basic understanding of how evolution and diversity are generated and maintained. On the one hand, it provides evidence for evolution happening through changes in the distribution of alleles in a population (so-called standing genetic variation), from generation to generation, rather than the appearance of mutations, from one generation to the next. On the other hand, as Henrique notes, 'It has implications for the definition of biodiversity: some of the 'reversed' flies may be phenotypically identical to the ancestral flies, but they are genetically different.

Phenotypically identical (as in the case of sliding clamp) yet different in their genetic sequences.

 

[Geriatrix]

If you replayed evolution, you would probably end up with the same result or very similar results, even if the gene sequences are highly divergent as a result of random noise.

I realise its not directly related with preadapted genes but evolution, or rather natural selection would not be vastly differant if the enviroment in which the organisms have to survive was exactly the same and I mean to the last seemingly insignificant variable.

Every extinction event(like volcanic eruptions or sudden chemical or temperature changes which may have killed off certain creatures and thus developments, but not others) no matter how small or brief would have to be taken into account.

In other words random mutation would not result in random survival. It does not imply that there was a plan behind the development of a certain creature either.

 

[Phronesis]

I realise its not directly related with preadapted genes but evolution, or rather natural selection would not be vastly differant if the enviroment in which the organisms have to survive was exactly the same and I mean to the last seemingly insignificant variable.

Why would that be? Say you have organism X and you replay its evolution in environment Y 3 times. Environment Y is hot, humid and contains the same food source.
There is no gaurantee that organism X will result in organism X1 over time in environment Y every time it is replayed.
Ever threw a drop of colouring (e.g. red food colouring) in a bathtub of water and see it progress through the water. If you repeat it, you will most certainly not get the same result. However, if you add a little machine that is programmed to swim downwards, you will get similar results irrespective of the obstacles (if they are not fatal to the machine).

Now if X (as in 1st example) repeatedly reach organism X1 over time in environment Y, does that mean evolutionary directions have a set direction with regards to environment Y, and not a stochastic direction?

Every extinction event(like volcanic eruptions or sudden chemical or temperature changes which may have killed off certain creatures and thus developments, but not others) no matter how small or brief would have to be taken into account.

In other words random mutation would not result in random survival. It does not imply that there was a plan behind the development of a certain creature either.

Certain robust features are shared among all domains (e.g. replication machinery) of life. If these features serve to bias evolutionary trajectories along certain paths and avoid others, it certainly does not imply that there is no plan behind it. It certainly implies that the same solutions were found through algorithmic search (irrespective of environmental change), and that the same solutions were inevitable if the search continued long enough.

 

[Geriatrix]

Why would that be? Say you have organism X and you replay its evolution in environment Y 3 times. Environment Y is hot, humid and contains the same food source.
There is no gaurantee that organism X will result in organism X1 over time in environment Y every time it is replayed.

Isn't that because the real life environmental variables change? You can't get organismX to exactly replay its evolution in real life.
If you run a computer simulation and you do not randomize the environmental parameters(as a real life test would do, however slight the variations may be), would the result of the interaction between the identical randomized parametered organism not be the same?

For instance, 2+2 always equal 4. But in real life it can't be amused that the environment=2(and organism for that matter), the best you could hope for is probably something like 1.0000852 or maybe 2.00000012. That would make a difference in the calculation either way. And that result as an input to another vast and complex calculation could certainly change that calculations result.

Does that make any sense?

Ever threw a drop of colouring (e.g. red food colouring) in a bathtub of water and see it progress through the water. If you repeat it, you will most certainly not get the same result.

Yes, because the environmental variables would not be the same.
Things that would alter the outcome of such a test would the particular volume(to the last molecule) of water in the second bathtub, the angle you dropped the ink in, the exact amount of ink, the temperature of the ink and the water, the turbulence on the water surface and the air while the drop is falling. You see what I'm saying?

However, if you add a little machine that is programmed to swim downwards, you will get similar results irrespective of the obstacles (if they are not fatal to the machine).

Well yes both would go down in the general sense, but I'm sure it would not be at EXACTLY the same speed, trajectory and end up at exactly the same position. There will always(in real life tests) be some sort of variation in the results of the outcome.

The change in the play between the device(like those mentioned above) and its properties compared to the water and the forces raging inside its volume(like those mentioned above) and outside influences on its volume would always be different. Thats the nature of reality.

Now if X (as in 1st example) repeatedly reach organism X1 over time in environment Y, does that mean evolutionary directions have a set direction with regards to environment Y, and not a stochastic direction?

Stochastic, wow I had to google that...
The problem is you are assuming that x will always be the exact same x and that x1 will always be exactly x1 and that the environment will aways be exactly y. Real life situations like that do not exist in the broad sense that we're discussing here.

Certain robust features are shared among all domains (e.g. replication machinery) of life. If these features serve to bias evolutionary trajectories along certain paths and avoid others, it certainly does not imply that there is no plan behind it. It certainly implies that the same solutions were found through algorithmic search (irrespective of environmental change), and that the same solutions were inevitable if the search continued long enough.

I disagree. I feel that unless you could recreate and test that assumption with EXACTLY the same variables and I mean to the last subatomic, gravitational and force interactions, then it would be hard to determine it that statement is true.

What I assume is that certain robust features are shared simply because they survived given their individual situations.
If you changed their values earlier in their interaction with their own and their exact(not approximate) environment influences, the outcome of the later shared features would differ. Thus if the plan could potentially have taken a different turn, it would not necessarily be a plan, but simply a result of an interaction.

 

[w1z4rd]

Nice posts Geriatrix.

 

[Phronesis]

Isn't that because the real life environmental variables change? You can't get organismX to exactly replay its evolution in real life.
If you run a computer simulation and you do not randomize the environmental parameters(as a real life test would do, however slight the variations may be), would the result of the interaction between the identical randomized parametered organism not be the same?

Well, in order to accurately model ligand-proteins interactions using computer simulations that employ variations of genetic algorithms (e.g. memetic algorithms) you need to randomize the behaviour of the protein in a given medium. This is computationally demanding. But if accurate, even when the reactions are random, separate succesful runs still converge on a few end points after RV and selection. It shows that the reaction in a given environment is inevitable.

For instance, 2+2 always equal 4. But in real life it can't be amused that the environment=2(and organism for that matter), the best you could hope for is probably something like 1.0000852 or maybe 2.00000012. That would make a difference in the calculation either way. And that result as an input to another vast and complex calculation could certainly change that calculations result.

Does that make any sense?

But the end result is still four? Not sure if I understand.

Yes, because the environmental variables would not be the same.
Things that would alter the outcome of such a test would the particular volume(to the last molecule) of water in the second bathtub, the angle you dropped the ink in, the exact amount of ink, the temperature of the ink and the water, the turbulence on the water surface and the air while the drop is falling. You see what I'm saying?

Even if you normalize for all those variables as best you can, you still won't get the same result. Not so with a machine that is programmed to swim downwards.

Well yes both would go down in the general sense, but I'm sure it would not be at EXACTLY the same speed, trajectory and end up at exactly the same position. There will always(in real life tests) be some sort of variation in the results of the outcome.

The change in the play between the device(like those mentioned above) and its properties compared to the water and the forces raging inside its volume(like those mentioned above) and outside influences on its volume would always be different. Thats the nature of reality

Yes, but the outcomes will still be similar, machines at the bottom. The colouring will spread in a stochastic way.

Stochastic, wow I had to google that...
The problem is you are assuming that x will always be the exact same x and that x1 will always be exactly x1 and that the environment will aways be exactly y. Real life situations like that do not exist in the broad sense that we're discussing here.

I agree, not exactly the same, but similar, even predictable results can be obtained. Not so with the colouring. Unless a mess is the prediction.

I disagree. I feel that unless you could recreate and test that assumption in with EXACTLY the same variables and I mean to the last subatomic, gravitational and force interactions, then it would be hard to determine it that statement is true.

It does not have to be exact to the last quantum bit (don't know if that is possible), just an approxamation by taking randomness into account.

What I assume is that certain robust features are shared simply because they survived given their individual situations.
If you changed their values earlier in their interaction with their own and their exact(not approximate) environment influences, the outcome of the later shared features would differ. Thus if the plan could potentially have taken a different turn, it would not necessarily be a plan, but simply a result of an interaction.

Even if it is the case that robust features just survived given their individual situations, they still persist in all of life (e.g. replication machinery). If you are assuming a plan, sure it could take a different turn, but if you assume a plan then the persistant features could also bias and facilitate evolutionary directions. There can be many paths to the same destination and genetic algorithmic search techniques have shown why this is in pre-existing fitness landscapes. Programmed evolution...

 

[Geriatrix]

Well, in order to accurately model ligand-proteins interactions using computer simulations that employ variations of genetic algorithms (e.g. memetic algorithms) you need to randomize the behaviour of the protein in a given medium. This is computationally demanding. But if accurate, even when the reactions are random, separate succesful runs still converge on a few end points after RV and selection. It shows that the reaction in a given environment is inevitable.

Well I must say I don't know anything about modeling ligand-proteins interactions but I assume that the results obtained are general and not exact when trying to determine the reaction of a specific protein as observed in nature. The fact that the results are generally accurate simply indicate that the inputs were generally similar too. (Both use generally similar environments and ligand-proteins as inputs)

I'm sure if the environmental variables in those experiments briefly shot up to include temperatures near 1000 degrees, the protein interactions would not be the same as when the temperature were assumed to be around 20 to 100 degrees.

But the end result is still four? Not sure if I understand.

Theoretically the end result would still be four. In reality if might be 3.005 or 4.00000000001.

Even if you normalize for all those variables as best you can, you still won't get the same result.

Are you sure? Can you realistically determine that with a real life experiment with the exact same conditions every time? I don't think you can, you'd have to take into account things we can't even think of yet(like what would the influence of dark matter, which we know almost nothing about, be on that experiment? Can it be assumed that it would not have an influence?).

In my line of work(not scientific but we use very complex and smart software) we sometimes have to do fluid simulations where the software we use takes millions(or billions, depending on how realistic you want the result to be and how much CPU's and time you've got access to) of separate particles, calculated as vertex points in space, with individual vector directions which interacts with set forces and with each others forces and set masses in order to simulate fluid interactions.

Now I realize this is just a approximate simulation on a PC but the simulations results, like your ink example(yes we've actually done those and yes its a nightmare ) are ALWAYS identical, except when you add some variation in particle mass, gravity, initial states, inertia and force strength and vector variations.

Yes, but the outcomes will still be similar, machines at the bottom. The colouring will spread in a stochastic way.

The results for the machines will be similar in a general sense but not in an exact way. If you put little mines randomly on the bottom of the bathtub, the fate of a particular machine can change drastically depending on where exactly it lands. Its chances of survival will be determined by specific initial states.

The colouring will appear to spread in a stochastic way to us simply because we don't have the ability to take every variable in reality into consideration nor the ability to recreate it for experimental purposes.

I agree, not exactly the same, but similar, even predictable results can be obtained. Not so with the colouring. Unless a mess is the prediction.

If the initial variable states for the colouring were identical I believe the results would be identical too. Other wise it would mean that 2+2 <> 4.

It does not have to be exact to the last quantum bit (don't know if that is possible), just an approximation by taking randomness into account.

Approximate inputs would give approximate results wouldn't it? Thus any assumptions of complete accuracy may simply be wishful thinking.

Even if it is the case that robust features just survived given their individual situations, they still persist in all of life (e.g. replication machinery).

Yes?

If you are assuming a plan, sure it could take a different turn, but if you assume a plan then the persistant features could also bias and facilitate evolutionary directions. There can be many paths to the same destination and genetic algorithmic search techniques have shown why this is in pre-existing fitness landscapes. Programmed evolution...

I can't comment on genetic algorithmic search techniques and such, I do not have the background knowledge to discuss it.

What I am trying to say though is that these things are determined by basically extremely complex equations with extremely complex and seeming obscure variable inputs. But they still follow mathematical laws.
And if we are not able to take every single input variable into account as it relates to reality, it is not accurate to say that the results of a calculation would be the same regardless of the inputs. They may be approximately the same, but approximate inputs added to another equation may result in a very different results, which to me, indicate that there is no plan. Except if you assume the mathematical laws of the universe to be the plan. But that would be an assumption.

 

[alloytoo]

Well I must say I don't know anything about modeling ligand-proteins interactions but I assume that the results obtained are general and not exact when trying to determine the reaction of a specific protein as observed in nature. The fact that the results are generally accurate simply indicate that the inputs were generally similar too. (Both use generally similar environments and ligand-proteins as inputs)

I'm sure if the environmental variables in those experiments briefly shot up to include temperatures near 1000 degrees, the protein interactions would not be the same as when the temperature were assumed to be around 20 to 100 degrees.



Theoretically the end result would still be four. In reality if might be 3.005 or 4.00000000001.



Are you sure? Can you realistically determine that with a real life experiment with the exact same conditions every time? I don't think you can, you'd have to take into account things we can't even think of yet(like what would the influence of dark matter, which we know almost nothing about, be on that experiment? Can it be assumed that it would not have an influence?).

In my line of work(not scientific but we use very complex and smart software) we sometimes have to do fluid simulations where the software we use takes millions(or billions, depending on how realistic you want the result to be and how much CPU's and time you've got access to) of separate particles, calculated as vertex points in space, with individual vector directions which interacts with set forces and with each others forces and set masses in order to simulate fluid interactions.

Now I realize this is just a approximate simulation on a PC but the simulations results, like your ink example(yes we've actually done those and yes its a nightmare ) are ALWAYS identical, except when you add some variation in particle mass, gravity, initial states, inertia and force strength and vector variations.



The results for the machines will be similar in a general sense but not in an exact way. If you put little mines randomly on the bottom of the bathtub, the fate of a particular machine can change drastically depending on where exactly it lands. Its chances of survival will be determined by specific initial states.

The colouring will appear to spread in a stochastic way to us simply because we don't have the ability to take every variable in reality into consideration nor the ability to recreate it for experimental purposes.


If the initial variable states for the colouring were identical I believe the results would be identical too. Other wise it would mean that 2+2 <> 4.



Approximate inputs would give approximate results wouldn't it? Thus any assumptions of complete accuracy may simply be wishful thinking.



Yes?


I can't comment on genetic algorithmic search techniques and such, I do not have the background knowledge to discuss it.

What I am trying to say though is that these things are determined by basically extremely complex equations with extremely complex and seeming obscure variable inputs. But they still follow mathematical laws.
And if we are not able to take every single input variable into account as it relates to reality, it is not accurate to say that the results of a calculation would be the same regardless of the inputs. They may be approximately the same, but approximate inputs added to another equation may result in a very different results, which to me, indicate that there is no plan. Except if you assume the mathematical laws of the universe to be the plan. But that would be an assumption.

We've got cumulative Random events being sorted by variable conditions
(which themselves are while not entirely random, certainly give a good go at it.)

I would imagine the further you go back, the more different the result would be.

 

[Phronesis]

Well I must say I don't know anything about modeling ligand-proteins interactions but I assume that the results obtained are general and not exact when trying to determine the reaction of a specific protein as observed in nature. The fact that the results are generally accurate simply indicate that the inputs were generally similar too. (Both use generally similar environments and ligand-proteins as inputs)

I'm sure if the environmental variables in those experiments briefly shot up to include temperatures near 1000 degrees, the protein interactions would not be the same as when the temperature were assumed to be around 20 to 100 degrees.

Given a range of environmental constraints, similar results after evolutionary processes will be reached.

Theoretically the end result would still be four. In reality if might be 3.005 or 4.00000000001.

Depends on the assumptions you make first? Organism = 2.11000000001, environment = 1.88?

Are you sure? Can you realistically determine that with a real life experiment with the exact same conditions every time? I don't think you can, you'd have to take into account things we can't even think of yet(like what would the influence of dark matter, which we know almost nothing about, be on that experiment? Can it be assumed that it would not have an influence?).

In my line of work(not scientific but we use very complex and smart software) we sometimes have to do fluid simulations where the software we use takes millions(or billions, depending on how realistic you want the result to be and how much CPU's and time you've got access to) of separate particles, calculated as vertex points in space, with individual vector directions which interacts with set forces and with each others forces and set masses in order to simulate fluid interactions.

Quantum indeterminacy and the quantum uncertainty principle? You can use approximations (which you probaly do). Sounds interesting btw.

Now I realize this is just a approximate simulation on a PC but the simulations results, like your ink example(yes we've actually done those and yes its a nightmare ) are ALWAYS identical, except when you add some variation in particle mass, gravity, initial states, inertia and force strength and vector variations.

Do these systems account for QI? I assume they are accurate and probably don't need to.

The results for the machines will be similar in a general sense but not in an exact way. If you put little mines randomly on the bottom of the bathtub, the fate of a particular machine can change drastically depending on where exactly it lands. Its chances of survival will be determined by specific initial states.

Not necessarily. In a strict deterministic universe maybe.

The colouring will appear to spread in a stochastic way to us simply because we don't have the ability to take every variable in reality into consideration nor the ability to recreate it for experimental purposes.


If the initial variable states for the colouring were identical I believe the results would be identical too. Other wise it would mean that 2+2 <> 4.

Quantum indeterminancy might just imply that it is happening in a stochastic and that it may not be accurately modelled to the last quantum bit of information.

Approximate inputs would give approximate results wouldn't it? Thus any assumptions of complete accuracy may simply be wishful thinking.

Agreed. But with sufficient knowledge of the system a reasonable approximation can be made.

Yes?

Certainly played a role in affecting evolutionary trajectories.

I can't comment on genetic algorithmic search techniques and such, I do not have the background knowledge to discuss it.

What I am trying to say though is that these things are determined by basically extremely complex equations with extremely complex and seeming obscure variable inputs. But they still follow mathematical laws.
And if we are not able to take every single input variable into account as it relates to reality, it is not accurate to say that the results of a calculation would be the same regardless of the inputs. They may be approximately the same, but approximate inputs added to another equation may result in a very different results, which to me, indicate that there is no plan. Except if you assume the mathematical laws of the universe to be the plan. But that would be an assumption.

I am not saying the results are exactly same every time (they are not, even when not considering quantum indeterminancy), just that different runs converge on roughly the same endpoint while differnent routes might have been taken due random factors.

 

[Geriatrix]

The discussion seems to be getting lost in quoting, so I'll just try and make a summary of my question. After some quoting of course.

Quantum indeterminacy and the quantum uncertainty principle? You can use approximations (which you probaly do). Sounds interesting btw.

No not even that complicated yet(although that makes it even more obvious how much we assume), simple mass interactions make it complex enough already.

The point I'm so clumsilly trying to make is that if we are unable to accurately determinde the exact chain of events that lead to what we now observe, how can we assume that anything was planned with a purpose, and not simply cause and effect? Because the smallest little change could steer the whole general direction of the chain of events.

Do we actually know how those genes you spoke about came to be in those primitive creatures? Do we know for a fact that it only led to the hedgehog? Why a hedgehog? They haven't really changed the course of history for the better(I'm assuming of course ).
And if gene development has a purpose, why did the Dodo evolve in the first place? Or the other 90% of creatures ever born only to find themselves extinct a while later.

Do these systems account for QI? I assume they are accurate and probably don't need to.

I doubt it takes QI into account, that would be outside of the scope of our simulations. They are accurate in and of themselves, but not in trying to recreate a real event. That would need to take QI in account.
They will just result in an approxomation.

Anyways, I find those sims bothersome enough without needing to throw quatim mechanics in there as well.

 

[w1z4rd]

Or the or 90% of creatures ever born only to find themselves extinct a while later.

Actually a very good point.