18 Mar 2009
In honour of Charles Darwin's 200th birthday .
Why does a string of DNA instructions end up getting chopped up into distinct groups of genes and chromosomes ?
The underlying forces are
1. Competition (Least cost)
2. Co-operation .(Most benefit)
DNA strings are prone to duplication and insertion during cell-division . Ie , similar strings recur . Similar strings have an evolutionary advantage in co-operating .
What is good for copyA is good for copyB .
They are inherently more secure against random , non-functional mutations .
But if a copy has a beneficial mutation , the competition mechanism will increase it's incidence .
This means stability from random mutation , and propagation of favourable traits .
In competition with other copy-strings , this complex of similar strings clump together to form a clump (gene).
The expression of these genes are involved in the evolutionary process . (Epigenetics , et al) . This leads to chromosomes (clumps of genes) and species (clumps of chromosomes) .
This is a fractal process , repeated throughout Terran organisms .(Cultural memes , etc)
Is this universal ?
Only if the base information string is delineated and a non-circular string .
Ie , even Terran organisms with circular DNA will be different in major ways . Since they do not need "end" markers , speciation does not occur and they merrily swop genetic material . (Bacteria ,etc) They might incorporate packages like genes , but it is not their basic organization . (This has lately been realized . Bacterial species are mostly a human measurement artifact.)
Group-selection is built into the heart of the DNA-system .
The relationship between Competition and Co-operation is what is important .
The optimal relationship :
Co-operation involves existing relationships (ie reserves) . Competition involves future putative advantages .
From http://andreswhy.blogspot.com "NewTools: reserves" , it is immediately clear that the least-error route is 28%-38% co-operation (average 33%), the rest is competition . Hence multi-cellurarism and speciation , with a high turnover .
Least-error does not mean maximum benefit . A healthy biosphere should have both .
This implies old-age in multi-cellular species
Stem cells compete too much (2/3), and do not co-operate(1/3) enough .
The Hayflick limit can be calculated directly from this principle .
This is for species optimization . For individual optimization the ratios differ .
An interesting Analogy : computers
The software faces the same evolutionary pressures as living organisms.
1.Operating system kernel – a single gene with many epigenetic switches (Application Interface(API) calls )
2.It is more efficient to do upgrades on a single Operating System(OS) as far as co-operation is concerned . Why we have species like MicroSoft . (Open-source software is more like bacteria.)
3.Different OS systems compete , but similar ones co-operate .
4.Duplication occurs with parallel processing : different processors access different API's of the OS .
5.There is strong evolutionary pressure to compress and minimize .
An interesting speculation :
"How-to" information is still encoded in the DNA-epigenetic system .
Backward-compatibility in OS terms .
We know that highly compressed information is indistinguishable from random noise.
We know that embrio's recapitulate previous evolutionary stages .
Where do they get the information to do this ?
From compressed data stored in the DNA , requested and decompressed by the epigenetic system (PNA mainly)
Recent environmental things affecting the mother / father gets added in to give the offspring a better chance by using recipes from previous compressed data . Speciated organisms are not as efficient at this as bacteria .
The computer-equivalent would be a compressed OS that decompresses on an epigenetic request (ie API call) that can be more efficiently done by it .
We already have that (multiple Operating systems on the same PC . The user selects the most efficient OS for what he wants to do . The User here would be the epigenetic