Associative learning in biochemical networks

Nikhil Gandhi, Gonen Ashkenasy, Emmanuel Tannenbaum

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, 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. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as 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 as 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 certain aspects of Pavlov's original series of conditioning experiments in dogs. We discuss how associative learning can potentially be performed in vitro within RNA, DNA, or peptide networks. We also describe how such a mechanism could be involved in genomic evolution, and suggest relevant bioinformatics studies that could potentially resolve these issues.

Original languageEnglish
Pages (from-to)58-66
Number of pages9
JournalJournal of Theoretical Biology
Volume249
Issue number1
DOIs
StatePublished - 7 Nov 2007

Keywords

  • Associative learning
  • Associative memory
  • Biochemical networks
  • RNA world

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics

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