  The evolution of the genome has led to very sophisticated and complex
regulation. Because of the abundance of non-coding RNA (ncRNA) in the cell,
different species will promiscuously associate with each other, suggesting
collective dynamics similar to artificial neural networks. Here we present a
simple mechanism allowing ncRNA to perform computations equivalent to neural
network algorithms such as Boltzmann machines and the Hopfield model. The
quantities analogous to the neural couplings are the equilibrium constants
between different RNA species. The relatively rapid equilibration of RNA
binding and unbinding is regulated by a slower process that degrades and
creates new RNA. The model requires that the creation rate for each species be
an increasing function of the ratio of total to unbound RNA. Similar mechanisms
have already been found to exist experimentally for ncRNA regulation. With the
overall concentration of RNA regulated, equilibrium constants can be chosen to
store many different patterns, or many different input-output relations. The
network is also quite insensitive to random mutations in equilibrium constants.
Therefore one expects that this kind of mechanism will have a much higher
mutation rate than ones typically regarded as being under evolutionary
constraint.
