Associative memory in gene regulation networks


Watson, Richard, Buckley, C. L., Mills, Rob and Davies, Adam (2010) Associative memory in gene regulation networks. In, Fellerman, Harold, Dörr, Mark, Hanczyc, Martin M., Ladegaard Laursen, Lone, Maurer, Sarah, Merkle, Daniel, Monnard, Pierre-Alain, Stoy, Kasper and Rasmussen, Steen (eds.) Artificial Life XII: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems. , MIT Press, 659-666. (Proceedings of the Twelth International Conference on the Simulation and Synthesis of Living Systems).

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Description/Abstract

The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organism’s gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily ‘Hebbian’ – ‘genes that fire together wire together’ – i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ‘recall’ capabilities normally reserved for cognitive systems.

Item Type: Book Section
ISBNs: 9780262290753
Related URLs:
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > EEE
ePrint ID: 339763
Date Deposited: 30 May 2012 08:46
Last Modified: 27 Mar 2014 20:22
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/339763

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