Constant Epochs and Punctuation in Evolution Peter Schuster - - PowerPoint PPT Presentation

Constant Epochs and Punctuation in Evolution

Peter Schuster Institut für Theoretische Chemie und Molekulare Strukturbiologie der Universität Wien Europäisches Forum Alpbach 2003 Alpbach, Tirol, 14.– 17.08.2003

Web-Page for further information: http://www.tbi.univie.ac.at/~pks

1. Historical prologue 2. Gradualism and punctuated equilibria in evolution 3. Catastrophes 4. Evolution experiments in vitro and in silico

1. Historical prologue 2. Gradualism and punctuated equilibria in evolution 3. Catastrophes 4. Evolution experiments in vitro and in silico

James Hutton, 1726-1797 Sir Charles Lyell, 1797-1875 Charles Robert Dawin, 1809-1882

Uniformitarianism: ‘Unterstanding the present is the key to understanding the past’ Gradualism: ‘Natura non fecit saltus’, small forces working constantly for long time shape the world

Three proponents for continuous development in the eighteenth and nineteenth century

Georges Cuvier, 1769-1832 Jean Louis Rodolphe Agassiz, 1807-1873

Two proponents of catastrophes and abrupt change in the nineteenth century

Diversity of concepts of evolution in twentieth century and current biology

1. Historical prologue 2. Gradualism and punctuated equilibria in evolution 3. Catastrophes 4. Evolution experiments in vitro and in silico

Formation of a insect-like complex shape from a dot through a sequence of small changes

Richard Dawkins, The Blind Watchmaker, Longman Scientific & Technical, 1986

Gradual change versus punctuated equilibrium in butterfly colors

time

Charles Darwin, The Origin of Species, 6th edition. Everyman‘s Library, Vol.811, Dent London, pp.121-122.

time morphologies morphologies

Phyletic tree as pictured by the gradualists’ and the punctuated equilibrium approach

Gradualism versus punctuated equilibrium in terms of phenotypic densities

1. Historical prologue 2. Gradualism and punctuated equilibria in evolution 3. Catastrophes 4. Evolution experiments in vitro and in silico

Falling meterorites:

An example is the Chicxulub crater in Mexico dated 65 million years ago L.W.Alvarez, Mass Extinctions caused by large bolide

• impacts. Physics Today 40: 24-33, 1987

Lake Agassiz formed through melting of glaciers at the end of the last Ice Age

Pitman and Ryan’s suggested association with Noah’s Flood is presumably wrong!

Fall and rise of the water level of the black sea during and after the last glaciation

1. Historical prologue 2. Gradualism and punctuated equilibria in evolution 3. Catastrophes 4. Evolution experiments in vitro and in silico

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

RNA sample Stock solution: Q RNA-replicase, ATP, CTP, GTP and UTP, buffer

• Time

1 2 3 4 5 6 69 70 The serial transfer technique applied to RNA evolution in vitro

Decrease in mean fitness due to quasispecies formation

The increase in RNA production rate during a serial transfer experiment

Bacterial Evolution

• S. F. Elena, V. S. Cooper, R. E. Lenski. Punctuated evolution caused by selection of

rare beneficial mutants. Science 272 (1996), 1802-1804

• D. Papadopoulos, D. Schneider, J. Meier-Eiss, W. Arber, R. E. Lenski, M. Blot.

Genomic evolution during a 10,000-generation experiment with bacteria. Proc.Natl.Acad.Sci.USA 96 (1999), 3807-3812

Epochal evolution of bacteria in serial transfer experiments under constant conditions

• S. F. Elena, V. S. Cooper, R. E. Lenski. Punctuated evolution caused by selection of rare beneficial mutants.

Science 272 (1996), 1802-1804

2000 2000 4000 4000 6000 6000 8000 8000 10000 10000 Time (Generations) Time (Generations) 5 10 15 20 25 Distance to ancestor Distance within sample 2 4 6 8 10 12

Variation of genotypes in a bacterial serial transfer experiment

• D. Papadopoulos, D. Schneider, J. Meier-Eiss, W. Arber, R. E. Lenski, M. Blot. Genomic evolution during a

10,000-generation experiment with bacteria. Proc.Natl.Acad.Sci.USA 96 (1999), 3807-3812

No new principle will declare itself from below a heap of facts.

Sir Peter Medawar, 1985

GCGGAU AUUCGC UUA AGUUGGGA G CUGAAGA AGGUC UUCGAUC A ACCA GCUC GAGC CCAGA UCUGG CUGUG CACAG

Genotype Variation Phenotype Selection Development

Variation operates on genotypes and selection sorts phenotypes according to fitness

Point Mutation Insertion Deletion

GAA AA UCCCG GAAUCC A CGA GAA AA UCCCGUCCCG GAAUCCA GAA CCCGAA U GAA CCCGAA C

Common mutations in DNA and RNA replication

Optimization of RNA molecules in silico

W.Fontana, P.Schuster, A computer model of evolutionary optimization. Biophysical Chemistry 26 (1987), 123-147 W.Fontana, W.Schnabl, P.Schuster, Physical aspects of evolutionary optimization and

• adaptation. Phys.Rev.A 40 (1989), 3301-3321

M.A.Huynen, W.Fontana, P.F.Stadler, Smoothness within ruggedness. The role of neutrality in adaptation. Proc.Natl.Acad.Sci.USA 93 (1996), 397-401 W.Fontana, P.Schuster, Continuity in evolution. On the nature of transitions. Science 280 (1998), 1451-1455 W.Fontana, P.Schuster, Shaping space. The possible and the attainable in RNA genotype- phenotype mapping. J.Theor.Biol. 194 (1998), 491-515 B.M.R. Stadler, P.F. Stadler, G.P. Wagner, W. Fontana, The topology of the possible: Formal spaces underlying patterns of evolutionary change. J.Theor.Biol. 213 (2001), 241-274

Stock Solution Reaction Mixture

Mutation rate: p = 0.001 Population size: 1000 N 100 000 Fitness function: fk = / [+ dS

(k)]

• dS

(k) = ds(Ik,I

) The flowreactor as a device for studies on evolution in silico

5'-End 3'-End

70 60 50 40 30 20 10

Randomly chosen initial structure Phenylalanyl-tRNA as target structure

s p a c e Sequence Concentration

Master sequence Mutant cloud “Off-the-cloud” mutations

The molecular quasispecies in sequence space

In silico optimization in the flow reactor: Trajectory (biologists‘ view) Time (arbitrary units) A v e r a g e d i s t a n c e f r

• m

i n i t i a l s t r u c t u r e 5

• d
• S

500 750 1000 1250 250 50 40 30 20 10

Evolutionary trajectory

In silico optimization in the flow reactor: Trajectory (physicists‘ view) Time (arbitrary units) A v e r a g e s t r u c t u r e d i s t a n c e t

• t

a r g e t d

• S

500 750 1000 1250 250 50 40 30 20 10

Evolutionary trajectory

44

Average structure distance to target dS

• Evolutionary trajectory

1250 10

44 42 40 38 36 Relay steps Number of relay step Time

Endconformation of optimization

44 43

Average structure distance to target dS

• Evolutionary trajectory

1250 10

44 42 40 38 36 Relay steps Number of relay step Time

Reconstruction of the last step 43 44

44 43 42

Average structure distance to target dS

• Evolutionary trajectory

1250 10

44 42 40 38 36 Relay steps Number of relay step Time

Reconstruction of last-but-one step 42 43 ( 44)

44 43 42 41

Average structure distance to target dS

• Evolutionary trajectory

1250 10

44 42 40 38 36 Relay steps Number of relay step Time

Reconstruction of step 41 42 ( 43 44)

44 43 42 41 40

Average structure distance to target dS

• Evolutionary trajectory

1250 10

44 42 40 38 36 Relay steps Number of relay step Time

Reconstruction of step 40 41 ( 42 43 44)

44 43 42 41 40 39 Evolutionary process Reconstruction

Average structure distance to target dS

• Evolutionary trajectory

1250 10

44 42 40 38 36 Relay steps Number of relay step Time

Reconstruction of the relay series

Transition inducing point mutations Neutral point mutations

Change in RNA sequences during the final five relay steps 39 44

In silico optimization in the flow reactor: Trajectory and relay steps Time (arbitrary units) A v e r a g e s t r u c t u r e d i s t a n c e t

• t

a r g e t d

• S

500 750 1000 1250 250 50 40 30 20 10

Evolutionary trajectory

Relay steps

10 08 12 14 Time (arbitrary units) Average structure distance to target dS

• 500

250 20 10

Uninterrupted presence Evolutionary trajectory Number of relay step

Transition inducing point mutations Neutral point mutations

Neutral genotype evolution during phenotypic stasis

Average structure distance to target dS

• Evolutionary trajectory

1250 10

44 42 40 38 36 Relay steps Number of relay step Time

38 37 36 Main transition leading to clover leaf

Reconstruction of a main transitions 36 37 ( 38)

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.

In silico optimization in the flow reactor: Main transitions Main transitions Relay steps Time (arbitrary units) Average structure distance to target d S

500 750 1000 1250 250 50 40 30 20 10

Evolutionary trajectory

Statistics of evolutionary trajectories

Population size N Number of replications < n >

rep

Number of transitions < n >

tr

Number of main transitions < n >

dtr

The number of main transitions or evolutionary innovations is constant.

Variation in genotype space during optimization of phenotypes

„...Variations neither useful not injurious would not be affected by natural selection, and would be left either a fluctuating element, as perhaps we see in certain polymorphic species, or would ultimately become fixed,

• wing to the nature of the organism and the nature of

the conditions. ...“

Charles Darwin, Origin of species (1859)

Genotype Space F i t n e s s

Start of Walk End of Walk Random Drift Periods Adaptive Periods

Evolution in genotype space sketched as a non-descending walk in a fitness landscape

Conclusions

The world of biology is essentially discrete since individual mutations can be traced down to elementary steps of chemical reactions. The majority of mutations is neutral with respect to fitness and selection. Selection operates by elimination of deleterious mutations and relatively rare fixation of advantageous mutations. Mutations in genotypes appear with constant rates per nucleotide and time and can be interpreted as a molecular clock of evolution. Evolutionary optimization may appear quasi-gradual or stepwise depending on the probability distributions of changes in the phenotypes. Main transitions or innovations are very rare events and occur only because a very large number of equivalent paths exists which lead all to the target (corresponding to an all roads go to Rome situation).

Acknowledgement of financial support

Fonds zur Förderung der wissenschaftlichen Forschung (FWF) Projects No. 09942, 10578, 11065, 13093 13887, and 14898 Jubiläumsfonds der Österreichischen Nationalbank Project No. Nat-7813 European Commission Project No. EU-980189

Universität Wien

Coworkers

Universität 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, AT Ulrike Langhammer, Ulrike Mückstein, Stefanie Widder Jan Cupal, Kurt Grünberger, Andreas Svrček-Seiler, Stefan Wuchty Ulrike Göbel, Institut für Molekulare Biotechnologie, Jena, GE Walter Grüner, Stefan Kopp, Jaqueline Weber