modeling molecular evolution - tbi › ~pks › presentation › minneapolis-03.pdf · theory of...
TRANSCRIPT
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Modeling Molecular EvolutionThe Origin of Information and Learning in Populations
Peter SchusterInstitut für Theoretische Chemie und Molekulare
Strukturbiologie der Universität Wien
Agent Based Modeling and Simulation
Minneapolis, 03.– 06.11.2003
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Web-Page for further information:
http://www.tbi.univie.ac.at/~pks
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1. RNA structure, replication kinetics, and origin of information
2. Evolution in silico and optimization of RNA structures
3. Random walks and ‚ensemble learning‘
4. Sequence-structure maps, neutral networks, and intersections
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1. RNA structure, replication kinetics, and origin of information
2. Evolution in silico and optimization of RNA structures
3. Random walks and ‚ensemble learning‘
4. Sequence-structure maps, neutral networks, and intersections
![Page 6: Modeling Molecular Evolution - TBI › ~pks › Presentation › minneapolis-03.pdf · Theory of molecular evolution M.Eigen, Self-organization of matter and the evolution of biological](https://reader033.vdocuments.site/reader033/viewer/2022060410/5f1041d37e708231d4483737/html5/thumbnails/6.jpg)
OCH2
OHO
O
PO
O
O
N1
OCH2
OHO
PO
O
O
N2
OCH2
OHO
PO
O
O
N3
OCH2
OHO
PO
O
O
N4
N A U G Ck = , , ,
3' - end
5' - end
NaØ
NaØ
NaØ
NaØ
RNA
nd 3’-endGCGGAU AUUCGCUUA AGUUGGGA G CUGAAGA AGGUC UUCGAUC A ACCAGCUC GAGC CCAGA UCUGG CUGUG CACAG
3'-end
5’-end
70
60
50
4030
20
10
Definition of RNA structure
5'-e
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The three-dimensional structure of a short double helical stack of B-DNA
James D. Watson, 1928- , and Francis Crick, 1916- ,Nobel Prize 1962
1953 – 2003 fifty years double helix
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5'-End
5'-End 3'-End
3'-End
70
60
50
4030
20
10
GCGGAUUUAGCUCAGDDGGGAGAGCMCCAGACUGAAYAUCUGGAGMUCCUGUGTPCGAUCCACAGAAUUCGCACCASequence
Secondary structure
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G
G
G
G
C
C
C G
C
C
G
C
C
G
C
C
G
C
C
G
C
C
C
C
G
G
G
G
G
C
G
C
Plus Strand
Plus Strand
Minus Strand
Plus Strand
Plus Strand
Minus Strand
3'
3'
3'
3'
3'
5'
5'
5'
3'
3'
5'
5'
5'+
Complex Dissociation
Synthesis
Synthesis
Complementary replication as thesimplest copying mechanism of RNAComplementarity is determined byWatson-Crick base pairs:
GÍC and A=U
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Evolution of RNA molecules based on Qβ phage
D.R.Mills, R.L.Peterson, S.Spiegelman, An extracellular Darwinian experiment with a self-duplicating nucleic acid molecule. Proc.Natl.Acad.Sci.USA 58 (1967), 217-224
S.Spiegelman, An approach to the experimental analysis of precellular evolution. Quart.Rev.Biophys. 4 (1971), 213-253
C.K.Biebricher, Darwinian selection of self-replicating RNA molecules. Evolutionary Biology 16 (1983), 1-52
G.Bauer, H.Otten, J.S.McCaskill, Travelling waves of in vitro evolving RNA.Proc.Natl.Acad.Sci.USA 86 (1989), 7937-7941
C.K.Biebricher, W.C.Gardiner, Molecular evolution of RNA in vitro. Biophysical Chemistry 66 (1997), 179-192
G.Strunk, T.Ederhof, Machines for automated evolution experiments in vitro based on the serial transfer concept. Biophysical Chemistry 66 (1997), 193-202
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RNA sample
Stock solution: Q RNA-replicase, ATP, CTP, GTP and UTP, bufferb
Time0 1 2 3 4 5 6 69 70
The serial transfer technique applied to RNA evolution in vitro
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Decrease in mean fitnessdue to quasispecies formation
The increase in RNA production rate during a serial transfer experiment
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No new principle will declare itself from below a heap of facts.
Sir Peter Medawar, 1985
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dx / dt = x - x
x
i i i
j j
; Σ = 1 ; i,j
f
f
i
j
Φ
Φ
fi Φ = (
= Σ
x - i )
j jx =1,2,...,n
[I ] = x 0 ; i i Æ i =1,2,...,n ; Ii
I1
I2
I1
I2
I1
I2
I i
I n
I i
I nI n
+
+
+
+
+
+
(A) +
(A) +
(A) +
(A) +
(A) +
(A) +
fn
fi
f1
f2
I mI m I m++(A) +(A) +fm
fm fj= max { ; j=1,2,...,n}
xm(t) 1 for t Á Á¸
[A] = a = constant
Reproduction of organisms or replication of molecules as the basis of selection
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Selection equation: [Ii] = xi Æ 0 , fi > 0
Mean fitness or dilution flux, φ (t), is a non-decreasing function of time,
Solutions are obtained by integrating factor transformation
( ) fxfxnifxdtdx n
j jjn
i iiii ====−= ∑∑ == 11
;1;,,2,1, φφ L
( ) { } 0var22
1≥=−== ∑
=
fffdtdxf
dtd i
n
ii
φ
( ) ( ) ( )( ) ( )
nitfx
tfxtxj
n
j j
iii ,,2,1;
exp0exp0
1
L=⋅
⋅=
∑ =
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s = ( f2-f1) / f1; f2 > f1 ; x1(0) = 1 - 1/N ; x2(0) = 1/N
200 400 600 800 1000
0.2
00
0.4
0.6
0.8
1
Time [Generations]
Frac
tion
of a
dvan
tage
ous v
aria
nt
s = 0.1
s = 0.01
s = 0.02
Selection of advantageous mutants in populations of N = 10 000 individuals
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Changes in RNA sequences originate from replication errors called mutations.
Mutations occur uncorrelated to their consequencesin the selection process and are, therefore, commonly characterized as random elements of evolution.
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G
G
G
C
C
C
G
C
C
G
C
C
C
G
C
C
C
G
C
G
G
G
G
C
Plus Strand
Plus Strand
Minus Strand
Plus Strand
3'
3'
3'
3'
5'
3'
5'
5'
5'
Point Mutation
Insertion
Deletion
GAA AA UCCCG
GAAUCC A CGA
GAA AAUCCCGUCCCG
GAAUCCA
The origins of changes in RNA sequences are replication errors called mutations.
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Theory of molecular evolution
M.Eigen, Self-organization of matter and the evolution of biological macromolecules. Naturwissenschaften 58 (1971), 465-526
C.J. Thompson, J.L. McBride, On Eigen's theory of the self-organization of matter and the evolution of biological macromolecules. Math. Biosci. 21 (1974), 127-142
B.L. Jones, R.H. Enns, S.S. Rangnekar, On the theory of selection of coupled macromolecular systems. Bull.Math.Biol. 38 (1976), 15-28
M.Eigen, P.Schuster, The hypercycle. A principle of natural self-organization. Part A: Emergence of the hypercycle. Naturwissenschaften 58 (1977), 465-526
M.Eigen, P.Schuster, The hypercycle. A principle of natural self-organization. Part B: The abstract hypercycle. Naturwissenschaften 65 (1978), 7-41
M.Eigen, P.Schuster, The hypercycle. A principle of natural self-organization. Part C: The realistic hypercycle. Naturwissenschaften 65 (1978), 341-369
J. Swetina, P. Schuster, Self-replication with errors - A model for polynucleotide replication.Biophys.Chem. 16 (1982), 329-345
J.S. McCaskill, A localization threshold for macromolecular quasispecies from continuously distributed replication rates. J.Chem.Phys. 80 (1984), 5194-5202
M.Eigen, J.McCaskill, P.Schuster, The molecular quasispecies. Adv.Chem.Phys. 75 (1989), 149-263
C. Reidys, C.Forst, P.Schuster, Replication and mutation on neutral networks. Bull.Math.Biol. 63(2001), 57-94
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Ij
In
I2
Ii
I1 I j
I j
I j
I j
I j
I j +
+
+
+
+
(A) +
fj Qj1
fj Qj2
fj Qji
fj Qjj
fj Qjn
Q (1- ) ij-d(i,j) d(i,j) = lp p
p .......... Error rate per digit
d(i,j) .... Hamming distance between Ii and Ij
........... Chain length of the polynucleotidel
dx / dt = x - x
x
i j j i
j j
Σ
; Σ = 1 ;
f
f x
j
j j i
Φ
Φ = Σ
Qji
QijΣi = 1
[A] = a = constant
[Ii] = xi 0 ; Æ i =1,2,...,n ;
Chemical kinetics of replication and mutation as parallel reactions
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.... GC UC ....CA
.... GC UC ....GU
.... GC UC ....GA .... GC UC ....CU
d =1H
d =1H
d =2H
City-block distance in sequence space 2D Sketch of sequence space
Single point mutations as moves in sequence space
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CGTCGTTACAATTTA GTTATGTGCGAATTC CAAATT AAAA ACAAGAG.....
CGTCGTTACAATTTA GTTATGTGCGAATTC CAAATT AAAA ACAAGAG.....
G A G T
A C A C
Hamming distance d (I ,I ) = H 1 2 4
d (I ,I ) = 0H 1 1
d (I ,I ) = d (I ,I )H H1 2 2 1
d (I ,I ) d (I ,I ) + d (I ,I )H H H1 3 1 2 2 3¶
(i)
(ii)
(iii)
The Hamming distance between sequences induces a metric in sequence space
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Mutation-selection equation: [Ii] = xi Æ 0, fi > 0, Qij Æ 0
Solutions are obtained after integrating factor transformation by means of an eigenvalue problem
fxfxnixxQfdtdx n
j jjn
i iijn
j jiji ====−= ∑∑∑ === 111
;1;,,2,1, φφ L
( ) ( ) ( )( ) ( )
)0()0(;,,2,1;exp0
exp01
1
1
0
1
0 ∑∑ ∑
∑=
=
−
=
−
= ==⋅⋅
⋅⋅=
n
i ikikn
j kkn
k jk
kkn
k iki xhcni
tc
tctx L
l
l
λ
λ
{ } { } { }njihHLnjiLnjiQfW ijijiji ,,2,1,;;,,2,1,;;,,2,1,; 1 LLlL ======÷ −
{ }1,,1,0;1 −==Λ=⋅⋅− nkLWL k Lλ
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spaceSequence
Con
cent
ratio
n
Master sequence
Mutant cloud
The molecular quasispecies in sequence space
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Information on the environment is created in the population during the selection process through autocatalytic self-enhancement of advantageous variants.
The population is visualized as a distribution of RNA molecules. In evolution the population carries a temporary memory on its recent history in terms of previously selected variants that are still present.
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1. RNA structure, replication kinetics, and origin of information
2. Evolution in silico and optimization of RNA structures
3. Random walks and ‚ensemble learning‘
4. Sequence-structure maps, neutral networks, and intersections
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In evolution variation occurs on genotypes but selection operates on the phenotype.
Mappings from genotypes into phenotypes are highly complex objects. The only computationally accessible case is in the evolution of RNA molecules.
The mapping from RNA sequences into secondary structures and function,
sequence ñ structure ñ function,
is used as a model for the complex relations between genotypes and phenotypes. Fertile progeny measured in terms of fitness in population biology is determined quantitatively by replication rate constants of RNA molecules.
Population biology Molecular genetics Evolution of RNA molecules
Genotype Genome RNA sequence
Phenotype Organism RNA structure and function
Fitness Reproductive success Replication rate constant
The RNA model
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5'-End
5'-End
5'-End
3'-End
3'-End
3'-End
70
60
50
4030
20
10
GCGGAUUUAGCUCAGDDGGGAGAGCMCCAGACUGAAYAUCUGGAGMUCCUGUGTPCGAUCCACAGAAUUCGCACCASequence
Secondary structure
Symbolic notation
Í
A symbolic notation of RNA secondary structure that is equivalent to the conventional graphs
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How to compute RNA secondary structures
Efficient algorithms based on dynamic programming are available for computation of minimum free energy and many suboptimal secondary structures for given sequences.
M.Zuker and P.Stiegler. Nucleic Acids Res. 9:133-148 (1981)
M.Zuker, Science 244: 48-52 (1989)
Equilibrium partition function and base pairing probabilities in Boltzmann ensembles of suboptimal structures.J.S.McCaskill. Biopolymers 29:1105-1190 (1990)
The Vienna RNA Package provides in addition: inverse folding (computing sequences for given secondary structures), computation of melting profiles from partitionfunctions, all suboptimal structures within a given energy interval, barrier tress of suboptimal structures, kinetic folding of RNA sequences, RNA-hybridization and RNA/DNA-hybridization through cofolding of sequences, alignment, etc.. I.L.Hofacker, W. Fontana, P.F.Stadler, L.S.Bonhoeffer, M.Tacker, and P. Schuster. Mh.Chem.125:167-188 (1994)
S.Wuchty, W.Fontana, I.L.Hofacker, and P.Schuster. Biopolymers 49:145-165 (1999)
C.Flamm, W.Fontana, I.L.Hofacker, and P.Schuster. RNA 6:325-338 (1999)
Vienna RNA Package: http://www.tbi.univie.ac.at
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G
GG
G
GG
G GG
G
GG
G
GG
G
U
U
U
U
UU
U
U
U
UU
A
A
AA
AA
AA
A
A
A
A
UC
C
CC
C
C
C
C
C
CC
C
5’-end 3’-end
GGCGCGCCCGGCGCC
GUAUCGAAAUACGUAGCGUAUGGGGAUGCUGGACGGUCCCAUCGGUACUCCA
UGGUUACGCGUUGGGGUAACGAAGAUUCCGAGAGGAGUUUAGUGACUAGAGG
RNAStudio.lnk
Folding of RNA sequences into secondary structures of minimal free energy, DG0300
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f0 ft
f1
f2
f3
f4
f6
f5f7
Replication rate constant:
fk = g / [a + DdS(k)]
DdS(k) = dH(Sk,St)
Evaluation of RNA secondary structures yields replication rate constants
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Hamming distance d (S ,S ) = H 1 2 4
d (S ,S ) = 0H 1 1
d (S ,S ) = d (S ,S )H H1 2 2 1
d (S ,S ) d (S ,S ) + d (S ,S )H H H1 3 1 2 2 3¶
(i)
(ii)
(iii)
The Hamming distance between structures in parentheses notation forms a metric in structure space
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Element class 1: The RNA molecule
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Stock Solution Reaction Mixture
Replication rate constant:
fk = g / [a + DdS(k)]
DdS(k) = dH(Sk,St)
Selection constraint:
# RNA molecules is controlled by the flow
NNtN ±≈)(
The flowreactor as a device for studies of evolution in vitro and in silico
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5'-End
3'-End
70
60
50
4030
20
10
Randomly chosen initial structure
Phenylalanyl-tRNA as target structure
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spaceSequence
Con
cent
ratio
n
Master sequence
Mutant cloud
“Off-the-cloud” mutations
The molecular quasispeciesin sequence space
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S{ = y( )I{
f S{ {ƒ= ( )
S{
f{
I{M
utat
ion
Genotype-Phenotype Mapping
Evaluation of the
Phenotype
Q{jI1
I2
I3
I4 I5
In
Q
f1
f2
f3
f4 f5
fn
I1
I2
I3
I4
I5
I{
In+1
f1
f2
f3
f4
f5
f{
fn+1
Q
Evolutionary dynamics including molecular phenotypes
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In silico optimization in the flow reactor: Trajectory (biologists‘ view)
Time (arbitrary units)
Aver
age
dist
ance
from
initi
al s
truct
ure
50
-d
D
S
500 750 1000 12502500
50
40
30
20
10
0
Evolutionary trajectory
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In silico optimization in the flow reactor: Trajectory (physicists‘ view)
Time (arbitrary units)
Aver
age
stru
ctur
e di
stan
ce to
targ
et
d
DS
500 750 1000 12502500
50
40
30
20
10
0
Evolutionary trajectory
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44A
vera
ge s
truc
ture
dis
tanc
e to
targ
et
d S
D
Evolutionary trajectory
1250
10
0
44
42
40
38
36Relay steps N
umber of relay step
Time
Endconformation of optimization
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4443A
vera
ge s
truc
ture
dis
tanc
e to
targ
et
d S
D
Evolutionary trajectory
1250
10
0
44
42
40
38
36Relay steps N
umber of relay step
Time
Reconstruction of the last step 43 Á 44
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444342A
vera
ge s
truc
ture
dis
tanc
e to
targ
et
d S
D
Evolutionary trajectory
1250
10
0
44
42
40
38
36Relay steps N
umber of relay step
Time
Reconstruction of last-but-one step 42 Á 43 (Á 44)
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44434241A
vera
ge s
truc
ture
dis
tanc
e to
targ
et
d S
D
Evolutionary trajectory
1250
10
0
44
42
40
38
36Relay steps N
umber of relay step
Time
Reconstruction of step 41 Á 42 (Á 43 Á 44)
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4443424140A
vera
ge s
truc
ture
dis
tanc
e to
targ
et
d S
D
Evolutionary trajectory
1250
10
0
44
42
40
38
36Relay steps N
umber of relay step
Time
Reconstruction of step 40 Á 41 (Á 42 Á 43 Á 44)
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444342414039
Evolutionary process
Reconstruction
Ave
rage
str
uctu
re d
ista
nce
to ta
rget
d S
D
Evolutionary trajectory
1250
10
0
44
42
40
38
36Relay steps N
umber of relay step
Time
Reconstruction of the relay series
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Transition inducing point mutations Neutral point mutations
Change in RNA sequences during the final five relay steps 39 Á 44
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In silico optimization in the flow reactor: Trajectory and relay steps
Time (arbitrary units)
Aver
age
stru
ctur
e di
stan
ce to
targ
et
d
DS
500 750 1000 12502500
50
40
30
20
10
0
Evolutionary trajectory
Relay steps
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1008
1214
Time (arbitrary units)
Aver
age
stru
ctur
e di
stan
ce
to ta
rget
d S
D
5002500
20
10
Uninterrupted presence
Evolutionary trajectory
Num
ber of relay step
28 neutral point mutations during a long quasi-stationary epoch
Transition inducing point mutations Neutral point mutations
Neutral genotype evolution during phenotypic stasis
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In silico optimization in the flow reactor: Main transitions
Main transitionsRelay steps
Time (arbitrary units)
Aver
age
stru
ctur
e di
stan
ce to
targ
et
d
DS
500 750 1000 12502500
50
40
30
20
10
0
Evolutionary trajectory
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00 09 31 44
Three important steps in the formation of the tRNA clover leaf from a randomly chosen initial structure corresponding to three main transitions.
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AUGC GC
Movies of optimization trajectories over the AUGC and the GC alphabet
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Runtime of trajectories
Freq
uenc
y
0 1000 2000 3000 4000 50000
0.05
0.1
0.15
0.2
Statistics of the lengths of trajectories from initial structure to target (AUGC-sequences)
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Number of transitions
Freq
uenc
y
0 20 40 60 80 1000
0.05
0.1
0.15
0.2
0.25
0.3
All transitions
Main transitions
Statistics of the numbers of transitions from initial structure to target (AUGC-sequences)
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Alphabet Runtime Transitions Main transitions No. of runs
AUGC 385.6 22.5 12.6 1017 GUC 448.9 30.5 16.5 611 GC 2188.3 40.0 20.6 107
Statistics of trajectories and relay series (mean values of log-normal distributions)
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1. RNA structure, replication kinetics, and origin of information
2. Evolution in silico and optimization of RNA structures
3. Random walks and ‚ensemble learning‘
4. Sequence-structure maps, neutral networks, and intersections
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1008
1214
Time (arbitrary units)
Aver
age
stru
ctur
e di
stan
ce
to ta
rget
d S
D
5002500
20
10
Uninterrupted presence
Evolutionary trajectory
Num
ber of relay step
28 neutral point mutations during a long quasi-stationary epoch
Transition inducing point mutations Neutral point mutations
Neutral genotype evolution during phenotypic stasis
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Variation in genotype space during optimization of phenotypes
Mean Hamming distance within the population and drift velocity of the population centerin sequence space.
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Spread of population in sequence space during a quasistationary epoch: t = 150
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Spread of population in sequence space during a quasistationary epoch: t = 170
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Spread of population in sequence space during a quasistationary epoch: t = 200
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Spread of population in sequence space during a quasistationary epoch: t = 350
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Spread of population in sequence space during a quasistationary epoch: t = 500
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Spread of population in sequence space during a quasistationary epoch: t = 650
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Spread of population in sequence space during a quasistationary epoch: t = 820
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Spread of population in sequence space during a quasistationary epoch: t = 825
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Spread of population in sequence space during a quasistationary epoch: t = 830
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Spread of population in sequence space during a quasistationary epoch: t = 835
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Spread of population in sequence space during a quasistationary epoch: t = 840
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Spread of population in sequence space during a quasistationary epoch: t = 845
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Spread of population in sequence space during a quasistationary epoch: t = 850
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Spread of population in sequence space during a quasistationary epoch: t = 855
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Element class 2: The ant worker
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Ant colony Random foraging Food source
Foraging behavior of ant colonies
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Ant colony Food source detected Food source
Foraging behavior of ant colonies
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Ant colony Pheromone trail laid down Food source
Foraging behavior of ant colonies
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Ant colony Pheromone controlled trail Food source
Foraging behavior of ant colonies
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RNA model Foraging behavior of ant colonies
Element RNA molecule Individual worker ant
Mechanism relating elements Mutation in quasi-species Genetics of kinship
Search process Optimization of RNA structure Recruiting of food
Search space Sequence space Three-dimensional space
Random step Mutation Element of ant walk
Self-enhancing process Replication Secretion of pheromone
Interaction between elements Mean replication rate Mean pheromone concentration
Goal of the search Target structure Food source
Temporary memory RNA sequences in population Pheromone trail
‘Learning’ entity Population of molecules Ant colony
Learning at population or colony level by trial and error
Two examples: (i) RNA model and (ii) ant colony
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1. RNA structure, replication kinetics, and origin of information
2. Evolution in silico and optimization of RNA structures
3. Random walks and ‚ensemble learning‘
4. Sequence-structure maps, neutral networks, and intersections
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Minimum free energycriterion
Inverse folding of RNA secondary structures
1st2nd3rd trial4th5th
The inverse folding algorithm searches for sequences that form a given RNA secondary structure under the minimum free energy criterion.
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Sk I. = ( )ψfk f Sk = ( )
Sequence space Structure space Real numbers
Mapping from sequence space into structure space and into function
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Sk I. = ( )ψfk f Sk = ( )
Sequence space Structure space Real numbers
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Sk I. = ( )ψfk f Sk = ( )
Sequence space Structure space Real numbers
The pre-image of the structure Sk in sequence space is the neutral network Gk
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Neutral networks are sets of sequences forming the same structure. Gk is the pre-image of the structure Sk in sequence space:
Gk = y-1(Sk) π {yj | y(Ij) = Sk}
The set is converted into a graph by connecting all sequences of Hamming distance one.
Neutral networks of small RNA molecules can be computed by exhaustive folding of complete sequence spaces, i.e. all RNA sequences of a given chain length. This number, N=4n , becomes very large with increasing length, and is prohibitive for numerical computations.
Neutral networks can be modelled by random graphs in sequence space. In this approach, nodes are inserted randomly into sequence space until the size of the pre-image, i.e. the number of neutral sequences, matches the neutral network to be studied.
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λj = 27 = 0.444 ,/12 λk = ø l (k)j
| |Gk
λ κ cr = 1 - -1 ( 1)/ κ-
λ λk cr . . . .>
λ λk cr . . . .<
network is connectedGk
network is connectednotGk
Connectivity threshold:
Alphabet size : = 4k ñ kAUGC
G S Sk k k= ( ) | ( ) = y y-1 U { }I Ij j
k lcr
2 0.5
3 0.423
4 0.370
GC,AU
GUC,AUG
AUGC
Mean degree of neutrality and connectivity of neutral networks
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A connected neutral network
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Giant Component
A multi-component neutral network
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Reference for postulation and in silico verification of neutral networks
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GkNeutral Network
Structure S k
Gk Cà k
Compatible Set Ck
The compatible set Ck of a structure Sk consists of all sequences which form Sk as its minimum free energy structure (the neutral network Gk) or one of itssuboptimal structures.
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Structure S 0
Structure S 1
The intersection of two compatible sets is always non empty: C0 Ú C1 â Ù
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Reference for the definition of the intersection and the proof of the intersection theorem
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A ribozyme switch
E.A.Schultes, D.B.Bartel, Science 289 (2000), 448-452
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Two ribozymes of chain lengths n = 88 nucleotides: An artificial ligase (A) and a natural cleavage ribozyme of hepatitis-d-virus (B)
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The sequence at the intersection:
An RNA molecules which is 88 nucleotides long and can form both structures
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Two neutral walks through sequence space with conservation of structure and catalytic activity
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Concluding remarks
(i) The RNA model allows for detailed insights into evolutionary optimization and experimental tests of predictions. Evolution occurs in steps: short adaptive phases are interrupted by long quasi-stationary epochs of neutral evolution.
(ii) RNA molecules share features with much more complex elements when they are subsumed in populations. The elements of a population are related by a genetic mechanism.
(iii) Creation of information and learning by trial and error occur atthe level of populations although the individual elements are subjected to random processes.
(iv) In this sense the population is more than the sum of its elements. It carries a temporary memory of its past in the form of molecular species that had been selected in previous adaptive phases.
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Acknowledgement of support
Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
Projects No. 09942, 10578, 11065, 1309313887, and 14898
Jubiläumsfonds der Österreichischen Nationalbank
Project No. Nat-7813
European Commission: Project No. EU-980189
The Santa Fe Institute and the Universität Wien
The software for producing RNA movies was developed by Robert Giegerich and coworkers at the Universität Bielefeld
Universität Wien
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CoworkersUniversität Wien
Walter Fontana, Santa Fe Institute, NM
Christian Reidys, Christian Forst, Los Alamos National Laboratory, NM
Peter Stadler, Bärbel Stadler, Universität Leipzig, GE
Ivo L.Hofacker, Christoph Flamm, Universität Wien, AT
Andreas Wernitznig, Michael Kospach, Universität Wien, ATUlrike Langhammer, Ulrike Mückstein, Stefanie Widder
Jan Cupal, Kurt Grünberger, Andreas Svrček-Seiler, Stefan Wuchty
Ulrike Göbel, Institut für Molekulare Biotechnologie, Jena, GEWalter Grüner, Stefan Kopp, Jaqueline Weber
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Web-Page for further information:
http://www.tbi.univie.ac.at/~pks
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