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Information and Information Processing in Biological Systems Peter - - PowerPoint PPT Presentation
Information and Information Processing in Biological Systems Peter - - PowerPoint PPT Presentation
Information and Information Processing in Biological Systems Peter Schuster, Ers Szathmry, and Avshalom Elitzur Institut fr Theoretische Chemie, Universitt Wien, Austria, Collegium Budapest Institute for Advanced Study , Ungarn, and
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Web-Pages for further information: http://www.tbi.univie.ac.at/~pks http://www.colbud.hu/fellows/szathmary.shtml http://faculty.biu.ac.il/~elitzua/
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Primitive Forms of Learning
Peter Schuster, Institut für Theoretische Chemie, Universität Wien
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Agent of class 1: The RNA molecule
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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
- n neutral networks
Drift of the population center in sequence space
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Spreading and evolution of a population on a neutral network: t = 150
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Spreading and evolution of a population on a neutral network : t = 170
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Spreading and evolution of a population on a neutral network : t = 200
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Spreading and evolution of a population on a neutral network : t = 350
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Spreading and evolution of a population on a neutral network : t = 500
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Spreading and evolution of a population on a neutral network : t = 650
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Spreading and evolution of a population on a neutral network : t = 820
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Spreading and evolution of a population on a neutral network : t = 825
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Spreading and evolution of a population on a neutral network : t = 830
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Spreading and evolution of a population on a neutral network : t = 835
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Spreading and evolution of a population on a neutral network : t = 840
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Spreading and evolution of a population on a neutral network : t = 845
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Spreading and evolution of a population on a neutral network : t = 850
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Spreading and evolution of a population on a neutral network : t = 855
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Agent of 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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Evolution of RNA Foraging ants Element RNA nucleotide Individual worker ant Genotype RNA sequence Worker ant collective Phenotype RNA structure Foraging path Learning entity Population of molecules Ant colony Relation between elements Mutation Reorientation of path segment Search process Optimization of structure Optimization of path Search space Sequence space Three-dimensional space Random step Mutation Segment of ant walk Self-enhancing process Replication Secretion of pheromone Measure of activity Mean replication rate Mean pheromone concentration Goal of the search Target structure Richest food source Temporary memory Sequence distribution Pheromone trail
Learning at population or colony level by variation and selection of success is based
- n creation of information on the environment.
Two examples: (i) RNA model and (ii) ant colony
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Wolfgang Wieser. Die Erfindung der Individualität oder die zwei Gesichter der Evolution. Spektrum Akademischer Verlag, Heidelberg 1998. A.C.Wilson. The Molecular Basis of Evolution. Scientific American, Oct.1985, 164-173.
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