Information and Information Processing in Biological Systems Peter - PowerPoint PPT Presentation

Information and Information Processing in Biological Systems Peter Schuster, Eörs Szathmáry, and Avshalom Elitzur Institut für Theoretische Chemie, Universität Wien, Austria, Collegium Budapest – Institute for Advanced Study , Ungarn, and Bar-Ilan University, Israel Europäisches Forum Alpbach Alpbach, 18.– 25.08.2005

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/

Primitive Forms of Learning Peter Schuster, Institut für Theoretische Chemie, Universität Wien

Agent of class 1: The RNA molecule

In silico optimization in the flow reactor: Evolutionary Trajectory

28 neutral point mutations during a long quasi-stationary epoch Transition inducing point mutations Neutral point mutations leave the change the molecular structure molecular structure unchanged Neutral genotype evolution during phenotypic stasis

Evolutionary trajectory Spreading of the population on neutral networks Drift of the population center in sequence space

Spreading and evolution of a population on a neutral network: t = 150

Spreading and evolution of a population on a neutral network : t = 170

Spreading and evolution of a population on a neutral network : t = 200

Spreading and evolution of a population on a neutral network : t = 350

Spreading and evolution of a population on a neutral network : t = 500

Spreading and evolution of a population on a neutral network : t = 650

Spreading and evolution of a population on a neutral network : t = 820

Spreading and evolution of a population on a neutral network : t = 825

Spreading and evolution of a population on a neutral network : t = 830

Spreading and evolution of a population on a neutral network : t = 835

Spreading and evolution of a population on a neutral network : t = 840

Spreading and evolution of a population on a neutral network : t = 845

Spreading and evolution of a population on a neutral network : t = 850

Spreading and evolution of a population on a neutral network : t = 855

Agent of class 2: The ant worker

Ant colony Random foraging Food source Foraging behavior of ant colonies

Ant colony Food source detected Food source Foraging behavior of ant colonies

Ant colony Pheromone trail laid down Food source Foraging behavior of ant colonies

Ant colony Pheromone controlled trail Food source Foraging behavior of ant colonies

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 on creation of information on the environment. Two examples: (i) RNA model and (ii) ant colony

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.