  Life relies on the efficient performance of molecular codes, which relate
symbols and meanings via error-prone molecular recognition. We describe how
optimizing a code to withstand the impact of molecular recognition noise may be
approximated by the statistics of a two-dimensional network made of polymers.
The noisy code is defined by partitioning the space of symbols into regions
according to their meanings. The "polymers" are the boundaries between these
regions and their statistics defines the cost and the quality of the noisy
code. When the parameters that control the cost-quality balance are varied, the
polymer network undergoes a first-order transition, where the number of encoded
meanings rises discontinuously. Effects of population dynamics on the evolution
of molecular codes are discussed.
