Development of a spin-glass model of prebiotic evolution: environmental effects on ensembles of genetic polymers.

We develop in two ways an existing spin-glass model of prebiotic polymer evolution. First, by choosing the environment J in a prescribed manner, similar to neural network presciptions, we may create an environment which favors a linearly independent set of evolutionary niches (Ea). That is, we may control which polymer "species" will evolve in our system. Computer simulations confirm this result. We obtain a quantitative value for the sharpness of a niche. Second, we extend the model by allowing a surviving polymer to act upon--to "remold"--its environment; the nature of the environmental action is governed by the "molding" matrix M. When the mold M is the identity matrix, the feedback algorithm reduces to a Hebb learning algorithm form, and a surviving polymer acts to enhance its own survival prospects. Molds having a structure analogous to (temporal) associative memories in neural networks can generate autocatalytic species or can exhibit symbiotic interspecies relationships.