Modeling Molecular Evolution - The Origin of Information and Learning in Populations

Tuesday, November 4, 2003 - 3:00pm - 3:35pm
Keller 3-180
Peter Schuster (Universität Wien)
Optimization through variation and selection and other evolutionary phenomena can be studied in cell-free systems by means of populations of nucleic acid molecules, in particular ribonucleic acid molecules (RNA). Computer modeling of RNA evolution in vitro provides even deeper insights than experiments, because it allows to trace the processes in populations with full genealogical information on all molecules. Molecules, representing partially autonomous agents, multiply and produce variants through imperfect copying. Selection operates on the level of the population, chooses between variants of different reproductive success, and provides a basis for the origin of biological information as well as primitive learning. A theoretical frame for modeling molecular evolution will be presented in the lecture and several illustrative examples of simulations of evolutionary optimization will be discussed.

Reference: James P. Crutchfield & Peter Schuster, Eds. Evolutionary Dynamics - Exploring the Interplay of Selection, Accident, Neutrality, and Function. Oxford University Press, New York 2003.