“All I Need is Time!” – The Modern Theory of Evolution

"Time is the hero of the plot… Given so much time the 'impossible' becomes possible, the possible

probable and the probable virtually certain. One has only to wait: time itself performs miracles." - George Wald

Sean D. Pitman M.D.August 2003

George Wald (1967 Nobel Prize winner in Medicine), "The Origin of Life," Scientific American, vol. 191 1954, p. 46; reprinted on p. 307-320, A Treasury of Science, Fourth Revised Edition, Harlow Shapley et al., eds., Harper and Brothers Publishers, 1958. p 309.

Definition: Common descent with modification

Mechanism: Random mutation and natural selection

Evolution

• Random Mutations: – Are real– Affect DNA– Must be inheritable

• Natural selection– A real force of nature– A mindless process (no goal in mind)– Is capable of detecting phenotype changes

• The phenotypic is the functional expression of the genotype

– Cannot detect all genotypic changes• Many genotypic changes are neutral

DNA

• Deoxyribonucleic acid• A chain made up of 4 chemical letters

– Adenine, Thymine, Guanine, Cytosine

• Information is coded by the sequential order of these chemical letters

• Similar to other code or language systems– Morse Code, English, computer code and

other language systems

Transcription and Translation

• Information in DNA is copied into a “working copy” called messenger RNA

• The message is then translated into another language system known as proteins

• DNA controls what, when, where and how much proteins are produced

Decoding DNA

• Three DNA “letters” (a codon) translates into one type of amino acid “letter” in a protein chain

• Twenty different letters in protein language• Sequential order of protein letters vital to function

– as with any other language system• Protein chains are folded into specific functional

3D shapes – often with the aid of other pre-formed proteins

Transcription then Translation

Proteins

• Functional units of the cell• Very specific sequential order and 3D

structures that are dependent upon the proper sequential order

• Some flexibility depending on location and class of amino acid “letter” change

• Changes in DNA and/or the resulting protein sequence may be functionally beneficial, harmful, or neutral

Changes that Aren’t

Gregor Johann Mendel - born July 22, 1822

• Father of genetics• Studied pea plant hybrids • Discovered basic principles of genetic

recombination• Darwin not aware of Mendel’s work

Genetic Recombination

Chromosome Loci

• Gene is a functional stretch of DNA that often codes for a protein

• Genes occupy specific positions on chromosomes called loci

• For a given locus, different versions of the same gene (alleles) will fit or work in this position – just like different types of wheels will work at the “wheel location” on a car

• If genes aren’t in the right locus, they will not work right (like putting seats where the tires are supposed to go)

Two Loci Per Trait

• Chromosomes come in pairs• One chromosome comes from the mother

and one from the father• Each locus on one chromosome has a

matching locus on its sister chromosome • During genetic recombination, these

chromosomes must match up exactly and interchange DNA only between matching loci

1.        Flower color: purple or white 2.        Flower position: axial or terminal 3.        Stem length: long or short 4.        Seed shape: round or wrinkled 5.        Seen color: yellow or green 6.        Pod shape: inflated or constricted 7.        Pod color: yellow or green

Different Pea Traits and Potential Form Variations

Dominance and Recessiveness

• If maternal and paternal chromosomes have different forms at the same loci on the twin chromosomes, which form will be expressed?

• Answer – The dominant form– Sometimes there is co-dominance,

incomplete dominance, or polygenetic governance of a particular trait

Mating a purely Dominant with a purely Recessive line

“Homozygotes” and “Heterozygotes”

Phenotype

Genotype

The Static Gene Pool• Fantastic potential for expressed variations• Made up of all the genes in a population• Many different options (alleles) per gene locus

exist in a gene pool• Genetic recombination produces random

allelic swapping at specific loci, but it does not produce new alleles or new loci– i.e., a red bike with knobby wheels or a yellow bike

with thin wheels or a red bike with wide wheels etc., but the basic bike setup would not change nor would the variation potential change via part “recombination”

The Evolving Gene Pool

• Must go beyond what genetic recombination can offer

• Dependent upon random mutation to existing DNA sequences to create new alleles and genetic loci in the gene pool of options

The Potential Space Checkerboard of Sequence Options

A Comparison to the English Language

• On our checkerboard each square represents a different potential sequence of a given length– For example, the total number of potential 3-letter

sequences is 17,576. Each one of these sequences is represented by a different square on our checkerboard.

– Some squares represent sequences that are functionally beneficial, others are functionally detrimental, but most are functionally neutral

Evolving . . . Small Words

• 3-letter checkerboard size: 17,576 squares• 2-letter checkerboard size: 677 squares• Defined 3-letter words: 972 (ratio 1:18)• Defined 2-letter words: 96 (ratio 1:7)• A random change in a 2- or 3-letter sequence will

often produce a new meaningful word:

cat – hat – bat – bad – bid – did – dig – dog

http://www.yak.net/kablooey/scrabble.html

http://www.aivazoglou.gr/2.html

Evolving . . . Bigger Words

• 7-letter checkerboard size: 8,031,810,176 squares• Meaningful 7-letter words: 23,109* • Meaningless 7-letter sequences: ~8 Billion• Each meaningful word is surrounded by 347,561

meaningless sequences – like an ocean of squares on our checkerboard

• The likelihood that a random change to a given 7-letter sequence will end up on a meaningful word is not nearly as good as it was for 2- and 3-letter sequences

• Much more time is required to find new 7-letter words on our checkerboard of sequence options

*http://www.aivazoglou.gr/7.html

Moving Up to Short Phrases• “Methinks it is like a weasel” – Shakespeare

– This sentence is 28 characters in length

• Checkerboard size: 10e40 squares (27e28) – If each square were 2cm across this checkerboard would be 1,000

trillion km across (~621 trillion miles)• ~93 million miles to the sun• ~25 trillion miles to nearest star (Alpha Centauri)

• How many meaningful 28-character phrases are there in the English language?– I’m not sure – perhaps a trillion?– If there were a trillion, each meaningful phrase would be surrounded

by a vast ocean of ten billion trillion trillion meaningless phrases

• Changing one letter per second, with no repeats, it would take over 300 million trillion years to come across just one of the trillion meaningful phrases on our checkerboard

• Math: 10e40 / 10e12 / 60 / 60 / 24/ 365 = 3.79 x 10e20

Increasing the PopulationIt does help – but how much?

• Take the same scenario as before and increase the number of evolving phrases a trillion fold– This would search out the squares on our

checkerboard a trillion times faster– Still works out to be over 300 million years to find

just one of one trillion meaningful phrases

• Note: We aren’t even talking about beneficial

phrases here - just meaningful phrases

Expanding Checkerboardvs.

Expanding Population

• If the average distance between any two meaningful sequences is two character changes (two steps on the checkerboard), the average random walk distance for one individual is more than 729 steps (27e2).

• Note also that random genotypic walk cannot be guided by a phenotypic selection process– As one moves from one meaningless square on the

checkerboard to another meaningless square, no change in meaning takes place, and therefore no change in phenotype - such changes are referred to as “neutral” mutations

• For the same 2-mutation gap– If the population were increased to 1,000

sequences, the random walk for this population as a whole would average less than 1 step

– Again, increasing the population decreases the average time required for the evolution of new meaningful sequence codes

– But, can the population expansion keep up as the average gaps between meaningful sequences expands?

Doubling the Gap• Lets expand the average gap from 2 to 4

character changes or “steps” on our checkerboard

• Now, instead of 729, there are 531,441 meaningless squares for every one meaningful square – With each doubling of the neutral gap, the

potential space increases by a factor of 2– In order to evolve at the same rate, our

population would also have to increase in size by a factor of 2

Doubling a Few More Times

• A gap of 8 : 282 billion meaningless squares on the checkerboard

• A gap of 16: 79 billion trillion . . .

• A gap of 32: 1 billion trillion trillion trillion . . .

Note: The junk-squares on the checkerboard quickly outpace the ability of a given population to keep up with the expanding neutral gaps found at higher and higher levels of functional complexity.

Comparison to Real Life?

• A gap of 32 Amino Acids: 4.29 x 10e41 (~100 thousand trillion trillion trillion)

• Total bacteria on Earth: 5 x 10e30 (5 million trillion trillion)*

• A checkerboard with 10e41 meaningless AA squares divided among 5 million trillion trillion bacteria would require each individual bacterium and its offspring (just one in a steady state population) to undergo a random walk of around 85 billion steps before success would be realized

• Time per step: ~10 years• Based on a very high mutation rate of 10e-5 per sequence

per generation (one mutation every 100,000 generations) with a generation time of 1 hour

• Average time to success: ~850 billion years

*http://news.bbc.co.uk/1/hi/sci/tech/158203.stm

Real Life Examples of Gene Pool Evolution?

• Antibiotic Resistance - yes• Single Protein Enzymes - yes• Multiple Protein Systems - not yet

• Alife Computer Software - sorta

De Novo Antibiotic Resistance

• Is a real example of evolution in action• Is the result of mutation and natural selection• Is based on antibiotic-target specificity

– Streptomycin – 30S ribosomal subunit– Erythromycin – 50S ribosomal subunit– Fluoroquinolones – DNA gyrases and topoisomerases– Rifampin – beta-subunit of RNA polymerase– Penicillins – penicillin binding proteins (cell wall synthesis)– Vancomycin – stem peptides involved with cross-linking– Isoniazid – inh enzyme involved with mycolic acid synthesis

Antibiotic-Target Interference

• High ratio of interfering sequences in sequence space (like ratio in 2 and 3-letter words in sequence space)

• Much easier to interfere with or break a previous interaction/function than to create a brand new function

• Remember Humpty Dumpty and all the kings men!

Antibiotic Evolution is Predictably Rapid

• The higher the antibiotic-target specificity, the easier it is to interrupt this interaction

• Odds are good that a random mutation will result in some degree of interference = rapid antibiotic residence

• In real life, this is exactly what happens– Antibiotic resistance evolves extremely rapidly in all

types of bacteria when exposed to an antibiotic – The same principle holds true for mechanisms of

drug resistance in other life forms (malaria, rats, weeds, etc.)

Up a Level to Single Protein Enzymes

• Have independent function not dependent upon the disruption of a pre-established function/interaction

• Specific AA sequence and 3D orientation vital to enzyme function

• If sequence changed too much, all function will be lost

Invariant Amino Acids• Cytochrome C (~100 AA)• Comparisons of 40 species: 35% invariant AA

– Humans and Bread Mold - 66% Identical– 40% more are restricted between 2 or 3 AA options

• similar in chemical character – hydrophilic, hydrophobic, basic acidic, or neutral with respect to water

– Only a few positions can tolerate a wide range of AA substitutions

100aa Checkerboard• 10e130 squares on our 100aa checkerboard

– One trillion trillion trillion trillion trillion miles wide– Note that there are only ~10e80 atoms in the visible universe!*

• How many squares with cytochrome c function?– High estimates: ~10e60 (lower estimates: ~10e40)

– Cytochrome C vs. Total: At least 1 in ~10e70

• How many other functions requiring at least 100aa would be beneficial to a given life form in a given environment? – Human genome thought to have around 35,000 protein coding genes– 35,000 times 10e60 is only ~10e64

• ~10e64 nonfunctional sequences for every functional sequence

*http://www.nso.edu/sunspot/pr/answerbook/universe.html

But Some Enzymes Have Evolved in Real Time!

• Barry Hall and Lactase Evolution in E. coli– LacZ genes deleted in E. coli

• Code for tetramer lactase enzyme of ~1,000aa per subunit

– Mutant bacteria grown on lactose enriched media– In one or two generations, the lactase function evolved– Completely different gene (ebg gene – hexamer of

~1000aa per subunit) evolved the lactase function with a single point mutation

– What are the odds?!

Double Mutant E. coli

• Hall deleted both the LacZ and ebg genes from certain colonies of E. coli

• Despite being grown on lactose enriched media for tens of thousands of generations, while undergoing high mutations rates, the lactase function was never evolved

• Hall described these bacteria as having, “limited evolutionary potential”

– Note: These same bacteria would quickly evolve resistance to any antibiotic environment

Limited Evolutionary Potential

• Many species of bacteria cannot evolve the lactase function despite hundreds of thousands of generations of records– Salmonella, Proteus, Pseudomonas etc.– Lactase function would be beneficial if it evolved

• What is the limiting factor?– A low ratio of beneficial functions vs. potential

functions at this level of complexity (~1,000aa level with around 10e1300 options)?

It Only Gets Worse

• There are quite a few examples of single protein functions evolving (nylonase, lactase, antibody enzymes, enzyme cascades etc.)

• However, there are NO examples of multi-protein functions evolving - period– Where multiple proteins are required to work

together at the same time in a specific orientation with each other (i.e., bacterial motility systems such as the flagellar apparatus)

The Famous Flagellum

• 50 or so different proteins working in concert

• Each protein part averages a few hundred amino acids

• Total sequence length: Probably more than 20,000aa

• Sequence space: At least10e26,000 potential differences

• So, what is the ratio of all beneficial sequences requiring at least 20,000aa compared with the total of 10e26,000 potential sequences at this level of complexity?

Flagellar Subsystems

• Parts of the flagellum used in other systems of function– Similar proteins in type III secretory systems (TTSS)

• TTSS coded for by ten genes – each homologous to genes in the bacterial flagellum code (The flagellum requires an additional 30 or 40 unique genes)

• Nguyen et al. 2000 suggest that TTSS evolved from the flagellum and not vice versa

• TTSS only found in animal and plant pathogens• Argument is that TTSS could only have existed since the evolution

of animal and plants – so it evolved from the flagellum• TTSS genes can be carried on plasmids and transferred

horizontally – flagellar genes are not carried on plasmids

ALL the Right Parts

• What if all the needed parts to make a flagellum already existed as parts of other systems of function?

• Whenever a flagellum was needed, these parts would just self-assemble to make a flagellum since all the parts are there – right?

• Would a watch self-assemble if I put all the necessary watch parts in a bag and shook the bag for a billion years?

Part Assembly• Remember, the information in DNA controls not only part

production, but the timing and location of part production.• DNA is like a biochemist who synthesizes complex

compounds. In order for the synthesis to be successful, the sequential order and amount of chemicals added to and removed from solution is vital to the formation of the final product.

• The parts, by themselves, without this higher informational input, will not self-assemble to produce much of anything.– Example: All the necessary amino acids are always there to

produce any protein that would be beneficial to the cell. But, left to themselves, the AAs do not self-assemble in a functional way that is greater than the sum of their parts.

“Wishful Speculations”

• “All of this [current hypothesizing about how evolution must have happened] is highly speculative, and accounts for cell biologist Franklin Harold's frank admission”:

– "There are presently no detailed Darwinian accounts of the evolution of any biochemical or cellular system, only a variety of wishful speculations."

http://www.designinference.com/documents/2003.02.Miller_Response.htm

- William Dembski

Harold, F. 2001. The Way of the Cell: Molecules, Organisms and the Order of Life. New York: Oxford University Press.

Computer Evolution

• Lenski et. al., The Evolutionary Origin of Complex Features, Nature, May 2003

• Goal - the evolution of logic functions

• Population - 3600 individuals divided among 50 different “genomes”

• Each individual had 50 lines of meaningful computer code

• 15 lines of code required for the self-replication function

• 35 lines were repeated copies of a no operation code (no logic function)

• Random translocation mutations of lines and portions of lines of code

• The evolution of specific logic functions rewarded with increased reproductive fitness

• Generations – 15,873

• Result – 23 of 50 genomes evolved the highest logic function (EQU)

A Few More Details

• The size of the checkerboard: 5.6 x 10e70 number of options– Based on ancestral genome length of 50 with 26 possible instructions at each site

• Size of gap between starting code and EQU function: 16 steps/mutations• Other “beneficial” intermediate functions: NAND, AND, OR, NOR, XOR, NOT

• Average gap between all functions: ~2.5 steps (search space: 3,400 options between each step – every option quickly covered with a population of 3,600 individuals)

• Average number of steps required to achieve EQU function in the 23 successful genomes: 103

– 45 beneficial, 18 detrimental, 48 neutral

Removing Intermediate Steps

• Lenski also experimented with an evolutionary scenario where only the EQU function was defined as beneficial

• Result: NONE of the 50 populations evolved the EQU function• As expected, these populations tested more of the potential sequence

space than those in the “reward-all environment” (2.15 x 10e7 vs. 1.22 x 10e7; P<0.0001, Mann-Witney test)– Still, this was a minute 1 x 10e-60% of the total space on the checkerboard– This is like searching out a grain of sand when the entire universe remains

unexplored– The EQU function could be on any grain of sand anywhere in the universe

• Without the intermediate functions arbitrarily defined as beneficial, the resulting neutral gap of 16 steps was simply impossible for Lenski’s population of computer creatures to get across – Even in trillions upon trillions upon trillions of years.

“All Evolution Needs is Time”

Yeah!

A WHOLE LOTTA TIME!!!