complexity in evolutionary processes - tbipks/presentation/wien-physik10.pdf · complexity in...
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Complexity in Evolutionary Processes
Peter Schuster
Institut für Theoretische Chemie, Universität Wien, Austriaand
The Santa Fe Institute, Santa Fe, New Mexico, USA
7th Vienna Central European Seminar onParticle Physics and Quantum Field Theory
Vienna, 26.– 28.11.2010
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Web-Page for further information:
http://www.tbi.univie.ac.at/~pks
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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1,0; 1011 ==+= −+ FFFFF nnn
Leonardo da Pisa„Fibonacci“
~1180 – ~1240
Thomas Robert Malthus1766 – 1834
1, 2 , 4 , 8 ,16 , 32 , 64, 128 , ...
geometric progression
exponential growthn
nf ⎟⎟⎠
⎞⎜⎜⎝
⎛ +≈
251
51
The history of exponential growth
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Three necessary conditions for Darwinian evolution are:
1. Multiplication,
2. Variation, and
3. Selection.
Darwin discovered the principle of natural selection from empirical observations in nature.
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Pierre-François Verhulst, 1804-1849
( ) trexCxCxtx
Cxxr
dtdx
−−+=⎟
⎠⎞
⎜⎝⎛ −=
)0()0()0()(,1
The logistic equation, 1828
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1.01
12 =−
=f
ffs
Two variants with a mean progeny of ten or eleven descendants
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Num
bers
N1(
n)an
d N
2(n)
N1(0) = 9999 , N2(0) = 1 ; s = 0.1 , 0.02 , 0.01
Selection of advantageous mutants in populations of N = 10 000 individuals
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( )ΦrxxCΦxr
xrCxxrx
Cxxrx
−==≡
−=⇒⎟⎠⎞
⎜⎝⎛ −=
dtd:1,)t(
dtd1
dtd
Darwin
[ ]
( ) ( ) ∑∑
∑
==
=
=−=−=
===
ni iijj
ni iijj
j
ni iiin
xfΦΦfxxffxx
Cxx
11
121
;dt
d
1;X:X,,X,X K
( ) { } 0var22dt
d 22 ≥=><−><= fffΦ
Generalization of the logistic equation to n variables yields selection
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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Taq = thermus aquaticus
Accuracy of replication: Q = q1 · q2 · q3 · … · qn
The logics of DNA replication
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Point mutation
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Manfred Eigen1927 -
∑∑
∑
==
=
=
=−=
n
i in
i ii
jin
i jij
xxfΦ
njΦxxWx
11
1,,2,1;
dtd
K
Mutation and (correct) replication as parallel chemical reactionsM. Eigen. 1971. Naturwissenschaften 58:465,
M. Eigen & P. Schuster.1977. Naturwissenschaften 64:541, 65:7 und 65:341
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∑∑
∑∑
==
==
=
=−=−=
n
i in
i ii
jiin
i jijin
i jij
xxfΦ
njΦxxfQΦxxWx
11
11,,2,1;
dtd
K
Factorization of the value matrix W separates mutation and fitness effects.
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integrating factor transformation
eigenvalue problem
Solution of the mutation-selection equation
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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Error rate p = 1-q0.00 0.05 0.10
Quasispecies Uniform distribution
Stationary population or quasispecies as a function of the mutation or errorrate p
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The no-mutational backflow or zeroth order approximation
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quasispecies
The error threshold in replication and mutation
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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single peak landscape
„Rugged“ fitness landscapes
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Error threshold on the single peak landscape
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linear andmultiplicativelandscape
Smooth fitness landscapes
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The linear fitness landscape shows no error threshold
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Make things as simple as possible, but not simpler !
Albert Einstein
Albert Einstein‘s razor, precise refence is unknown.
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Sewall Wright. 1931. Evolution in Mendelian populations. Genetics 16:97-159.
-- --. 1932. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In: D.F.Jones, ed. Proceedings of the Sixth International Congress on Genetics, Vol.I. Brooklyn Botanical Garden. Ithaca, NY, pp. 356-366.
-- --. 1988. Surfaces of selective value revisited. The American Naturalist 131:115-131.
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Build-up principle of binary sequence spaces
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single peak landscape
Rugged fitness landscapesover individual binary sequences with n = 10
„realistic“ landscape
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Error threshold: Individual sequences
n = 10, = 2, s = 491 and d = 0, 1.0, 1.875
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d = 0.100
Case I: Strong Quasispecies
n = 10, f0 = 1.1, fn = 1.0, s = 919
d = 0.200
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d = 0.100
Case III: Multiple transitions
n = 10, f0 = 1.1, fn = 1.0, s = 637
d = 0.195
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d = 0.199
Case III: Multiple transitions
n = 10, f0 = 1.1, fn = 1.0, s = 637
d = 0.200
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Paul E. Phillipson, Peter Schuster. (2009) Modeling by nonlinear differential equations. Dissipative and conservative processes. World Scientific, Singapore, pp.9-60.
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W = G F
1 , 1 largest eigenvalue and eigenvector
diagonalization of matrix W„ complicated but not complex “
fitness landscapemutation matrix
„ complex “( complex )
sequence structure
„ complex “
mutation selection
Complexity in molecular evolution
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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N = 4n
NS < 3n
Criterion: Minimum free energy (mfe)
Rules: _ ( _ ) _ {AU,CG,GC,GU,UA,UG}
A symbolic notation of RNA secondary structure that is equivalent to the conventional graphs
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The inverse folding algorithm searches for sequences that form a given RNA secondary structure under the minimum free energy criterion.
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What is neutrality ?
Selective neutrality = = several genotypes having the same fitness.
Structural neutrality == several genotypes forming molecules with the same structure.
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A mapping and its inversion
Gk = ( ) | ( ) = -1 US I Sk j j kI
( ) = I Sj k
Space of genotypes: = {I
S
I I I I I
S S S S S
1 2 3 4 N
1 2 3 4 M
, , , , ... , } ; Hamming metric
Space of phenotypes: , , , , ... , } ; metric (not required)
N M
= {
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many genotypes one phenotype
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AUCAAUCAG
GUCAAUCAC
GUCAAUCAUGUCAAUCAA
GUCAAUCCG
GUCAAUCG
G
GU
CA
AU
CU
G
GU
CA
AU
GA
G
GUC
AAUU
AG
GUCAAUAAGGUCAACCAG
GUCAAGCAG
GUCAAACAG
GUCACUCAG
GUCAGUCAG
GUCAUUCAGGUCCAUCAG GUCGAUCAG
GUCU
AUCA
G
GU
GA
AUC
AG
GU
UA
AU
CA
G
GU
AA
AU
CA
G
GCC
AAUC
AGGGCAAUCAG
GACAAUCAG
UUCAAUCAG
CUCAAUCAG
GUCAAUCAG
One-error neighborhood
The surrounding of GUCAAUCAG in sequence space
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One error neighborhood – Surrounding of an RNA molecule of chain length n=50 in sequence and shape space
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One error neighborhood – Surrounding of an RNA molecule of chain length n=50 in sequence and shape space
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One error neighborhood – Surrounding of an RNA molecule of chain length n=50 in sequence and shape space
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One error neighborhood – Surrounding of an RNA molecule of chain length n=50 in sequence and shape space
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GGCUAUCGUAUGUUUACCCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAUCGUACGUUUACCCAAAAGUCUACGUUGGACCCAGGCAUUAGACGGGCUAUCGUACGUUUACUCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAUCGUACGCUUACCCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCCAUCGUACGUUUACCCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAUCGUACGUUUACCCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAUCGUACGUGUACCCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAACGUACGUUUACCCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAUCGUACGUUUACCCAAAAGUCUACGUUGGACCCUGGCAUUGGACGGGCUAUCGUACGUUUACCCAAAAGUCUACGUUGGACCCAGGCACUGGACGGGCUAUCGUACGUUUACCCAAAAGUCUACGUUGGUCCCAGGCAUUGGACGGGCUAGCGUACGUUUACCCAAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAUCGUACGUUUACCCGAAAGUCUACGUUGGACCCAGGCAUUGGACGGGCUAUCGUACGUUUACCCAAAAGCCUACGUUGGACCCAGGCAUUGGACG G
GC
UAU
CG
UAC
GU
UUA
CCC A
AAAG U
CUACG UUGGACC C AG
GCAU
UGGACG
One error neighborhood – Surrounding of an RNA molecule of chain length n=50 in sequence and shape space
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Number Mean Value Variance Std.Dev.Total Hamming Distance: 150000 11.647973 23.140715 4.810480Nonzero Hamming Distance: 99875 16.949991 30.757651 5.545958Degree of Neutrality: 50125 0.334167 0.006961 0.083434Number of Structures: 1000 52.31 85.30 9.24
1 (((((.((((..(((......)))..)))).))).))............. 50125 0.3341672 ..(((.((((..(((......)))..)))).)))................ 2856 0.0190403 ((((((((((..(((......)))..)))))))).))............. 2799 0.0186604 (((((.((((..((((....))))..)))).))).))............. 2417 0.0161135 (((((.((((.((((......)))).)))).))).))............. 2265 0.0151006 (((((.(((((.(((......))).))))).))).))............. 2233 0.0148877 (((((..(((..(((......)))..)))..))).))............. 1442 0.0096138 (((((.((((..((........))..)))).))).))............. 1081 0.0072079 ((((..((((..(((......)))..))))..)).))............. 1025 0.006833
10 (((((.((((..(((......)))..)))).))))).............. 1003 0.00668711 .((((.((((..(((......)))..)))).))))............... 963 0.00642012 (((((.(((...(((......)))...))).))).))............. 860 0.00573313 (((((.((((..(((......)))..)))).)).)))............. 800 0.00533314 (((((.((((...((......))...)))).))).))............. 548 0.00365315 (((((.((((................)))).))).))............. 362 0.00241316 ((.((.((((..(((......)))..)))).))..))............. 337 0.00224717 (.(((.((((..(((......)))..)))).))).).............. 241 0.00160718 (((((.(((((((((......))))))))).))).))............. 231 0.00154019 ((((..((((..(((......)))..))))...))))............. 225 0.00150020 ((....((((..(((......)))..)))).....))............. 202 0.001347 G
GC
UAU
CG
UAC
GU
UUA
CCC A
AAAG U
CUACG UUGGACC C AG
GCAU
UGGACG
Shadow – Surrounding of an RNA structure in shape space: AUGC alphabet, chain length n=50
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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Stochastic phenomena in evolutionary processes
ODEs (in population genetics) describe expectation valuesin infinite populations.
1. Finite population size effects
2. Low numbers of individual species
3. Selective neutrality
Every mutant starts from a single copy.
Populations drift randomly in the space of neutral variants.
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probabilistic notion of particle numbers Xj
master equation
flow reactor
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Evolution of RNA molecules as a Markow process
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Evolution of RNA molecules as a Markow process
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Evolution of RNA molecules as a Markow process
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Evolution of RNA molecules as a Markow process
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RNA replication and mutation as a multitype branching process
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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Population size Ne = 10000 , s = 0
Stochastic population genetics of neutral, asexually reproducing species
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Motoo Kimura‘s population genetics of neutral evolution.
Evolutionary rate at the molecular level. Nature 217: 624-626, 1955.
The Neutral Theory of Molecular Evolution. Cambridge University Press. Cambridge, UK, 1983.
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The average time of replacement of a dominant genotype in a population is the reciprocal mutation rate, 1/ , and therefore independent of
population size.
Fixation of mutants in neutral evolution (Motoo Kimura, 1955)
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Is the Kimura scenario correct for frequent mutations?
Fixation of mutants in neutral evolution (Motoo Kimura, 1955)
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5.0)()(lim 210 ==→ pxpxp
dH = 1
apx
apx
p
p
−=
=
→
→
1)(lim
)(lim
20
10
dH = 2
dH ≥ 3
1)(lim,0)(lim
or0)(lim,1)(lim
2010
2010
==
==
→→
→→
pxpx
pxpx
pp
pp
Random fixation in thesense of Motoo Kimura
Pairs of neutral sequences in replication networks
P. Schuster, J. Swetina. 1988. Bull. Math. Biol. 50:635-650
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A fitness landscape including neutrality
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Neutral network: Individual sequences
n = 10, = 1.1, d = 1.0
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Consensus sequence of a quasispecies of two strongly coupled sequences of Hamming distance dH(Xi,,Xj) = 1.
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Neutral network: Individual sequences
n = 10, = 1.1, d = 1.0
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Consensus sequence of a quasispecies of two strongly coupled sequences of Hamming distance dH(Xi,,Xj) = 2.
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N = 7
Neutral networks with increasing : = 0.10, s = 229
Adjacency matrix
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1. Exponential growth and selection
2. Evolution as replication and mutation
3. A phase transition in evolution
4. Fitness landscapes as source of complexity
5. Molecular landscapes from biopolymers
6. The role of stochasticity
7. Neutrality and selection
8. Computer simulation of evolution
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Computer simulation using Gillespie‘s algorithm:
Replication rate constant:
fk = / [ + dS(k)]
dS(k) = dH(Sk,S )
Selection constraint:
Population size, N = # RNA molecules, is controlled by
the flow
Mutation rate:
p = 0.001 / site replication
NNtN ±≈)(
The flowreactor as a device for studies of evolution in vitro and in silico
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Evolution in silico
W. Fontana, P. Schuster, Science 280 (1998), 1451-1455
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Phenylalanyl-tRNA as target structure
Structure of randomly chosen initial sequence
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In silico optimization in the flow reactor: Evolutionary Trajectory
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28 neutral point mutations during a long quasi-stationary epoch
Transition inducing point mutations change the molecular structure
Neutral point mutations leave the molecular structure unchanged
Neutral genotype evolution during phenotypic stasis
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Evolutionary trajectory
Spreading of the population on neutral networks
Drift of the population center in sequence space
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Coworkers
Peter Stadler, Bärbel M. Stadler, Universität Leipzig, GE
Walter Fontana, Harvard Medical School, MA
Ivo L.Hofacker, Christoph Flamm, Universität Wien, AT
Martin Nowak, Harvard University, MA
Christian Reidys, Nankai University, Tien Tsin, China
Christian Forst, Los Alamos National Laboratory, NM
Kurt Grünberger, Michael Kospach , Andreas Wernitznig, Stefanie Widder,Stefan Wuchty, Jan Cupal, Stefan Bernhart, Lukas Endler, Ulrike Langhammer,
Rainer Machne, Ulrike Mückstein, Erich Bornberg-Bauer, Universität Wien, AT
Thomas Wiehe, Ulrike Göbel, Walter Grüner, Stefan Kopp, Jaqueline Weber, Institut für Molekulare Biotechnologie, Jena, GE
Universität Wien
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Acknowledgement of support
Fonds zur Förderung der wissenschaftlichen Forschung (FWF)Projects No. 09942, 10578, 11065, 13093
13887, and 14898
Wiener Wissenschafts-, Forschungs- und Technologiefonds (WWTF) Project No. Mat05
Jubiläumsfonds der Österreichischen NationalbankProject No. Nat-7813
European Commission: Contracts No. 98-0189, 12835 (NEST)
Austrian Genome Research Program – GEN-AU: BioinformaticsNetwork (BIN)
Österreichische Akademie der Wissenschaften
Siemens AG, Austria
Universität Wien and the Santa Fe Institute
Universität Wien
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Thank you for your attention!
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Web-Page for further information:
http://www.tbi.univie.ac.at/~pks
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