Mapping of statistical physics to information theory with application to biological systems

The problem of achieving a mapping of formalisms in statistical physics and theoretical biology to information theory is discussed using an example fo r canonical ensembles. We extend the meaning of the Handscomb Monte-Carlo m ethod to a general recipe for the transformation from a "configuration" spa ce to a "sentence" space. The ensemble of "sentences" and its corresponding source uncertainty function are introduced. A possible mapping procedure b ased on a generalization of the Handscomb representation is described. For a biological illustration, we present a way to introduce a pathway represen tation to describe metabolic processes in living systems. (C) 2000 Academic Press.