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 Gradualism : ‘Natura non fecit saltus’, small forces present is the key to understanding the past’ 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 Q β 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 Time 0 1 2 3 4 5 6 69 70 � Stock solution: Q RNA-replicase, ATP, CTP, GTP and UTP, buffer 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
12 25 Distance within sample Distance to ancestor 10 20 8 15 6 10 4 5 2 2000 4000 6000 8000 10000 2000 4000 6000 8000 10000 Time (Generations) Time (Generations) 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 UUA GCUC AGUUGGGA GAGC CCAGA G CUGAAGA UCUGG AGGUC CUGUG UUCGAUC CACAG A AUUCGC ACCA Genotype Variation Development Phenotype Selection Variation operates on genotypes and selection sorts phenotypes according to fitness
GAA CCCGAA U GAA CCCGAA C Point Mutation GAA UCCCGUCCCG AA GAA UCCCG AA Insertion GAAUCCA GAAUCC CGA A Deletion 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: f k = � / [ � + � d S (k) ] � d S (k) = d s (I k ,I � ) The flowreactor as a device for studies on evolution in silico
3'-End 5'-End 70 60 10 50 20 30 40 Randomly chosen Phenylalanyl-tRNA as initial structure target structure
Master sequence Mutant cloud “Off-the-cloud” Concentration mutations Sequence e c a p s The molecular quasispecies in sequence space
50 S d � - 0 5 40 e r u t c u r Evolutionary trajectory t s 30 l a i t i n i m o r f 20 e c n a t s i d e g 10 a r e v A 0 0 250 500 750 1000 1250 Time (arbitrary units) In silico optimization in the flow reactor: Trajectory ( biologists‘ view )
50 S d � 40 t e g r a t o t e 30 c n a t s i d e r u 20 t c u r t s e g a 10 r e v A Evolutionary trajectory 0 0 250 500 750 1000 1250 Time (arbitrary units) In silico optimization in the flow reactor: Trajectory ( physicists‘ view )
Average structure distance to target dS 36 � Relay steps Number of relay step 10 38 40 42 44 Evolutionary trajectory 0 1250 Time 44 Endconformation of optimization
Average structure distance to target dS 36 � Relay steps Number of relay step 10 38 40 42 44 Evolutionary trajectory 0 1250 Time 43 44 Reconstruction of the last step 43 � 44
Average structure distance to target dS 36 � Relay steps Number of relay step 10 38 40 42 44 Evolutionary trajectory 0 1250 Time 42 43 44 Reconstruction of last-but-one step 42 � 43 ( � 44)
Average structure distance to target dS 36 � Relay steps Number of relay step 10 38 40 42 44 Evolutionary trajectory 0 1250 Time 41 42 43 44 Reconstruction of step 41 � 42 ( � 43 � 44)
Average structure distance to target dS 36 � Relay steps Number of relay step 10 38 40 42 44 Evolutionary trajectory 0 1250 Time 40 41 42 43 44 Reconstruction of step 40 � 41 ( � 42 � 43 � 44)
Average structure distance to target dS 36 � Relay steps Number of relay step 10 38 40 42 44 Evolutionary trajectory 0 1250 Time Evolutionary process 39 40 41 42 43 44 Reconstruction Reconstruction of the relay series
Transition inducing point mutations Neutral point mutations Change in RNA sequences during the final five relay steps 39 � 44
50 Relay steps S d � 40 t e g r a t o t e 30 c n a t s i d e r u 20 t c u r t s e g a 10 r e v A Evolutionary trajectory 0 0 250 500 750 1000 1250 Time (arbitrary units) In silico optimization in the flow reactor: Trajectory and relay steps
Average structure distance Uninterrupted presence Number of relay step 08 to target dS 10 � 12 20 14 Evolutionary trajectory 10 0 250 500 Time (arbitrary units) Transition inducing point mutations Neutral point mutations Neutral genotype evolution during phenotypic stasis
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