  We develop a simple, chemostat-based model illustrating how a process
analogous to associative learning can occur in a biochemical network.
Associative learning is a form of learning whereby a system "learns" to
associate two stimuli with one another. In our model, two types of replicating
molecules, denoted A and B, are present in some initial concentration in the
chemostat. Molecules A and B are stimulated to replicate by some growth
factors, denoted GA and GB, respectively. It is also assumed that A and B can
covalently link, and that the conjugated molecule can be stimulated by either
the GA or GB growth factors (and can be degraded). We show that, if the
chemostat is stimulated by both growth factors for a certain time, followed by
a time gap during which the chemostat is not stimulated at all, and if the
chemostat is then stimulated again by only one of the growth factors, then
there will be a transient increase in the number of molecules activated by the
other growth factor. Therefore, the chemostat bears the imprint of earlier,
simultaneous stimulation with both growth factors, which is indicative of
associative learning. It is interesting to note that the dynamics of our model
is consistent with various aspects of Pavlov's original series of associative
learning experiments in dogs. We discuss how associative learning can
potentially be performed in vitro within RNA, DNA, or peptide networks. We also
highlight how such a mechanism could potentially be involved in genomic
evolution, and suggest bioinformatics studies that could be used to find
evidence for associative learning processes at work inside living cells.
