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Associative memory in gene regulation networks

Associative memory in gene regulation networks
Associative memory in gene regulation networks
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.
978-0-262-29075-3
659-666
MIT Press
Watson, Richard
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Buckley, C. L.
403be04e-fca5-4f1c-b0c4-d84401f51d51
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Davies, Adam
01904f16-2cc8-4130-904e-736ea077d01a
Fellerman, Harold
Dörr, Mark
Hanczyc, Martin M.
Ladegaard Laursen, Lone
Maurer, Sarah
Merkle, Daniel
Monnard, Pierre-Alain
Stoy, Kasper
Rasmussen, Steen
Watson, Richard
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75
Buckley, C. L.
403be04e-fca5-4f1c-b0c4-d84401f51d51
Mills, Rob
3d53d4bc-e1de-4807-b89b-f5813f2172a7
Davies, Adam
01904f16-2cc8-4130-904e-736ea077d01a
Fellerman, Harold
Dörr, Mark
Hanczyc, Martin M.
Ladegaard Laursen, Lone
Maurer, Sarah
Merkle, Daniel
Monnard, Pierre-Alain
Stoy, Kasper
Rasmussen, Steen

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

Record type: Conference or Workshop Item (Paper)

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.

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Published date: 2010
Organisations: Agents, Interactions & Complexity, EEE

Identifiers

Local EPrints ID: 339763
URI: http://eprints.soton.ac.uk/id/eprint/339763
ISBN: 978-0-262-29075-3
PURE UUID: de6cad9f-f15b-4135-b0e8-7e42138bd12f
ORCID for Richard Watson: ORCID iD orcid.org/0000-0002-2521-8255

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Date deposited: 30 May 2012 08:46
Last modified: 15 Mar 2024 03:21

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Contributors

Author: Richard Watson ORCID iD
Author: C. L. Buckley
Author: Rob Mills
Author: Adam Davies
Editor: Harold Fellerman
Editor: Mark Dörr
Editor: Martin M. Hanczyc
Editor: Lone Ladegaard Laursen
Editor: Sarah Maurer
Editor: Daniel Merkle
Editor: Pierre-Alain Monnard
Editor: Kasper Stoy
Editor: Steen Rasmussen

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