Genome Length Grows by the Activity of Replication Alone

[jerrywickey]

In runs of First Colony where advantageous mutations were assigned zero probability, the length of organisms containing sequences expressing replicative ability grew in length.

 

The average number of nucleotides increased despite zero advantageous mutations.

 

Screen capture of a run where advantageous mutation are suppressed to zero, showing genome growth of nearly 200%. Disadvantageous mutations are allowed but were suppressed by replication.

 

 

Software available free at http://www.satellitemagnet.com/firstcolony

 

Screen capture of control simulation run where advantageous mutations are not suppressed, showing genome growth of the same nearly 200%. Again disadvantageous mutations were suppressed by replication.

 

 

Screen capture showing extreme genome growth of over a 880% after a run of ten times the length of time for development. Suppressing advantageous mutation. Notice disadvantageous mutations are again suppressed to nearly zero by the activity of replication.

 

 

Dr. Aller and I spent two hours trying to find the mechanism. We found it!

 

Normally, advantageous sequence find their way into a genome by random mutation, causing greater likelihood of replication as well as simultaneously adding nucleotides to the genome of that organism.

 

In other words, a larger genome is statistically more likely to contain more advantageous and disadvantageous sequences. But the genomes containing disadvantageous sequences are disadvantaged replicators. The advantaged organisms are the only ones which survive.

 

But, with activity of advantageous mutations artificially suppressed by the software parameters, the genome still grew. This was astounding to me and Dr. Aller. The power of replication alone to gather information defied reason.

 

We found the answer! We found the mechanism.

 

It is surpassingly mathematical. Prior to the influence of more developed and complex biologic activity, the connection between early evolution and the math of prebiotic chemistry is accentuated.

 

Let x represent the length of a given genome in nucleotides.

Let r represent the number of nucleotides which constitute the RNA sequence which endows replication activity.

 

Then x+1 and x-1 represent the random addition or deduction of one nucleotide to the length of the genome. Advantageous and disadvantageous characteristics of these mutations are ignored because we are examining genome growth with out the aid of advantageous mutation.

 

But the addition or deduction of a nucleotide may effect the sequence endowing replication activity. This sequence must reside with in the genome of the organism.

 

The likelihood of a mutation which adds a nucleotide disrupting the replication sequence by randomly adding a nucleotide to the sequence endowing replicative activity is 1 in r / (x+2) A random nucleotide might be added to the beginning or the end of the entire genome. The replicative sequence is just as statistically likely to reside at the end or any where in the middle.

 

While, the likelihood of a mutation which subtracts a nucleotide disrupting the replication sequence by deducting a nucleotide to the sequence endowing replicative activity is only 1 in r / x.

 

Therefore a random mutation which deducts a nucleotide is statistically more likely to disrupt replicative activity then a random mutation which adds a nucleotide.

 

Lengthening occurs with out replication disruption at a rate of (x - r) / (x +2)

Reduction occurs with out replication disruption at a rate of (x-r) / (x)

(x-r)/(x+2) is always less than (x-r)/(x)

 

Random lengthening is statistically less disruptive to replication than random reduction.

 

Jerry

[ecoli]

I'm slightly confused... is this a computer model, or did you actually sequence colonies?

 

In any case, what's the model organism/selection pressures? How do you know if a mutation is advantageous?

[Psycho]

How do you know if a mutation is advantageous?

Indeed and even if it was in reality how would you know it was in computer program if the sequence that cause the advantageous mutation didn't exist in real life.

 

I can't really see how you can turn off advantageous mutations unless you know them or which clearly we don't.

[jerrywickey]

Both questions are insightful. I have considered both these things.

 

First, this is a computer model. But it is not like other models which deal with the specific advantage or disadvantage. I looked at these models and realized the problems they introduce. My model accurately models mutations and accurately maintains at equilibrium an average sequence length of 15 nucleotides. This is the length MIT suggests might be the length of nucleotides which are assembled prebiotically.

 

That was the major motivation to write the software. I wanted the simulation to actually simulate mutations not the effects mutations might have.

 

Second, you are right, we do not know what RNA sequences could express activity. We have a few examples. An MIT study found

 

GGAAAAAGACAAAUCUGCCCUCAGAGCUUGAG

AACAUAUUCGGAUGCAGAGGAGGCAGCCUCCG

GUGGCGCGAUAGCGCCAACGUUCUCAACAGGC

GCCCAAUACUCCCGCUUCGGCGGGUGGGGAU

AACACCUGACGAAAAGGCGAUGUUAGACACGC

CAAGGUCAUAAUCCCCGGAGCUUCGGCUCC

 

carries on some limited replication activity with out any additional biology.

 

What we know with certainty is that some sequences endow the activity of replication, some sequences enhance replication, and some make replication less likely. But we do not know what these sequences are and we do not know what the exact mechanism of these sequences might be.

 

So I am simply arbitrarily assigning the sequences, ignoring the chemical activity and modeling the net effect of the activity. i.e. a replicated sequence and increased or decreased likelihood of successful replication.

 

Third, by arbitrarily assigning sequences which endow activity, the user has complete control over the environment in which these theoretical organisms exist. The user might simply make a world where no advantageous sequence exist and see what would happen.

 

By adjusting these parameters until the results match observations, we can find what these parameters must have been.

 

You just have to play with the simulations to see what I mean. Dr. Alex Aller helped me to validate the software.

 

We spent a lot of time, simulating lab work that he had actually done. The results of my software, matched his lab results almost perfectly.

 

The most exciting thing I found yet, playing with the software is that replication alone will grow a genome and that advantageous mutations cause an emergent process which organizes information.

 

Organizing information was the most consistent objection to the theory of evolution. My software, clearly shows information being collected and organized. Studying the mechanisms which cause this emergent process is very interesting to me.

 

Jerry